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promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/chat_flow | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/chat_flow/template/azure_openai.yaml.jinja2 | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/AzureOpenAIConnection.schema.json
name: {{ connection }}
type: azure_open_ai
api_key: "<user-input>"
api_base: "<user-input>"
api_type: "azure"
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/package_tool/README.md.jinja2 | The directory structure in the package tool is as follows:
```python
{{ package_name }}
│ setup.py # This file contains metadata about your project like the name, version.
│
│ MANIFEST.in # This file is used to determine which files to include in the distribution of the project.
│
└───{{ package_name }}{{" " * (24 - package_name|length)}}# This is the source directory. All of your project’s source code should be placed in this directory.
{{ tool_name }}.py{{ " " * (17 - tool_name|length)}}# The source code of tools. Using the @tool decorator to identify the function as a tool.
utils.py # Utility functions for the package. A method for listing all tools defined in the package is generated in this file.
__init__.py
```
Please refer to [tool doc](https://microsoft.github.io/promptflow/how-to-guides/develop-a-tool/index.html) for more details about how to develop a tool. | 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/package_tool/init.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/package_tool/utils.py.jinja2 | from pathlib import Path
import importlib.util
from promptflow import PFClient
package_name = "{{ package_name }}"
def list_package_tools(raise_error=False):
"""
List the meta of all tools in the package.
The key of meta dict is the module name of tools and value is the meta data of the tool.
"""
# This function is auto generated by pf CLI, please do not modify manually.
tools = {}
pf_client = PFClient()
tools = pf_client.tools._list_tools_in_package(package_name, raise_error=raise_error)
return tools
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/package_tool/setup.py.jinja2 | from setuptools import find_packages, setup
PACKAGE_NAME = "{{ package_name }}"
setup(
name=PACKAGE_NAME,
version="0.0.1",
description="This is my tools package",
packages=find_packages(),
entry_points={
"package_tools": ["{{ package_name }} = {{ package_name }}.utils:list_package_tools"],
},
install_requires=[
"promptflow",
]
)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/package_tool/tool.py.jinja2 | {% if icon %}
from pathlib import Path
{% endif %}
from promptflow import tool
from promptflow.connections import CustomConnection
@tool(
name="{{ tool_name }}",
description="This is {{ tool_name }} tool",
{% if icon %}
icon={{ icon }},
{% endif %}
{% for key, value in extra_info.items() %}
{{ key }}={{ value }},
{% endfor %}
)
def {{ tool_name }}(connection: CustomConnection, input_text: str) -> str:
# Replace with your tool code.
# Usually connection contains configs to connect to an API.
# Use CustomConnection is a dict. You can use it like: connection.api_key, connection.api_base
# Not all tools need a connection. You can remove it if you don't need it.
return "Hello " + input_text
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/entry_flow/flow.dag.yaml.jinja2 | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
{% for arg, typ in flow_inputs.items() %}
{{ arg }}:
type: {{ typ }}
{% endfor %}
outputs:
output:
type: string
reference: {% raw %}${{% endraw %}{{ main_node_name }}.output}
nodes:
{% for param_name, file in prompt_params.items() %}
- name: {{ param_name }}
type: prompt
source:
type: code
path: {{ file }}
inputs: # Please check the generated prompt inputs
{% for arg in prompt_inputs[param_name].keys() %}
{{ arg }}: ${inputs.{{ arg }}}
{% endfor %}
{% endfor %}
- name: {{ main_node_name }}
type: python
source:
type: code
path: {{ tool_file }}
inputs:
{# Below are node inputs link to flow inputs #}
{% for arg in func_params.keys() %}
{{ arg }}: ${inputs.{{ arg }}}
{% endfor %}
{# Below are node prompt template inputs from prompt nodes #}
{% for param_name, file in prompt_params.items() %}
{{ param_name }}: {% raw %}${{% endraw %}{{ param_name }}.output}
{% endfor %}
connection: custom_connection
{% if setup_sh or python_requirements_txt %}
environment:
{% if setup_sh %}
setup_sh: {{ setup_sh }}
{% endif %}
{% if python_requirements_txt %}
python_requirements_txt: {{ python_requirements_txt }}
{% endif %}
{% endif %}
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/entry_flow/requirements_txt | promptflow | 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/entry_flow/flow.tools.json.jinja2 | {
"package": {},
"code": {
{% for key, prompt_obj in prompt_params.items() %}
"{{ key }}": {
"type": "prompt",
"inputs": {
{% for input_name, value in prompt_obj.get("inputs", {}).items() %}
"{{ input_name }}": {
"type": [
{% for typ in value["type"] %}
"{{ typ.value }}"
{% endfor %}
]
}{{ "," if not loop.last else "" }}
{% endfor %}
},
"source": "{{ prompt_obj.source }}"
},
{% endfor %}
"{{ tool_file }}": {
"type": "python",
"inputs": {
{% for arg, typ in tool_meta_args.items() %}
"{{ arg }}": {
"type": [
"{{ typ }}"
]
},
{% endfor %}
"connection": {
"type": [
"CustomConnection"
]
}
},
"function": "{{ tool_function }}",
"source": "{{ tool_file }}"
}
}
}
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/entry_flow/tool.py.jinja2 | import os
from promptflow import tool
from promptflow.connections import CustomConnection
{{ function_import }}
@tool
def {{ tool_function }}(
{% for arg in tool_arg_list %}
{{ arg.name }},
{% endfor %}
connection: CustomConnection) -> str:
# set environment variables
for key, value in dict(connection).items():
os.environ[key] = value
# call the entry function
return {{ entry_function }}(
{% for arg in tool_arg_list %}
{{ arg.name }}={{ arg.name }},
{% endfor %}
)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/entry_flow/gitignore | .env
__pycache__/
.promptflow/*
!.promptflow/flow.tools.json
.runs/
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/evaluation_flow/data.jsonl | {"groundtruth": "App", "prediction": "App"}
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/evaluation_flow/line_process.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from promptflow import tool
@tool
def line_process(groundtruth: str, prediction: str):
"""
This tool processes the prediction of a single line and returns the processed result.
:param groundtruth: the groundtruth of a single line.
:param prediction: the prediction of a single line.
"""
# Add your line processing logic here
processed_result = "Correct" if groundtruth.lower() == prediction.lower() else "Incorrect"
return processed_result
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/evaluation_flow/aggregate.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from typing import List
from promptflow import log_metric, tool
@tool
def aggregate(processed_results: List[str]):
"""
This tool aggregates the processed result of all lines and calculate the accuracy. Then log metric for the accuracy.
:param processed_results: List of the output of line_process node.
"""
# Add your aggregation logic here
# Aggregate the results of all lines and calculate the accuracy
aggregated_result = round((processed_results.count("Correct") / len(processed_results)), 2)
# Log metric the aggregate result
log_metric(key="accuracy", value=aggregated_result)
return aggregated_result
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/evaluation_flow/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
groundtruth:
type: string
prediction:
type: string
outputs:
results:
type: string
reference: ${line_process.output}
nodes:
- name: line_process
type: python
source:
type: code
path: line_process.py
inputs:
groundtruth: ${inputs.groundtruth}
prediction: ${inputs.prediction}
- name: aggregate
type: python
source:
type: code
path: aggregate.py
inputs:
processed_results: ${line_process.output}
aggregation: true
environment:
python_requirements_txt: requirements.txt
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/evaluation_flow | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/evaluation_flow/.promptflow/flow.tools.json | {
"package": {},
"code": {
"line_process.py": {
"type": "python",
"inputs": {
"groundtruth": {
"type": [
"string"
]
},
"prediction": {
"type": [
"string"
]
}
},
"function": "line_process"
},
"aggregate.py": {
"type": "python",
"inputs": {
"processed_results": {
"type": [
"object"
]
}
},
"function": "aggregate"
}
}
}
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/standard_flow/hello.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from promptflow import tool
# The inputs section will change based on the arguments of the tool function, after you save the code
# Adding type to arguments and return value will help the system show the types properly
# Please update the function name/signature per need
@tool
def my_python_tool(input1: str) -> str:
return "Prompt: " + input1
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/standard_flow/data.jsonl | {"text": "Hello World!"}
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/standard_flow/hello.jinja2 | {# Please replace the template with your own prompt. #}
Write a simple {{text}} program that displays the greeting message when executed.
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/standard_flow/flow.dag.yaml | $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
text:
type: string
outputs:
output_prompt:
type: string
reference: ${echo_my_prompt.output}
nodes:
- name: hello_prompt
type: prompt
source:
type: code
path: hello.jinja2
inputs:
text: ${inputs.text}
- name: echo_my_prompt
type: python
source:
type: code
path: hello.py
inputs:
input1: ${hello_prompt.output}
environment:
python_requirements_txt: requirements.txt
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/standard_flow | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/data/standard_flow/.promptflow/flow.tools.json | {
"package": {},
"code": {
"hello.jinja2": {
"type": "prompt",
"inputs": {
"text": {
"type": [
"string"
]
}
}
},
"hello.py": {
"type": "python",
"inputs": {
"input1": {
"type": [
"string"
]
}
},
"function": "my_python_tool"
}
}
}
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf/_upgrade.py | import os
from promptflow._cli._params import (
add_param_yes,
base_params,
)
from promptflow._cli._utils import activate_action, get_cli_sdk_logger
from promptflow._utils.utils import prompt_y_n
from promptflow.exceptions import UserErrorException
logger = get_cli_sdk_logger()
UPGRADE_MSG = 'Not able to upgrade automatically'
def add_upgrade_parser(subparsers):
"""Add upgrade parser to the pf subparsers."""
epilog = """
Examples:
# Upgrade prompt flow without prompt and run non-interactively:
pf upgrade --yes
""" # noqa: E501
add_params = [
add_param_yes,
] + base_params
activate_action(
name="upgrade",
description="Upgrade prompt flow CLI.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="pf upgrade",
action_param_name="action",
)
def upgrade_version(args):
import platform
import sys
import subprocess
from promptflow._constants import _ENV_PF_INSTALLER, CLI_PACKAGE_NAME
from promptflow._version import VERSION as local_version
from packaging.version import parse
from promptflow._utils.version_hint_utils import get_latest_version_from_pypi
latest_version = get_latest_version_from_pypi(CLI_PACKAGE_NAME)
if not latest_version:
logger.warning("Failed to get the latest prompt flow version.")
return
elif parse(latest_version) <= parse(local_version):
logger.warning("You already have the latest prompt flow version: %s", local_version)
return
yes = args.yes
exit_code = 0
installer = os.getenv(_ENV_PF_INSTALLER) or ''
installer = installer.upper()
print(f"installer: {installer}")
latest_version_msg = 'Upgrading prompt flow CLI version to {}.'.format(latest_version) if yes \
else 'Latest version available is {}.'.format(latest_version)
logger.warning("Your current prompt flow CLI version is %s. %s", local_version, latest_version_msg)
if not yes:
logger.warning("Please check the release notes first")
if not sys.stdin.isatty():
logger.debug('No tty available.')
raise UserErrorException("No tty available. Please run command with --yes.")
confirmation = prompt_y_n("Do you want to continue?", default='y')
if not confirmation:
logger.debug("Upgrade stopped by user")
return
if installer == 'MSI':
_upgrade_on_windows(yes)
elif installer == 'PIP':
pip_args = [sys.executable, '-m', 'pip', 'install', '--upgrade',
'promptflow[azure,executable,pfs,azureml-serving]', '-vv',
'--disable-pip-version-check', '--no-cache-dir']
logger.debug("Update prompt flow with '%s'", " ".join(pip_args))
exit_code = subprocess.call(pip_args, shell=platform.system() == 'Windows')
elif installer == 'SCRIPT':
command = "curl https://promptflowartifact.blob.core.windows.net/linux-install-scripts/install | bash"
logger.warning(f"{UPGRADE_MSG}, you can try to run {command} in your terminal directly to upgrade package.")
return
else:
logger.warning(UPGRADE_MSG)
return
if exit_code:
err_msg = "CLI upgrade failed."
logger.warning(err_msg)
sys.exit(exit_code)
import importlib
import json
importlib.reload(subprocess)
importlib.reload(json)
version_result = subprocess.check_output(['pf', 'version'], shell=platform.system() == 'Windows')
version_json = json.loads(version_result)
new_version = version_json['promptflow']
if new_version == local_version:
err_msg = f"CLI upgrade to version {latest_version} failed or aborted."
logger.warning(err_msg)
sys.exit(1)
logger.warning("Upgrade finished.")
def _upgrade_on_windows(yes):
"""Download MSI to a temp folder and install it with msiexec.exe.
Directly installing from URL may be blocked by policy: https://github.com/Azure/azure-cli/issues/19171
This also gives the user a chance to manually install the MSI in case of msiexec.exe failure.
"""
import subprocess
import sys
import tempfile
msi_url = 'https://aka.ms/installpromptflowwindowsx64'
logger.warning("Updating prompt flow CLI with MSI from %s", msi_url)
# Save MSI to ~\AppData\Local\Temp\promptflow-msi, clean up the folder first
msi_dir = os.path.join(tempfile.gettempdir(), 'promptflow-msi')
try:
import shutil
shutil.rmtree(msi_dir)
except FileNotFoundError:
# The folder has already been deleted. No further retry is needed.
# errno: 2, winerror: 3, strerror: 'The system cannot find the path specified'
pass
except OSError as err:
logger.warning("Failed to delete '%s': %s. You may try to delete it manually.", msi_dir, err)
os.makedirs(msi_dir, exist_ok=True)
msi_path = _download_from_url(msi_url, msi_dir)
if yes:
subprocess.Popen(['msiexec.exe', '/i', msi_path, '/qn'])
else:
subprocess.call(['msiexec.exe', '/i', msi_path])
logger.warning("Installation started. Please complete the upgrade in the opened window.")
sys.exit(0)
def _download_from_url(url, target_dir):
import requests
r = requests.get(url, stream=True)
if r.status_code != 200:
raise UserErrorException("Request to {} failed with {}".format(url, r.status_code))
# r.url is the real path of the msi, like
# 'https://promptflowartifact.blob.core.windows.net/msi-installer/promptflow.msi'
file_name = r.url.rsplit('/')[-1]
msi_path = os.path.join(target_dir, file_name)
logger.warning("Downloading MSI to %s", msi_path)
with open(msi_path, 'wb') as f:
for chunk in r.iter_content(chunk_size=1024):
f.write(chunk)
return msi_path
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf/_experiment.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import argparse
import json
from promptflow._cli._params import (
add_param_all_results,
add_param_archived_only,
add_param_include_archived,
add_param_max_results,
base_params,
)
from promptflow._cli._utils import activate_action, exception_handler
from promptflow._sdk._constants import get_list_view_type
from promptflow._sdk._load_functions import load_common
from promptflow._sdk._pf_client import PFClient
from promptflow._sdk.entities._experiment import Experiment, ExperimentTemplate
from promptflow._utils.logger_utils import get_cli_sdk_logger
logger = get_cli_sdk_logger()
_client = None
def _get_pf_client():
global _client
if _client is None:
_client = PFClient()
return _client
def add_param_template(parser):
parser.add_argument("--template", type=str, required=True, help="The experiment template path.")
def add_param_name(parser):
parser.add_argument("--name", "-n", type=str, help="The experiment name.")
def add_experiment_create(subparsers):
epilog = """
Examples:
# Create an experiment from a template:
pf experiment create --template flow.exp.yaml
"""
add_params = [add_param_template, add_param_name] + base_params
create_parser = activate_action(
name="create",
description=None,
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Create an experiment.",
action_param_name="sub_action",
)
return create_parser
def add_experiment_list(subparsers):
epilog = """
Examples:
# List all experiments:
pf experiment list
"""
activate_action(
name="list",
description="List all experiments.",
epilog=epilog,
add_params=[
add_param_max_results,
add_param_all_results,
add_param_archived_only,
add_param_include_archived,
]
+ base_params,
subparsers=subparsers,
help_message="List all experiments.",
action_param_name="sub_action",
)
def add_experiment_show(subparsers):
epilog = """
Examples:
# Get and show an experiment:
pf experiment show -n my_experiment
"""
activate_action(
name="show",
description="Show an experiment for promptflow.",
epilog=epilog,
add_params=[add_param_name] + base_params,
subparsers=subparsers,
help_message="Show an experiment for promptflow.",
action_param_name="sub_action",
)
def add_experiment_start(subparsers):
epilog = """
Examples:
# Start an experiment:
pf experiment start -n my_experiment
"""
activate_action(
name="start",
description="Start an experiment.",
epilog=epilog,
add_params=[add_param_name] + base_params,
subparsers=subparsers,
help_message="Start an experiment.",
action_param_name="sub_action",
)
def add_experiment_parser(subparsers):
experiment_parser = subparsers.add_parser(
"experiment",
description="[Experimental] A CLI tool to manage experiment for prompt flow.",
help="[Experimental] pf experiment. This is an experimental feature, and may change at any time.",
)
subparsers = experiment_parser.add_subparsers()
add_experiment_create(subparsers)
add_experiment_list(subparsers)
add_experiment_show(subparsers)
add_experiment_start(subparsers)
experiment_parser.set_defaults(action="experiment")
def dispatch_experiment_commands(args: argparse.Namespace):
if args.sub_action == "create":
create_experiment(args)
elif args.sub_action == "list":
list_experiment(args)
elif args.sub_action == "show":
show_experiment(args)
elif args.sub_action == "start":
start_experiment(args)
elif args.sub_action == "show-status":
pass
elif args.sub_action == "update":
pass
elif args.sub_action == "delete":
pass
elif args.sub_action == "stop":
pass
elif args.sub_action == "test":
pass
elif args.sub_action == "clone":
pass
@exception_handler("Create experiment")
def create_experiment(args: argparse.Namespace):
template_path = args.template
logger.debug("Loading experiment template from %s", template_path)
template = load_common(ExperimentTemplate, source=template_path)
logger.debug("Creating experiment from template %s", template.name)
experiment = Experiment.from_template(template, name=args.name)
logger.debug("Creating experiment %s", experiment.name)
exp = _get_pf_client()._experiments.create_or_update(experiment)
print(json.dumps(exp._to_dict(), indent=4))
@exception_handler("List experiment")
def list_experiment(args: argparse.Namespace):
list_view_type = get_list_view_type(archived_only=args.archived_only, include_archived=args.include_archived)
results = _get_pf_client()._experiments.list(args.max_results, list_view_type=list_view_type)
print(json.dumps([result._to_dict() for result in results], indent=4))
@exception_handler("Show experiment")
def show_experiment(args: argparse.Namespace):
result = _get_pf_client()._experiments.get(args.name)
print(json.dumps(result._to_dict(), indent=4))
@exception_handler("Start experiment")
def start_experiment(args: argparse.Namespace):
result = _get_pf_client()._experiments.start(args.name)
print(json.dumps(result._to_dict(), indent=4))
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf/_init_entry_generators.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import inspect
import json
import shutil
from abc import ABC, abstractmethod
from ast import literal_eval
from enum import Enum
from pathlib import Path
from jinja2 import Environment, Template, meta
from promptflow._sdk._constants import DEFAULT_ENCODING
from promptflow._sdk.operations._flow_operations import FlowOperations
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow.contracts.flow import Flow as ExecutableFlow
from promptflow.exceptions import UserErrorException
logger = get_cli_sdk_logger()
TEMPLATE_PATH = Path(__file__).parent.parent / "data" / "entry_flow"
CHAT_FLOW_TEMPLATE_PATH = Path(__file__).parent.parent / "data" / "chat_flow" / "template"
TOOL_TEMPLATE_PATH = Path(__file__).parent.parent / "data" / "package_tool"
EXTRA_FILES_MAPPING = {"requirements.txt": "requirements_txt", ".gitignore": "gitignore"}
SERVE_TEMPLATE_PATH = Path(__file__).resolve().parent.parent.parent / "_sdk" / "data" / "executable"
class BaseGenerator(ABC):
@property
@abstractmethod
def tpl_file(self):
pass
@property
@abstractmethod
def entry_template_keys(self):
pass
def generate(self) -> str:
"""Generate content based on given template and actual value of template keys."""
with open(self.tpl_file, encoding=DEFAULT_ENCODING) as f:
entry_template = f.read()
entry_template = Template(entry_template, trim_blocks=True, lstrip_blocks=True)
return entry_template.render(**{key: getattr(self, key) for key in self.entry_template_keys})
def generate_to_file(self, target):
"""Generate content to a file based on given template and actual value of template keys."""
target = Path(target).resolve()
action = "Overwriting" if target.exists() else "Creating"
print(f"{action} {target.resolve()}...")
with open(target, "w", encoding=DEFAULT_ENCODING) as f:
f.write(self.generate())
class ToolPyGenerator(BaseGenerator):
def __init__(self, entry, function, function_obj):
self.function_import = f"from {Path(entry).stem} import {function}"
self.entry_function = function
self.tool_function = f"{function}_tool"
# TODO: support default for tool args
self.tool_arg_list = inspect.signature(function_obj).parameters.values()
@property
def tpl_file(self):
return TEMPLATE_PATH / "tool.py.jinja2"
@property
def entry_template_keys(self):
return ["function_import", "entry_function", "tool_function", "tool_arg_list"]
class ValueType(str, Enum):
INT = "int"
DOUBLE = "double"
BOOL = "bool"
STRING = "string"
LIST = "list"
OBJECT = "object"
@staticmethod
def from_type(t: type):
if t == int:
return ValueType.INT
if t == float:
return ValueType.DOUBLE
if t == bool:
return ValueType.BOOL
if t == str:
return ValueType.STRING
if t == list:
return ValueType.LIST
return ValueType.OBJECT
class ToolMetaGenerator(BaseGenerator):
def __init__(self, tool_py, function, function_obj, prompt_params):
self.tool_file = tool_py
self.tool_function = f"{function}_tool"
# TODO: support default for tool meta args
self.tool_meta_args = self.get_tool_meta_args(function_obj)
self._prompt_params = prompt_params
@property
def prompt_params(self):
from promptflow._core.tool_meta_generator import generate_prompt_meta_dict
prompt_objs = {}
for key, file_name in self._prompt_params.items():
file_path = Path(file_name)
if not file_path.exists():
logger.warning(
f'Cannot find the prompt template "{file_name}", creating an empty prompt file in the flow...'
)
with open(file_path, "w") as f:
f.write("{# please enter your prompt content in this file. #}")
with open(file_name, "r") as f:
content = f.read()
name = Path(file_name).stem
prompt_objs[key] = generate_prompt_meta_dict(name, content, prompt_only=True, source=file_name)
return prompt_objs
def get_tool_meta_args(self, function_obj):
func_params = inspect.signature(function_obj).parameters
# TODO: Support enum/union in the future
return {k: ValueType.from_type(v.annotation).value for k, v in func_params.items()}
@property
def tpl_file(self):
return TEMPLATE_PATH / "flow.tools.json.jinja2"
@property
def entry_template_keys(self):
return ["prompt_params", "tool_file", "tool_meta_args", "tool_function"]
class FlowDAGGenerator(BaseGenerator):
def __init__(self, tool_py, function, function_obj, prompt_params):
self.tool_file = tool_py
self.main_node_name = function
self.prompt_params = prompt_params
self.setup_sh = None
self.python_requirements_txt = None
self._prompt_inputs = None
self._func_params = None
self._function_obj = function_obj
# Abstract prompt param from tool meta args
self.flow_inputs = self.get_flow_inputs(prompt_params)
def get_flow_inputs(self, prompt_params):
"""Generate the flow inputs"""
flow_inputs = {
k: ValueType.from_type(v.annotation).value for k, v in self.func_params.items() if k not in prompt_params
}
for prompt_inputs in self.prompt_inputs.values():
flow_inputs.update(prompt_inputs)
return flow_inputs
@property
def tpl_file(self):
return TEMPLATE_PATH / "flow.dag.yaml.jinja2"
@property
def func_params(self):
"""Generate function inputs without prompt templates."""
if self._func_params is None:
self._func_params = {
k: v for k, v in inspect.signature(self._function_obj).parameters.items() if k not in self.prompt_params
}
return self._func_params
@property
def prompt_inputs(self):
"""Generate prompt inputs."""
if self._prompt_inputs is None:
self._prompt_inputs = {}
for prompt_name, file_name in self.prompt_params.items():
try:
with open(file_name, "r") as f:
env = Environment()
ast = env.parse(f.read())
variables = meta.find_undeclared_variables(ast)
self._prompt_inputs[prompt_name] = {item: "string" for item in variables or []}
except Exception as e:
logger.warning(f"Get the prompt input from {file_name} failed, {e}.")
return self._prompt_inputs
@property
def entry_template_keys(self):
return [
"flow_inputs",
"main_node_name",
"prompt_params",
"tool_file",
"setup_sh",
"python_requirements_txt",
"prompt_inputs",
"func_params",
]
def generate_to_file(self, target):
# Get requirements.txt and setup.sh from target folder.
requirements_file = "requirements.txt"
if (Path(target).parent / requirements_file).exists():
self.python_requirements_txt = requirements_file
setup_file = "setup.sh"
if (Path(target).parent / setup_file).exists():
self.setup_sh = setup_file
super().generate_to_file(target=target)
class FlowMetaYamlGenerator(BaseGenerator):
def __init__(self, flow_name):
self.flow_name = flow_name
@property
def tpl_file(self):
return TEMPLATE_PATH / "flow.meta.yaml.jinja2"
@property
def entry_template_keys(self):
return ["flow_name"]
class StreamlitFileReplicator:
def __init__(self, flow_name, flow_dag_path):
self.flow_name = flow_name
self.flow_dag_path = Path(flow_dag_path)
self.executable = ExecutableFlow.from_yaml(
flow_file=Path(self.flow_dag_path.name), working_dir=self.flow_dag_path.parent
)
self.is_chat_flow, self.chat_history_input_name, error_msg = FlowOperations._is_chat_flow(self.executable)
@property
def flow_inputs(self):
if self.is_chat_flow:
results = {}
for flow_input, value in self.executable.inputs.items():
if value.is_chat_input:
if value.type.value not in [ValueType.STRING.value, ValueType.LIST.value]:
raise UserErrorException(
f"Only support string or list type for chat input, but got {value.type.value}."
)
results.update({flow_input: (value.default, value.type.value)})
else:
results = {
flow_input: (value.default, value.type.value) for flow_input, value in self.executable.inputs.items()
}
return results
@property
def label(self):
return "Chat" if self.is_chat_flow else "Run"
@property
def py_file(self):
return SERVE_TEMPLATE_PATH / "main.py"
@property
def flow_path(self):
return self.flow_dag_path.as_posix()
@property
def chat_output_name(self):
try:
output_name = next(
filter(
lambda key: self.executable.outputs[key].is_chat_output,
self.executable.outputs.keys(),
)
)
except StopIteration:
output_name = None
return output_name
@property
def is_streaming(self):
return True if self.is_chat_flow else False
@property
def entry_template_keys(self):
return [
"flow_name",
"flow_path",
"is_chat_flow",
"chat_history_input_name",
"flow_inputs",
"label",
"chat_output_name",
"is_streaming",
]
def generate_to_file(self, target):
if Path(target).name == "main.py":
target = Path(target).resolve()
shutil.copy(self.py_file, target)
config_content = {key: getattr(self, key) for key in self.entry_template_keys}
with open(target.parent / "config.json", "w") as file:
json.dump(config_content, file, indent=4)
else:
shutil.copy(SERVE_TEMPLATE_PATH / Path(target).name, target)
class ChatFlowDAGGenerator(BaseGenerator):
def __init__(self, connection, deployment):
self.connection = connection
self.deployment = deployment
@property
def tpl_file(self):
return CHAT_FLOW_TEMPLATE_PATH / "flow.dag.yaml.jinja2"
@property
def entry_template_keys(self):
return ["connection", "deployment"]
class AzureOpenAIConnectionGenerator(BaseGenerator):
def __init__(self, connection):
self.connection = connection
@property
def tpl_file(self):
return CHAT_FLOW_TEMPLATE_PATH / "azure_openai.yaml.jinja2"
@property
def entry_template_keys(self):
return ["connection"]
class OpenAIConnectionGenerator(BaseGenerator):
def __init__(self, connection):
self.connection = connection
@property
def tpl_file(self):
return CHAT_FLOW_TEMPLATE_PATH / "openai.yaml.jinja2"
@property
def entry_template_keys(self):
return ["connection"]
def copy_extra_files(flow_path, extra_files, overwrite=False):
for file_name in extra_files:
extra_file_path = (
Path(__file__).parent.parent / "data" / "entry_flow" / EXTRA_FILES_MAPPING.get(file_name, file_name)
)
target_path = Path(flow_path) / file_name
if target_path.exists() and not overwrite:
continue
action = "Overwriting" if target_path.exists() else "Creating"
print(f"{action} {target_path.resolve()}...")
shutil.copy2(extra_file_path, target_path)
class ToolPackageGenerator(BaseGenerator):
def __init__(self, tool_name, icon=None, extra_info=None):
self.tool_name = tool_name
self._extra_info = extra_info
self.icon = icon
@property
def extra_info(self):
if self._extra_info:
extra_info = {}
for k, v in self._extra_info.items():
try:
extra_info[k] = literal_eval(v)
except Exception:
extra_info[k] = repr(v)
return extra_info
else:
return {}
@property
def tpl_file(self):
return TOOL_TEMPLATE_PATH / "tool.py.jinja2"
@property
def entry_template_keys(self):
return ["tool_name", "extra_info", "icon"]
class SetupGenerator(BaseGenerator):
def __init__(self, package_name, tool_name):
self.package_name = package_name
self.tool_name = tool_name
@property
def tpl_file(self):
return TOOL_TEMPLATE_PATH / "setup.py.jinja2"
@property
def entry_template_keys(self):
return ["package_name", "tool_name"]
class ToolPackageUtilsGenerator(BaseGenerator):
def __init__(self, package_name):
self.package_name = package_name
@property
def tpl_file(self):
return TOOL_TEMPLATE_PATH / "utils.py.jinja2"
@property
def entry_template_keys(self):
return ["package_name"]
class ToolReadmeGenerator(BaseGenerator):
def __init__(self, package_name, tool_name):
self.package_name = package_name
self.tool_name = tool_name
@property
def tpl_file(self):
return TOOL_TEMPLATE_PATH / "README.md.jinja2"
@property
def entry_template_keys(self):
return ["package_name", "tool_name"]
class InitGenerator(BaseGenerator):
@property
def tpl_file(self):
return TOOL_TEMPLATE_PATH / "init.py"
@property
def entry_template_keys(self):
pass
def generate(self) -> str:
with open(self.tpl_file) as f:
init_content = f.read()
return init_content
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf/_run.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import argparse
import json
from typing import Callable, Dict, List, Optional, Tuple
from promptflow._cli._params import (
add_param_all_results,
add_param_archived_only,
add_param_columns_mapping,
add_param_connections,
add_param_environment_variables,
add_param_include_archived,
add_param_max_results,
add_param_output_format,
add_param_run_name,
add_param_set,
add_param_yes,
add_parser_build,
base_params,
)
from promptflow._cli._utils import (
_output_result_list_with_format,
activate_action,
confirm,
exception_handler,
list_of_dict_to_dict,
list_of_dict_to_nested_dict,
pretty_print_dataframe_as_table,
)
from promptflow._sdk._constants import MAX_SHOW_DETAILS_RESULTS, get_list_view_type
from promptflow._sdk._load_functions import load_run
from promptflow._sdk._pf_client import PFClient
from promptflow._sdk._run_functions import _create_run
from promptflow._sdk._utils import safe_parse_object_list
from promptflow._sdk.entities import Run
from promptflow.exceptions import UserErrorException
def add_run_parser(subparsers):
run_parser = subparsers.add_parser("run", description="A CLI tool to manage runs for prompt flow.", help="pf run")
subparsers = run_parser.add_subparsers()
add_run_create(subparsers)
# add_run_cancel(subparsers)
add_run_update(subparsers)
add_run_stream(subparsers)
add_run_list(subparsers)
add_run_show(subparsers)
add_run_show_details(subparsers)
add_run_show_metrics(subparsers)
add_run_visualize(subparsers)
add_run_archive(subparsers)
add_run_restore(subparsers)
add_run_delete(subparsers)
add_parser_build(subparsers, "run")
run_parser.set_defaults(action="run")
def add_run_create_common(subparsers, add_param_list, epilog: Optional[str] = None):
# pf run create --file batch_run.yaml [--stream]
add_param_file = lambda parser: parser.add_argument( # noqa: E731
"-f",
"--file",
dest="file",
type=str,
help="Local path to the YAML file containing the run definition. "
"Reference https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json for the schema.",
)
add_param_stream = lambda parser: parser.add_argument( # noqa: E731
"-s",
"--stream",
action="store_true",
default=False,
help="Indicates whether to stream the run's logs to the console.",
)
add_param_flow = lambda parser: parser.add_argument( # noqa: E731
"--flow",
type=str,
help="Local path to the flow directory."
"If --file is provided, this path should be relative path to the file.",
)
add_param_variant = lambda parser: parser.add_argument( # noqa: E731
"--variant", type=str, help="Node & variant name in format of ${node_name.variant_name}."
)
add_param_run = lambda parser: parser.add_argument( # noqa: E731
"--run",
type=str,
help="Referenced flow run name referenced by current run. "
"For example, you can run an evaluation flow against an existing run.",
)
add_param_name = lambda parser: parser.add_argument("-n", "--name", type=str, help="Name of the run.") # noqa: E731
add_params = [
add_param_file,
add_param_stream,
add_param_flow,
add_param_variant,
add_param_run,
add_param_name,
add_param_columns_mapping,
# add env var overwrite
add_param_environment_variables,
add_param_connections,
add_param_set,
] + base_params
add_params.extend(add_param_list)
create_parser = activate_action(
name="create",
description=None,
epilog=epilog or "pf run create --file <local-path-to-yaml> [--stream]",
add_params=add_params,
subparsers=subparsers,
help_message="Create a run.",
action_param_name="sub_action",
)
return create_parser
def add_run_create(subparsers):
epilog = """
Examples:
# Create a run with YAML file:
pf run create -f <yaml-filename>
# Create a run with YAML file and replace another data in the YAML file:
pf run create -f <yaml-filename> --data <path-to-new-data-file-relative-to-yaml-file>
# Create a run from flow directory and reference a run:
pf run create --flow <path-to-flow-directory> --data <path-to-data-file> --column-mapping groundtruth='${data.answer}' prediction='${run.outputs.category}' --run <run-name> --variant "${summarize_text_content.variant_0}" --stream # noqa: E501
# Create a run from an existing run record folder
pf run create --source <path-to-run-folder>
"""
# data for pf has different help doc than pfazure
def add_param_data(parser):
parser.add_argument(
"--data",
type=str,
help="Local path to the data file." "If --file is provided, this path should be relative path to the file.",
)
def add_param_source(parser):
parser.add_argument("--source", type=str, help="Local path to the existing run record folder.")
add_run_create_common(subparsers, [add_param_data, add_param_source], epilog=epilog)
def add_run_cancel(subparsers):
epilog = """
Example:
# Cancel a run:
pf run cancel --name <name>
"""
add_params = [add_param_run_name] + base_params
activate_action(
name="cancel",
description=None,
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Cancel a run.",
action_param_name="sub_action",
)
def add_run_update(subparsers):
epilog = """
Example:
# Update a run metadata:
pf run update --name <name> --set display_name="<display-name>" description="<description>" tags.key="<value>"
"""
add_params = [
add_param_run_name,
add_param_set,
] + base_params
activate_action(
name="update",
description=None,
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Update a run metadata, including display name, description and tags.",
action_param_name="sub_action",
)
def add_run_stream(subparsers):
epilog = """
Example:
# Stream run logs:
pf run stream --name <name>
"""
add_params = [add_param_run_name] + base_params
activate_action(
name="stream",
description=None,
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Stream run logs to the console.",
action_param_name="sub_action",
)
def add_run_list(subparsers):
epilog = """
Examples:
# List runs status:
pf run list
# List most recent 10 runs status:
pf run list --max-results 10
# List active and archived runs status:
pf run list --include-archived
# List archived runs status only:
pf run list --archived-only
# List all runs status:
pf run list --all-results
# List all runs status as table:
pf run list --output table
"""
add_params = [
add_param_max_results,
add_param_all_results,
add_param_archived_only,
add_param_include_archived,
add_param_output_format,
] + base_params
activate_action(
name="list",
description=None,
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="List runs.",
action_param_name="sub_action",
)
def add_run_show(subparsers):
epilog = """
Example:
# Show the status of a run:
pf run show --name <name>
"""
add_params = [add_param_run_name] + base_params
activate_action(
name="show",
description=None,
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Show details for a run.",
action_param_name="sub_action",
)
def add_run_show_details(subparsers):
epilog = """
Example:
# View input(s) and output(s) of a run:
pf run show-details --name <name>
"""
add_param_max_results = lambda parser: parser.add_argument( # noqa: E731
"-r",
"--max-results",
dest="max_results",
type=int,
default=MAX_SHOW_DETAILS_RESULTS,
help=f"Number of lines to show. Default is {MAX_SHOW_DETAILS_RESULTS}.",
)
add_params = [add_param_max_results, add_param_run_name, add_param_all_results] + base_params
activate_action(
name="show-details",
description=None,
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Preview a run's input(s) and output(s).",
action_param_name="sub_action",
)
def add_run_show_metrics(subparsers):
epilog = """
Example:
# View metrics of a run:
pf run show-metrics --name <name>
"""
add_params = [add_param_run_name] + base_params
activate_action(
name="show-metrics",
description=None,
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Print run metrics to the console.",
action_param_name="sub_action",
)
def add_run_visualize(subparsers):
epilog = """
Examples:
# Visualize a run:
pf run visualize -n <name>
# Visualize runs:
pf run visualize --names "<name1,name2>"
pf run visualize --names "<name1>, <name2>"
"""
add_param_name = lambda parser: parser.add_argument( # noqa: E731
"-n", "--names", type=str, required=True, help="Name of the runs, comma separated."
)
add_param_html_path = lambda parser: parser.add_argument( # noqa: E731
"--html-path", type=str, default=None, help=argparse.SUPPRESS
)
add_params = [add_param_name, add_param_html_path] + base_params
activate_action(
name="visualize",
description=None,
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Visualize a run.",
action_param_name="sub_action",
)
def add_run_delete(subparsers):
epilog = """
Example:
# Caution: pf run delete is irreversible.
# This operation will delete the run permanently from your local disk.
# Both run entity and output data will be deleted.
# Delete a run:
pf run delete -n "<name>"
"""
add_params = [add_param_run_name, add_param_yes] + base_params
activate_action(
name="delete",
description=None,
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Delete a run irreversible.",
action_param_name="sub_action",
)
def add_run_archive(subparsers):
epilog = """
Example:
# Archive a run:
pf run archive --name <name>
"""
add_params = [add_param_run_name] + base_params
activate_action(
name="archive",
description=None,
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Archive a run.",
action_param_name="sub_action",
)
def add_run_restore(subparsers):
epilog = """
Example:
# Restore an archived run:
pf run restore --name <name>
"""
add_params = [add_param_run_name] + base_params
activate_action(
name="restore",
description=None,
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Restore an archived run.",
action_param_name="sub_action",
)
def dispatch_run_commands(args: argparse.Namespace):
if args.sub_action == "create":
create_run(create_func=_create_run, args=args)
elif args.sub_action == "update":
update_run(name=args.name, params=args.params_override)
elif args.sub_action == "stream":
stream_run(name=args.name)
elif args.sub_action == "list":
list_runs(
max_results=args.max_results,
all_results=args.all_results,
archived_only=args.archived_only,
include_archived=args.include_archived,
output=args.output,
)
elif args.sub_action == "show":
show_run(name=args.name)
elif args.sub_action == "show-details":
show_run_details(name=args.name, max_results=args.max_results, all_results=args.all_results)
elif args.sub_action == "show-metrics":
show_run_metrics(name=args.name)
elif args.sub_action == "visualize":
visualize_run(names=args.names, html_path=args.html_path)
elif args.sub_action == "archive":
archive_run(name=args.name)
elif args.sub_action == "restore":
restore_run(name=args.name)
elif args.sub_action == "export":
export_run(args)
elif args.sub_action == "delete":
delete_run(args.name, args.yes)
else:
raise ValueError(f"Unrecognized command: {args.sub_action}")
def _parse_metadata_args(params: List[Dict[str, str]]) -> Tuple[Optional[str], Optional[str], Optional[Dict[str, str]]]:
display_name, description, tags = None, None, {}
for param in params:
for k, v in param.items():
if k == "display_name":
if display_name is not None:
raise ValueError("Duplicate argument: 'display_name'.")
display_name = v
elif k == "description":
if description is not None:
raise ValueError("Duplicate argument: 'description'.")
description = v
elif k.startswith("tags."):
tag_key = k.replace("tags.", "")
if tag_key in tags:
raise ValueError(f"Duplicate argument: 'tags.{tag_key}'.")
tags[tag_key] = v
if len(tags) == 0:
tags = None
return display_name, description, tags
@exception_handler("Update run")
def update_run(name: str, params: List[Dict[str, str]]) -> None:
# params_override can have multiple items when user specifies with
# `--set key1=value1 key2=value`
# so we need to merge them first.
display_name, description, tags = _parse_metadata_args(params)
pf_client = PFClient()
run = pf_client.runs.update(
name=name,
display_name=display_name,
description=description,
tags=tags,
)
print(json.dumps(run._to_dict(), indent=4))
@exception_handler("Stream run")
def stream_run(name: str) -> None:
pf_client = PFClient()
run = pf_client.runs.stream(name=name)
print(json.dumps(run._to_dict(), indent=4))
@exception_handler("List runs")
def list_runs(
max_results: int,
all_results: bool,
archived_only: bool,
include_archived: bool,
output,
):
pf_client = PFClient()
# aligned behaviour with v2 SDK, all_results will overwrite max_results
if all_results:
max_results = None
runs = pf_client.runs.list(
max_results=max_results,
list_view_type=get_list_view_type(archived_only=archived_only, include_archived=include_archived),
)
# hide additional info and debug info in run list for better user experience
parser = lambda run: run._to_dict(exclude_additional_info=True, exclude_debug_info=True) # noqa: E731
json_list = safe_parse_object_list(
obj_list=runs,
parser=parser,
message_generator=lambda x: f"Error parsing run {x.name!r}, skipped.",
)
_output_result_list_with_format(result_list=json_list, output_format=output)
return runs
@exception_handler("Show run")
def show_run(name: str) -> None:
pf_client = PFClient()
run = pf_client.runs.get(name=name)
print(json.dumps(run._to_dict(), indent=4))
@exception_handler("Show run details")
def show_run_details(name: str, max_results: int, all_results: bool) -> None:
pf_client = PFClient()
details = pf_client.runs.get_details(name=name, max_results=max_results, all_results=all_results)
pretty_print_dataframe_as_table(details)
@exception_handler("Show run metrics")
def show_run_metrics(name: str) -> None:
pf_client = PFClient()
metrics = pf_client.runs.get_metrics(name=name)
print(json.dumps(metrics, indent=4))
@exception_handler("Visualize run")
def visualize_run(names: str, html_path: Optional[str] = None) -> None:
run_names = [name.strip() for name in names.split(",")]
pf_client = PFClient()
pf_client.runs.visualize(run_names, html_path=html_path)
@exception_handler("Archive run")
def archive_run(name: str) -> None:
pf_client = PFClient()
run = pf_client.runs.archive(name=name)
print(json.dumps(run._to_dict(), indent=4))
@exception_handler("Restore run")
def restore_run(name: str) -> None:
pf_client = PFClient()
run = pf_client.runs.restore(name=name)
print(json.dumps(run._to_dict(), indent=4))
def _parse_kv_pair(kv_pairs: str) -> Dict[str, str]:
result = {}
for kv_pairs in kv_pairs.split(","):
kv_pair = kv_pairs.strip()
if "=" not in kv_pair:
raise ValueError(f"Invalid key-value pair: {kv_pair}")
key, value = kv_pair.split("=", 1)
result[key] = value
return result
@exception_handler("Create run")
def create_run(create_func: Callable, args):
file = args.file
flow = args.flow
run_source = getattr(args, "source", None) # source is only available for pf args, not pfazure.
data = args.data
column_mapping = args.column_mapping
variant = args.variant
name = args.name
run = args.run
stream = args.stream
environment_variables = args.environment_variables
connections = args.connections
params_override = args.params_override or []
if environment_variables:
environment_variables = list_of_dict_to_dict(environment_variables)
if connections:
connections = list_of_dict_to_nested_dict(connections)
if column_mapping:
column_mapping = list_of_dict_to_dict(column_mapping)
if file:
for param_key, param in {
"name": name,
"flow": flow,
"variant": variant,
"data": data,
"column_mapping": column_mapping,
"run": run,
"environment_variables": environment_variables,
"connections": connections,
}.items():
if not param:
continue
params_override.append({param_key: param})
run = load_run(source=file, params_override=params_override)
elif flow:
run_data = {
"name": name,
"flow": flow,
"data": data,
"column_mapping": column_mapping,
"run": run,
"variant": variant,
"environment_variables": environment_variables,
"connections": connections,
}
# remove empty fields
run_data = {k: v for k, v in run_data.items() if v is not None}
run = Run._load(data=run_data, params_override=params_override)
elif run_source:
display_name, description, tags = _parse_metadata_args(params_override)
processed_params = {
"display_name": display_name,
"description": description,
"tags": tags,
}
run = Run._load_from_source(source=run_source, params_override=processed_params)
else:
raise UserErrorException("To create a run, one of [file, flow, source] must be specified.")
run = create_func(run=run, stream=stream)
if stream:
print("\n") # change new line to show run info
print(json.dumps(run._to_dict(), indent=4))
@exception_handler("Delete run")
def delete_run(name: str, skip_confirm: bool = False) -> None:
if confirm("Are you sure to delete run irreversibly?", skip_confirm):
pf_client = PFClient()
pf_client.runs.delete(name=name)
else:
print("The delete operation was canceled.")
def export_run(args):
raise NotImplementedError()
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf/entry.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# pylint: disable=wrong-import-position
import json
import time
from promptflow._cli._pf._experiment import add_experiment_parser, dispatch_experiment_commands
from promptflow._cli._utils import _get_cli_activity_name
from promptflow._sdk._configuration import Configuration
from promptflow._sdk._telemetry import ActivityType, get_telemetry_logger, log_activity
from promptflow._sdk._telemetry.activity import update_activity_name
# Log the start time
start_time = time.perf_counter()
# E402 module level import not at top of file
import argparse # noqa: E402
import logging # noqa: E402
import sys # noqa: E402
from promptflow._cli._pf._config import add_config_parser, dispatch_config_commands # noqa: E402
from promptflow._cli._pf._connection import add_connection_parser, dispatch_connection_commands # noqa: E402
from promptflow._cli._pf._flow import add_flow_parser, dispatch_flow_commands # noqa: E402
from promptflow._cli._pf._run import add_run_parser, dispatch_run_commands # noqa: E402
from promptflow._cli._pf._tool import add_tool_parser, dispatch_tool_commands # noqa: E402
from promptflow._cli._pf.help import show_privacy_statement, show_welcome_message # noqa: E402
from promptflow._cli._pf._upgrade import add_upgrade_parser, upgrade_version # noqa: E402
from promptflow._cli._user_agent import USER_AGENT # noqa: E402
from promptflow._sdk._utils import ( # noqa: E402
get_promptflow_sdk_version,
print_pf_version,
setup_user_agent_to_operation_context,
)
from promptflow._utils.logger_utils import get_cli_sdk_logger # noqa: E402
# get logger for CLI
logger = get_cli_sdk_logger()
def run_command(args):
# Log the init finish time
init_finish_time = time.perf_counter()
try:
# --verbose, enable info logging
if hasattr(args, "verbose") and args.verbose:
for handler in logger.handlers:
handler.setLevel(logging.INFO)
# --debug, enable debug logging
if hasattr(args, "debug") and args.debug:
for handler in logger.handlers:
handler.setLevel(logging.DEBUG)
if args.version:
print_pf_version()
elif args.action == "flow":
dispatch_flow_commands(args)
elif args.action == "connection":
dispatch_connection_commands(args)
elif args.action == "run":
dispatch_run_commands(args)
elif args.action == "config":
dispatch_config_commands(args)
elif args.action == "tool":
dispatch_tool_commands(args)
elif args.action == "upgrade":
upgrade_version(args)
elif args.action == "experiment":
dispatch_experiment_commands(args)
except KeyboardInterrupt as ex:
logger.debug("Keyboard interrupt is captured.")
# raise UserErrorException(error=ex)
# Cant't raise UserErrorException due to the code exit(1) of promptflow._cli._utils.py line 368.
raise ex
except SystemExit as ex: # some code directly call sys.exit, this is to make sure command metadata is logged
exit_code = ex.code if ex.code is not None else 1
logger.debug(f"Code directly call sys.exit with code {exit_code}")
# raise UserErrorException(error=ex)
# Cant't raise UserErrorException due to the code exit(1) of promptflow._cli._utils.py line 368.
raise ex
except Exception as ex:
logger.debug(f"Command {args} execute failed. {str(ex)}")
# raise UserErrorException(error=ex)
# Cant't raise UserErrorException due to the code exit(1) of promptflow._cli._utils.py line 368.
raise ex
finally:
# Log the invoke finish time
invoke_finish_time = time.perf_counter()
logger.info(
"Command ran in %.3f seconds (init: %.3f, invoke: %.3f)",
invoke_finish_time - start_time,
init_finish_time - start_time,
invoke_finish_time - init_finish_time,
)
def get_parser_args(argv):
parser = argparse.ArgumentParser(
prog="pf",
formatter_class=argparse.RawDescriptionHelpFormatter,
description="pf: manage prompt flow assets. Learn more: https://microsoft.github.io/promptflow.",
)
parser.add_argument(
"-v", "--version", dest="version", action="store_true", help="show current CLI version and exit"
)
subparsers = parser.add_subparsers()
add_upgrade_parser(subparsers)
add_flow_parser(subparsers)
add_connection_parser(subparsers)
add_run_parser(subparsers)
add_config_parser(subparsers)
add_tool_parser(subparsers)
if Configuration.get_instance().is_internal_features_enabled():
add_experiment_parser(subparsers)
return parser.prog, parser.parse_args(argv)
def entry(argv):
"""
Control plane CLI tools for promptflow.
"""
prog, args = get_parser_args(argv)
if hasattr(args, "user_agent"):
setup_user_agent_to_operation_context(args.user_agent)
logger = get_telemetry_logger()
activity_name = _get_cli_activity_name(cli=prog, args=args)
activity_name = update_activity_name(activity_name, args=args)
with log_activity(
logger,
activity_name,
activity_type=ActivityType.PUBLICAPI,
):
run_command(args)
def main():
"""Entrance of pf CLI."""
command_args = sys.argv[1:]
if len(command_args) == 1 and command_args[0] == "version":
version_dict = {"promptflow": get_promptflow_sdk_version()}
version_dict_string = json.dumps(version_dict, ensure_ascii=False, indent=2, sort_keys=True,
separators=(",", ": ")) + "\n"
print(version_dict_string)
return
if len(command_args) == 0:
# print privacy statement & welcome message like azure-cli
show_privacy_statement()
show_welcome_message()
command_args.append("-h")
elif len(command_args) == 1:
# pf only has "pf --version" with 1 layer
if command_args[0] not in ["--version", "-v", "upgrade"]:
command_args.append("-h")
setup_user_agent_to_operation_context(USER_AGENT)
entry(command_args)
if __name__ == "__main__":
main()
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf/_config.py | import argparse
import json
from promptflow._cli._params import add_param_set_positional, base_params
from promptflow._cli._utils import activate_action, list_of_dict_to_dict
from promptflow._sdk._configuration import Configuration, InvalidConfigValue
from promptflow._sdk._utils import print_red_error
from promptflow._utils.logger_utils import get_cli_sdk_logger
logger = get_cli_sdk_logger()
def add_config_set(subparsers):
epilog = """
Examples:
# Config connection provider to azure workspace for current user:
pf config set connection.provider="azureml://subscriptions/<your-subscription>/resourceGroups/<your-resourcegroup>/providers/Microsoft.MachineLearningServices/workspaces/<your-workspace>"
""" # noqa: E501
activate_action(
name="set",
description="Set prompt flow configs for current user.",
epilog=epilog,
add_params=[add_param_set_positional] + base_params,
subparsers=subparsers,
help_message="Set prompt flow configs for current user, configs will be stored at ~/.promptflow/pf.yaml.",
action_param_name="sub_action",
)
def add_config_show(subparsers):
epilog = """
Examples:
# Show prompt flow for current user:
pf config show
"""
activate_action(
name="show",
description="Show prompt flow configs for current user.",
epilog=epilog,
add_params=base_params,
subparsers=subparsers,
help_message="Show prompt flow configs for current user.",
action_param_name="sub_action",
)
def add_config_parser(subparsers):
config_parser = subparsers.add_parser(
"config", description="A CLI tool to set prompt flow configs for current user.", help="pf config"
)
subparsers = config_parser.add_subparsers()
add_config_set(subparsers)
add_config_show(subparsers)
config_parser.set_defaults(action="config")
def dispatch_config_commands(args: argparse.Namespace):
if args.sub_action == "set":
set_config(args)
if args.sub_action == "show":
show_config()
def set_config(args):
params_override = list_of_dict_to_dict(args.params_override)
for k, v in params_override.items():
logger.debug("Setting config %s to %s", k, v)
try:
Configuration.get_instance().set_config(k, v)
print(f"Set config {args.params_override} successfully.")
except InvalidConfigValue as e:
error_message = f"Invalid config value {v!r} for {k!r}: {str(e)}"
print_red_error(error_message)
def show_config():
configs = Configuration.get_instance().get_all()
print(json.dumps(configs, indent=4))
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf/_tool.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import argparse
import json
import re
import shutil
from pathlib import Path
from promptflow._cli._params import add_param_set_tool_extra_info, base_params
from promptflow._cli._pf._init_entry_generators import (
InitGenerator,
SetupGenerator,
ToolPackageGenerator,
ToolPackageUtilsGenerator,
ToolReadmeGenerator,
)
from promptflow._cli._utils import activate_action, exception_handler, list_of_dict_to_dict
from promptflow._sdk._constants import DEFAULT_ENCODING
from promptflow._sdk._pf_client import PFClient
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow.exceptions import UserErrorException
logger = get_cli_sdk_logger()
def add_tool_parser(subparsers):
"""Add flow parser to the pf subparsers."""
tool_parser = subparsers.add_parser(
"tool",
description="Manage tools for promptflow.",
help="pf tool",
)
subparsers = tool_parser.add_subparsers()
add_parser_init_tool(subparsers)
add_parser_list_tool(subparsers)
add_parser_validate_tool(subparsers)
tool_parser.set_defaults(action="tool")
def add_parser_init_tool(subparsers):
"""Add tool init parser to the pf tool subparsers."""
epilog = """
Examples:
# Creating a package tool from scratch:
pf tool init --package package_tool --tool tool_name
# Creating a package tool with extra info:
pf tool init --package package_tool --tool tool_name --set icon=<icon-path> category=<category>
# Creating a python tool from scratch:
pf tool init --tool tool_name
""" # noqa: E501
add_param_package = lambda parser: parser.add_argument( # noqa: E731
"--package", type=str, help="The package name to create."
)
add_param_tool = lambda parser: parser.add_argument( # noqa: E731
"--tool", type=str, required=True, help="The tool name to create."
)
add_params = [
add_param_package,
add_param_tool,
add_param_set_tool_extra_info,
] + base_params
return activate_action(
name="init",
description="Creating a tool.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Initialize a tool directory.",
action_param_name="sub_action",
)
def add_parser_list_tool(subparsers):
"""Add tool list parser to the pf tool subparsers."""
epilog = """
Examples:
# List all package tool in the environment:
pf tool list
# List all package tool and code tool in the flow:
pf tool list --flow flow-path
""" # noqa: E501
add_param_flow = lambda parser: parser.add_argument("--flow", type=str, help="the flow directory") # noqa: E731
add_params = [
add_param_flow,
] + base_params
return activate_action(
name="list",
description="List tools.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="List all tools in the environment.",
action_param_name="sub_action",
)
def add_parser_validate_tool(subparsers):
"""Add tool list parser to the pf tool subparsers."""
epilog = """
Examples:
# Validate single function tool:
pf tool validate -–source <package_name>.<module_name>.<tool_function>
# Validate all tool in a package tool:
pf tool validate -–source <package_name>
# Validate tools in a python script:
pf tool validate --source <path_to_tool_script>
""" # noqa: E501
def add_param_source(parser):
parser.add_argument("--source", type=str, help="The tool source to be used.", required=True)
return activate_action(
name="validate",
description="Validate tool.",
epilog=epilog,
add_params=[
add_param_source,
],
subparsers=subparsers,
help_message="Validate tool. Will raise error if it is not valid.",
action_param_name="sub_action",
)
def dispatch_tool_commands(args: argparse.Namespace):
if args.sub_action == "init":
init_tool(args)
elif args.sub_action == "list":
list_tool(args)
elif args.sub_action == "validate":
validate_tool(args)
@exception_handler("Tool init")
def init_tool(args):
# Validate package/tool name
pattern = r"^[a-zA-Z_][a-zA-Z0-9_]*$"
if args.package and not re.match(pattern, args.package):
raise UserErrorException(f"The package name {args.package} is a invalid identifier.")
if not re.match(pattern, args.tool):
raise UserErrorException(f"The tool name {args.tool} is a invalid identifier.")
print("Creating tool from scratch...")
extra_info = list_of_dict_to_dict(args.extra_info)
icon_path = extra_info.pop("icon", None)
if icon_path and not Path(icon_path).exists():
raise UserErrorException(f"Cannot find the icon path {icon_path}.")
if args.package:
package_path = Path(args.package)
package_name = package_path.stem
script_code_path = package_path / package_name
script_code_path.mkdir(parents=True, exist_ok=True)
# Generate manifest file
manifest_file = package_path / "MANIFEST.in"
manifest_file.touch(exist_ok=True)
with open(manifest_file, "r") as f:
manifest_contents = [line.strip() for line in f.readlines()]
if icon_path:
package_icon_path = package_path / "icons"
package_icon_path.mkdir(exist_ok=True)
dst = shutil.copy2(icon_path, package_icon_path)
icon_path = f'Path(__file__).parent.parent / "icons" / "{Path(dst).name}"'
icon_manifest = f"include {package_name}/icons"
if icon_manifest not in manifest_contents:
manifest_contents.append(icon_manifest)
with open(manifest_file, "w", encoding=DEFAULT_ENCODING) as f:
f.writelines("\n".join(set(manifest_contents)))
# Generate package setup.py
SetupGenerator(package_name=package_name, tool_name=args.tool).generate_to_file(package_path / "setup.py")
# Generate utils.py to list meta data of tools.
ToolPackageUtilsGenerator(package_name=package_name).generate_to_file(script_code_path / "utils.py")
ToolReadmeGenerator(package_name=package_name, tool_name=args.tool).generate_to_file(package_path / "README.md")
else:
script_code_path = Path(".")
if icon_path:
icon_path = f'"{Path(icon_path).as_posix()}"'
# Generate tool script
ToolPackageGenerator(tool_name=args.tool, icon=icon_path, extra_info=extra_info).generate_to_file(
script_code_path / f"{args.tool}.py"
)
InitGenerator().generate_to_file(script_code_path / "__init__.py")
print(f'Done. Created the tool "{args.tool}" in {script_code_path.resolve()}.')
@exception_handler("Tool list")
def list_tool(args):
pf_client = PFClient()
package_tools = pf_client._tools.list(args.flow)
print(json.dumps(package_tools, indent=4))
@exception_handler("Tool validate")
def validate_tool(args):
import importlib
pf_client = PFClient()
try:
__import__(args.source)
source = importlib.import_module(args.source)
logger.debug(f"The source {args.source} is used as a package to validate.")
except ImportError:
try:
module_name, func_name = args.source.rsplit(".", 1)
module = importlib.import_module(module_name)
source = getattr(module, func_name)
logger.debug(f"The source {args.source} is used as a function to validate.")
except Exception:
if not Path(args.source).exists():
raise UserErrorException("Invalid source to validate tools.")
logger.debug(f"The source {args.source} is used as a script to validate.")
source = args.source
validation_result = pf_client._tools.validate(source)
print(repr(validation_result))
if not validation_result.passed:
exit(1)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf/_connection.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import argparse
import json
from functools import partial
from promptflow._cli._params import (
add_param_all_results,
add_param_max_results,
add_param_set,
add_param_yes,
base_params,
)
from promptflow._cli._utils import activate_action, confirm, exception_handler, get_secret_input, print_yellow_warning
from promptflow._sdk._constants import MAX_LIST_CLI_RESULTS
from promptflow._sdk._load_functions import load_connection
from promptflow._sdk._pf_client import PFClient
from promptflow._sdk.entities._connection import _Connection
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow._utils.yaml_utils import load_yaml
logger = get_cli_sdk_logger()
_client = None
def _get_pf_client():
global _client
if _client is None:
_client = PFClient()
return _client
def add_param_file(parser):
parser.add_argument("--file", "-f", type=str, help="File path of the connection yaml.", required=True)
def add_param_name(parser, required=False):
parser.add_argument("--name", "-n", type=str, help="Name of the connection.", required=required)
def add_connection_parser(subparsers):
connection_parser = subparsers.add_parser(
"connection",
description="""A CLI tool to manage connections for promptflow.
Your secrets will be encrypted using AES(Advanced Encryption Standard) technology.""", # noqa: E501
help="pf connection",
)
subparsers = connection_parser.add_subparsers()
add_connection_create(subparsers)
add_connection_update(subparsers)
add_connection_show(subparsers)
add_connection_list(subparsers)
add_connection_delete(subparsers)
connection_parser.set_defaults(action="connection")
def add_connection_create(subparsers):
# Do not change the indent of epilog
epilog = """
Examples:
# Creating a connection with yaml file:
pf connection create -f connection.yaml
# Creating a connection with yaml file and overrides:
pf connection create -f connection.yaml --set api_key="my_api_key"
# Creating a custom connection with .env file, note that overrides specified by --set will be ignored:
pf connection create -f .env --name custom
"""
activate_action(
name="create",
description="Create a connection.",
epilog=epilog,
add_params=[add_param_set, add_param_file, add_param_name] + base_params,
subparsers=subparsers,
help_message="Create a connection.",
action_param_name="sub_action",
)
def add_connection_update(subparsers):
epilog = """
Examples:
# Updating a connection:
pf connection update -n my_connection --set api_key="my_api_key"
"""
activate_action(
name="update",
description="Update a connection.",
epilog=epilog,
add_params=[add_param_set, partial(add_param_name, required=True)] + base_params,
subparsers=subparsers,
help_message="Update a connection.",
action_param_name="sub_action",
)
def add_connection_show(subparsers):
epilog = """
Examples:
# Get and show a connection:
pf connection show -n my_connection_name
"""
activate_action(
name="show",
description="Show a connection for promptflow.",
epilog=epilog,
add_params=[partial(add_param_name, required=True)] + base_params,
subparsers=subparsers,
help_message="Show a connection for promptflow.",
action_param_name="sub_action",
)
def add_connection_delete(subparsers):
epilog = """
Examples:
# Delete a connection:
pf connection delete -n my_connection_name
"""
activate_action(
name="delete",
description="Delete a connection with specific name.",
epilog=epilog,
add_params=[partial(add_param_name, required=True), add_param_yes] + base_params,
subparsers=subparsers,
help_message="Delete a connection with specific name.",
action_param_name="sub_action",
)
def add_connection_list(subparsers):
epilog = """
Examples:
# List all connections:
pf connection list
"""
activate_action(
name="list",
description="List all connections.",
epilog=epilog,
add_params=[add_param_max_results, add_param_all_results] + base_params,
subparsers=subparsers,
help_message="List all connections.",
action_param_name="sub_action",
)
def validate_and_interactive_get_secrets(connection, is_update=False):
"""Validate the connection and interactive get secrets if no secrets is provided."""
prompt = "=================== Please input required secrets ==================="
missing_secrets_prompt = False
for name, val in connection.secrets.items():
if not _Connection._is_scrubbed_value(val) and not _Connection._is_user_input_value(val):
# Not scrubbed value, not require user input.
continue
if is_update and _Connection._is_scrubbed_value(val):
# Scrubbed value, will use existing, not require user input.
continue
if not missing_secrets_prompt:
print(prompt)
missing_secrets_prompt = True
while True:
secret = get_secret_input(prompt=f"{name}: ")
if secret:
break
print_yellow_warning("Secret can't be empty.")
connection.secrets[name] = secret
if missing_secrets_prompt:
print("=================== Required secrets collected ===================")
return connection
# Note the connection secrets value behaviors:
# --------------------------------------------------------------------------------
# | secret value | CLI create | CLI update | SDK create_or_update |
# --------------------------------------------------------------------------------
# | empty or all "*" | prompt input | use existing values | use existing values |
# | <no-change> | prompt input | use existing values | use existing values |
# | <user-input> | prompt input | prompt input | raise error |
# --------------------------------------------------------------------------------
@exception_handler("Connection create")
def create_connection(file_path, params_override=None, name=None):
params_override = params_override or []
if name:
params_override.append({"name": name})
connection = load_connection(source=file_path, params_override=params_override)
existing_connection = _get_pf_client().connections.get(connection.name, raise_error=False)
if existing_connection:
logger.warning(f"Connection with name {connection.name} already exists. Updating it.")
# Note: We don't set the existing secret back here, let user input the secrets.
validate_and_interactive_get_secrets(connection)
connection = _get_pf_client().connections.create_or_update(connection)
print(json.dumps(connection._to_dict(), indent=4))
@exception_handler("Connection show")
def show_connection(name):
connection = _get_pf_client().connections.get(name)
print(json.dumps(connection._to_dict(), indent=4))
@exception_handler("Connection list")
def list_connection(max_results=MAX_LIST_CLI_RESULTS, all_results=False):
connections = _get_pf_client().connections.list(max_results, all_results)
print(json.dumps([connection._to_dict() for connection in connections], indent=4))
def _upsert_connection_from_file(file, params_override=None):
# Note: This function is used for pfutil, do not edit it.
params_override = params_override or []
params_override.append(load_yaml(file))
connection = load_connection(source=file, params_override=params_override)
existing_connection = _get_pf_client().connections.get(connection.name, raise_error=False)
if existing_connection:
connection = _Connection._load(data=existing_connection._to_dict(), params_override=params_override)
validate_and_interactive_get_secrets(connection, is_update=True)
# Set the secrets not scrubbed, as _to_dict() dump scrubbed connections.
connection._secrets = existing_connection._secrets
else:
validate_and_interactive_get_secrets(connection)
connection = _get_pf_client().connections.create_or_update(connection)
return connection
@exception_handler("Connection update")
def update_connection(name, params_override=None):
params_override = params_override or []
existing_connection = _get_pf_client().connections.get(name)
connection = _Connection._load(data=existing_connection._to_dict(), params_override=params_override)
validate_and_interactive_get_secrets(connection, is_update=True)
# Set the secrets not scrubbed, as _to_dict() dump scrubbed connections.
connection._secrets = existing_connection._secrets
connection = _get_pf_client().connections.create_or_update(connection)
print(json.dumps(connection._to_dict(), indent=4))
@exception_handler("Connection delete")
def delete_connection(name, skip_confirm: bool = False):
if confirm("Are you sure you want to perform this operation?", skip_confirm):
_get_pf_client().connections.delete(name)
else:
print("The delete operation was canceled.")
def dispatch_connection_commands(args: argparse.Namespace):
if args.sub_action == "create":
create_connection(args.file, args.params_override, args.name)
elif args.sub_action == "show":
show_connection(args.name)
elif args.sub_action == "list":
list_connection(args.max_results, args.all_results)
elif args.sub_action == "update":
update_connection(args.name, args.params_override)
elif args.sub_action == "delete":
delete_connection(args.name, args.yes)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf/_flow.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import argparse
import importlib
import json
import os
import shutil
import subprocess
import sys
import tempfile
import webbrowser
from pathlib import Path
from promptflow._cli._params import (
add_param_config,
add_param_entry,
add_param_environment_variables,
add_param_flow_display_name,
add_param_function,
add_param_inputs,
add_param_prompt_template,
add_param_source,
add_param_yes,
add_parser_build,
base_params,
)
from promptflow._cli._pf._init_entry_generators import (
AzureOpenAIConnectionGenerator,
ChatFlowDAGGenerator,
FlowDAGGenerator,
OpenAIConnectionGenerator,
StreamlitFileReplicator,
ToolMetaGenerator,
ToolPyGenerator,
copy_extra_files,
)
from promptflow._cli._pf._run import exception_handler
from promptflow._cli._utils import _copy_to_flow, activate_action, confirm, inject_sys_path, list_of_dict_to_dict
from promptflow._constants import LANGUAGE_KEY, FlowLanguage
from promptflow._sdk._constants import PROMPT_FLOW_DIR_NAME, ConnectionProvider
from promptflow._sdk._pf_client import PFClient
from promptflow._sdk.operations._flow_operations import FlowOperations
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow.exceptions import ErrorTarget, UserErrorException
DEFAULT_CONNECTION = "open_ai_connection"
DEFAULT_DEPLOYMENT = "gpt-35-turbo"
logger = get_cli_sdk_logger()
def add_flow_parser(subparsers):
"""Add flow parser to the pf subparsers."""
flow_parser = subparsers.add_parser(
"flow",
description="Manage flows for promptflow.",
help="pf flow",
)
flow_subparsers = flow_parser.add_subparsers()
add_parser_init_flow(flow_subparsers)
add_parser_test_flow(flow_subparsers)
add_parser_serve_flow(flow_subparsers)
add_parser_build(flow_subparsers, "flow")
add_parser_validate_flow(flow_subparsers)
flow_parser.set_defaults(action="flow")
def dispatch_flow_commands(args: argparse.Namespace):
if args.sub_action == "init":
init_flow(args)
elif args.sub_action == "test":
test_flow(args)
elif args.sub_action == "serve":
serve_flow(args)
elif args.sub_action == "build":
build_flow(args)
elif args.sub_action == "validate":
validate_flow(args)
def add_parser_init_flow(subparsers):
"""Add flow create parser to the pf flow subparsers."""
epilog = """
Examples:
# Creating a flow folder with code/prompts and yaml definitions of the flow:
pf flow init --flow my-awesome-flow
# Creating an eval prompt flow:
pf flow init --flow my-awesome-flow --type evaluation
# Creating a flow in existing folder
pf flow init --flow intent_copilot --entry intent.py --function extract_intent --prompt-template prompt_template=tpl.jinja2
""" # noqa: E501
add_param_type = lambda parser: parser.add_argument( # noqa: E731
"--type",
type=str,
choices=["standard", "evaluation", "chat"],
help="The initialized flow type.",
default="standard",
)
add_param_connection = lambda parser: parser.add_argument( # noqa: E731
"--connection", type=str, help=argparse.SUPPRESS
)
add_param_deployment = lambda parser: parser.add_argument( # noqa: E731
"--deployment", type=str, help=argparse.SUPPRESS
)
add_params = [
add_param_type,
add_param_yes,
add_param_flow_display_name,
add_param_entry,
add_param_function,
add_param_prompt_template,
add_param_connection,
add_param_deployment,
] + base_params
activate_action(
name="init",
description="Creating a flow folder with code/prompts and yaml definitions of the flow.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Initialize a prompt flow directory.",
action_param_name="sub_action",
)
def add_parser_serve_flow(subparsers):
"""Add flow serve parser to the pf flow subparsers."""
epilog = """
Examples:
# Serve flow as an endpoint:
pf flow serve --source <path_to_flow>
# Serve flow as an endpoint with specific port and host:
pf flow serve --source <path_to_flow> --port 8080 --host localhost --environment-variables key1="`${my_connection.api_key}" key2="value2"
# Serve flow without opening browser:
pf flow serve --source <path_to_flow> --skip-open-browser
""" # noqa: E501
add_param_port = lambda parser: parser.add_argument( # noqa: E731
"--port", type=int, default=8080, help="The port on which endpoint to run."
)
add_param_host = lambda parser: parser.add_argument( # noqa: E731
"--host", type=str, default="localhost", help="The host of endpoint."
)
add_param_static_folder = lambda parser: parser.add_argument( # noqa: E731
"--static_folder", type=str, help=argparse.SUPPRESS
)
add_param_skip_browser = lambda parser: parser.add_argument( # noqa: E731
"--skip-open-browser", action="store_true", default=False, help="Skip open browser for flow serving."
)
activate_action(
name="serve",
description="Serving a flow as an endpoint.",
epilog=epilog,
add_params=[
add_param_source,
add_param_port,
add_param_host,
add_param_static_folder,
add_param_environment_variables,
add_param_config,
add_param_skip_browser,
]
+ base_params,
subparsers=subparsers,
help_message="Serving a flow as an endpoint.",
action_param_name="sub_action",
)
def add_parser_validate_flow(subparsers):
"""Add flow validate parser to the pf flow subparsers."""
epilog = """
Examples:
# Validate flow
pf flow validate --source <path_to_flow>
""" # noqa: E501
activate_action(
name="validate",
description="Validate a flow and generate flow.tools.json for the flow.",
epilog=epilog,
add_params=[
add_param_source,
]
+ base_params,
subparsers=subparsers,
help_message="Validate a flow. Will raise error if the flow is not valid.",
action_param_name="sub_action",
)
def add_parser_test_flow(subparsers):
"""Add flow test parser to the pf flow subparsers."""
epilog = """
Examples:
# Test the flow:
pf flow test --flow my-awesome-flow
# Test the flow with inputs:
pf flow test --flow my-awesome-flow --inputs key1=val1 key2=val2
# Test the flow with specified variant node:
pf flow test --flow my-awesome-flow --variant ${node_name.variant_name}
# Test the single node in the flow:
pf flow test --flow my-awesome-flow --node node_name
# Chat in the flow:
pf flow test --flow my-awesome-flow --node node_name --interactive
""" # noqa: E501
add_param_flow = lambda parser: parser.add_argument( # noqa: E731
"--flow", type=str, required=True, help="the flow directory to test."
)
add_param_node = lambda parser: parser.add_argument( # noqa: E731
"--node", type=str, help="the node name in the flow need to be tested."
)
add_param_variant = lambda parser: parser.add_argument( # noqa: E731
"--variant", type=str, help="Node & variant name in format of ${node_name.variant_name}."
)
add_param_interactive = lambda parser: parser.add_argument( # noqa: E731
"--interactive", action="store_true", help="start a interactive chat session for chat flow."
)
add_param_multi_modal = lambda parser: parser.add_argument( # noqa: E731
"--multi-modal", action="store_true", help=argparse.SUPPRESS
)
add_param_ui = lambda parser: parser.add_argument("--ui", action="store_true", help=argparse.SUPPRESS) # noqa: E731
add_param_input = lambda parser: parser.add_argument("--input", type=str, help=argparse.SUPPRESS) # noqa: E731
add_param_detail = lambda parser: parser.add_argument( # noqa: E731
"--detail", type=str, default=None, required=False, help=argparse.SUPPRESS
)
add_params = [
add_param_flow,
add_param_node,
add_param_variant,
add_param_interactive,
add_param_input,
add_param_inputs,
add_param_environment_variables,
add_param_multi_modal,
add_param_ui,
add_param_config,
add_param_detail,
] + base_params
activate_action(
name="test",
description="Test the flow.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Test the prompt flow or flow node.",
action_param_name="sub_action",
)
def init_flow(args):
if any([args.entry, args.prompt_template]):
print("Creating flow from existing folder...")
prompt_tpl = {}
if args.prompt_template:
for _dct in args.prompt_template:
prompt_tpl.update(**_dct)
_init_existing_flow(args.flow, args.entry, args.function, prompt_tpl)
else:
# Create an example flow
print("Creating flow from scratch...")
_init_flow_by_template(args.flow, args.type, args.yes, args.connection, args.deployment)
def _init_existing_flow(flow_name, entry=None, function=None, prompt_params: dict = None):
flow_path = Path(flow_name).resolve()
if not function:
logger.error("--function must be specified when --entry is specified.")
return
if not flow_path.exists():
logger.error(f"{flow_path.resolve()} must exist when --entry specified.")
return
print(f"Change working directory to .. {flow_path.resolve()}")
os.chdir(flow_path)
entry = Path(entry).resolve()
if not entry.exists():
logger.error(f"{entry} must exist.")
return
with inject_sys_path(flow_path):
# import function object
function_obj = getattr(importlib.import_module(entry.stem), function)
# Create tool.py
tool_py = f"{function}_tool.py"
python_tool = ToolPyGenerator(entry, function, function_obj)
tools = ToolMetaGenerator(tool_py, function, function_obj, prompt_params)
python_tool_inputs = [arg.name for arg in python_tool.tool_arg_list]
for tool_input in tools.prompt_params.keys():
if tool_input not in python_tool_inputs:
error = ValueError(f"Template parameter {tool_input} doesn't find in python function arguments.")
raise UserErrorException(target=ErrorTarget.CONTROL_PLANE_SDK, message=str(error), error=error)
python_tool.generate_to_file(tool_py)
# Create .promptflow and flow.tools.json
meta_dir = flow_path / PROMPT_FLOW_DIR_NAME
meta_dir.mkdir(parents=True, exist_ok=True)
tools.generate_to_file(meta_dir / "flow.tools.json")
# Create flow.dag.yaml
FlowDAGGenerator(tool_py, function, function_obj, prompt_params).generate_to_file("flow.dag.yaml")
copy_extra_files(flow_path=flow_path, extra_files=["requirements.txt", ".gitignore"])
print(f"Done. Generated flow in folder: {flow_path.resolve()}.")
def _init_chat_flow(flow_name, flow_path, connection=None, deployment=None):
from promptflow._sdk._configuration import Configuration
example_flow_path = Path(__file__).parent.parent / "data" / "chat_flow" / "flow_files"
for item in list(example_flow_path.iterdir()):
_copy_to_flow(flow_path=flow_path, source_file=item)
# Generate flow.dag.yaml to chat flow.
connection = connection or DEFAULT_CONNECTION
deployment = deployment or DEFAULT_DEPLOYMENT
ChatFlowDAGGenerator(connection=connection, deployment=deployment).generate_to_file(flow_path / "flow.dag.yaml")
# When customer not configure the remote connection provider, create connection yaml to chat flow.
is_local_connection = Configuration.get_instance().get_connection_provider() == ConnectionProvider.LOCAL
if is_local_connection:
OpenAIConnectionGenerator(connection=connection).generate_to_file(flow_path / "openai.yaml")
AzureOpenAIConnectionGenerator(connection=connection).generate_to_file(flow_path / "azure_openai.yaml")
copy_extra_files(flow_path=flow_path, extra_files=["requirements.txt", ".gitignore"])
print(f"Done. Created chat flow folder: {flow_path.resolve()}.")
if is_local_connection:
print(
f"The generated chat flow is requiring a connection named {connection}, "
"please follow the steps in README.md to create if you haven't done that."
)
else:
print(
f"The generated chat flow is requiring a connection named {connection}, "
"please ensure it exists in workspace."
)
flow_test_command = f"pf flow test --flow {flow_name} --interactive"
print(f"You can execute this command to test the flow, {flow_test_command}")
def _init_standard_or_evaluation_flow(flow_name, flow_path, flow_type):
example_flow_path = Path(__file__).parent.parent / "data" / f"{flow_type}_flow"
for item in list(example_flow_path.iterdir()):
_copy_to_flow(flow_path=flow_path, source_file=item)
copy_extra_files(flow_path=flow_path, extra_files=["requirements.txt", ".gitignore"])
print(f"Done. Created {flow_type} flow folder: {flow_path.resolve()}.")
flow_test_command = f"pf flow test --flow {flow_name} --input {os.path.join(flow_name, 'data.jsonl')}"
print(f"You can execute this command to test the flow, {flow_test_command}")
def _init_flow_by_template(flow_name, flow_type, overwrite=False, connection=None, deployment=None):
flow_path = Path(flow_name)
if flow_path.exists():
if not flow_path.is_dir():
logger.error(f"{flow_path.resolve()} is not a folder.")
return
answer = confirm(
"The flow folder already exists, do you want to create the flow in this existing folder?", overwrite
)
if not answer:
print("The 'pf init' command has been cancelled.")
return
flow_path.mkdir(parents=True, exist_ok=True)
if flow_type == "chat":
_init_chat_flow(flow_name=flow_name, flow_path=flow_path, connection=connection, deployment=deployment)
else:
_init_standard_or_evaluation_flow(flow_name=flow_name, flow_path=flow_path, flow_type=flow_type)
@exception_handler("Flow test")
def test_flow(args):
from promptflow._sdk._load_functions import load_flow
config = list_of_dict_to_dict(args.config)
pf_client = PFClient(config=config)
if args.environment_variables:
environment_variables = list_of_dict_to_dict(args.environment_variables)
else:
environment_variables = {}
inputs = {}
if args.input:
from promptflow._utils.load_data import load_data
if args.input and not args.input.endswith(".jsonl"):
error = ValueError("Only support jsonl file as input.")
raise UserErrorException(
target=ErrorTarget.CONTROL_PLANE_SDK,
message=str(error),
error=error,
)
inputs = load_data(local_path=args.input)[0]
if args.inputs:
inputs.update(list_of_dict_to_dict(args.inputs))
if args.multi_modal or args.ui:
with tempfile.TemporaryDirectory() as temp_dir:
flow = load_flow(args.flow)
script_path = [
os.path.join(temp_dir, "main.py"),
os.path.join(temp_dir, "utils.py"),
os.path.join(temp_dir, "logo.png"),
]
for script in script_path:
StreamlitFileReplicator(
flow_name=flow.display_name if flow.display_name else flow.name,
flow_dag_path=flow.flow_dag_path,
).generate_to_file(script)
main_script_path = os.path.join(temp_dir, "main.py")
pf_client.flows._chat_with_ui(script=main_script_path)
else:
if args.interactive:
pf_client.flows._chat(
flow=args.flow,
inputs=inputs,
environment_variables=environment_variables,
variant=args.variant,
show_step_output=args.verbose,
)
else:
result = pf_client.flows.test(
flow=args.flow,
inputs=inputs,
environment_variables=environment_variables,
variant=args.variant,
node=args.node,
allow_generator_output=False,
stream_output=False,
dump_test_result=True,
detail=args.detail,
)
# Print flow/node test result
if isinstance(result, dict):
print(json.dumps(result, indent=4, ensure_ascii=False))
else:
print(result)
def serve_flow(args):
from promptflow._sdk._load_functions import load_flow
logger.info("Start serve model: %s", args.source)
# Set environment variable for local test
source = Path(args.source)
logger.info(
"Start promptflow server with port %s",
args.port,
)
os.environ["PROMPTFLOW_PROJECT_PATH"] = source.absolute().as_posix()
flow = load_flow(args.source)
if flow.dag.get(LANGUAGE_KEY, FlowLanguage.Python) == FlowLanguage.CSharp:
serve_flow_csharp(args, source)
else:
serve_flow_python(args, source)
logger.info("Promptflow app ended")
def serve_flow_csharp(args, source):
from promptflow.batch._csharp_executor_proxy import EXECUTOR_SERVICE_DLL
try:
# Change working directory to model dir
logger.info(f"Change working directory to model dir {source}")
os.chdir(source)
command = [
"dotnet",
EXECUTOR_SERVICE_DLL,
"--port",
str(args.port),
"--yaml_path",
"flow.dag.yaml",
"--assembly_folder",
".",
"--connection_provider_url",
"",
"--log_path",
"",
"--serving",
]
subprocess.run(command, stdout=sys.stdout, stderr=sys.stderr)
except KeyboardInterrupt:
pass
def _resolve_python_flow_additional_includes(source) -> Path:
# Resolve flow additional includes
from promptflow import load_flow
flow = load_flow(source)
with FlowOperations._resolve_additional_includes(flow.path) as resolved_flow_path:
if resolved_flow_path == flow.path:
return source
# Copy resolved flow to temp folder if additional includes exists
# Note: DO NOT use resolved flow path directly, as when inner logic raise exception,
# temp dir will fail due to file occupied by other process.
temp_flow_path = Path(tempfile.TemporaryDirectory().name)
shutil.copytree(src=resolved_flow_path.parent, dst=temp_flow_path, dirs_exist_ok=True)
return temp_flow_path
def serve_flow_python(args, source):
from promptflow._sdk._serving.app import create_app
static_folder = args.static_folder
if static_folder:
static_folder = Path(static_folder).absolute().as_posix()
config = list_of_dict_to_dict(args.config)
source = _resolve_python_flow_additional_includes(source)
os.environ["PROMPTFLOW_PROJECT_PATH"] = source.absolute().as_posix()
logger.info(f"Change working directory to model dir {source}")
os.chdir(source)
app = create_app(
static_folder=static_folder,
environment_variables=list_of_dict_to_dict(args.environment_variables),
config=config,
)
if not args.skip_open_browser:
target = f"http://{args.host}:{args.port}"
logger.info(f"Opening browser {target}...")
webbrowser.open(target)
# Debug is not supported for now as debug will rerun command, and we changed working directory.
app.run(port=args.port, host=args.host)
def build_flow(args):
"""
i. `pf flow build --source <flow_folder> --output <output_folder> --variant <variant>`
ii. `pf flow build --source <flow_folder> --format docker --output <output_folder> --variant <variant>`
iii. `pf flow build --source <flow_folder> --format executable --output <output_folder> --variant <variant>`
# default to resolve variant and update flow.dag.yaml, support this in case customer want to keep the
variants for continuous development
# we can delay this before receiving specific customer request
v. `pf flow build --source <flow_folder> --output <output_folder> --keep-variants`
output structure:
flow/
.connections/
Dockerfile|executable.exe
...
"""
pf_client = PFClient()
pf_client.flows.build(
flow=args.source,
output=args.output,
format=args.format,
variant=args.variant,
flow_only=args.flow_only,
)
print(
f"Exported flow to {Path(args.output).absolute().as_posix()}.\n"
f"please check {Path(args.output).joinpath('README.md').absolute().as_posix()} "
f"for how to use it."
)
def validate_flow(args):
pf_client = PFClient()
validation_result = pf_client.flows.validate(
flow=args.source,
)
print(repr(validation_result))
if not validation_result.passed:
exit(1)
else:
exit(0)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf/help.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from promptflow._sdk._configuration import Configuration
# This logic is copied from: https://github.com/microsoft/knack/blob/dev/knack/help.py
# Will print privacy message and welcome when user run `pf` command.
PRIVACY_STATEMENT = """
Welcome to prompt flow!
---------------------
Use `pf -h` to see available commands or go to https://aka.ms/pf-cli.
Telemetry
---------
The prompt flow CLI collects usage data in order to improve your experience.
The data is anonymous and does not include commandline argument values.
The data is collected by Microsoft.
You can change your telemetry settings with `pf config`.
"""
WELCOME_MESSAGE = r"""
____ _ __ _
| _ \ _ __ ___ _ __ ___ _ __ | |_ / _| | _____ __
| |_) | '__/ _ \| '_ ` _ \| '_ \| __| | |_| |/ _ \ \ /\ / /
| __/| | | (_) | | | | | | |_) | |_ | _| | (_) \ V V /
|_| |_| \___/|_| |_| |_| .__/ \__| |_| |_|\___/ \_/\_/
|_|
Welcome to the cool prompt flow CLI!
Use `pf --version` to display the current version.
Here are the base commands:
"""
def show_privacy_statement():
config = Configuration.get_instance()
ran_before = config.get_config("first_run")
if not ran_before:
print(PRIVACY_STATEMENT)
config.set_config("first_run", True)
def show_welcome_message():
print(WELCOME_MESSAGE)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf_azure/_utils.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from promptflow._cli._utils import get_client_for_cli
from promptflow.azure import PFClient
def _get_azure_pf_client(subscription_id, resource_group, workspace_name, debug=False):
ml_client = get_client_for_cli(
subscription_id=subscription_id, resource_group_name=resource_group, workspace_name=workspace_name
)
client = PFClient(ml_client=ml_client, logging_enable=debug)
return client
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf_azure/_run.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import argparse
import functools
import json
from typing import Dict, List, Optional
from promptflow._cli._params import (
add_param_all_results,
add_param_archived_only,
add_param_include_archived,
add_param_max_results,
add_param_output,
add_param_output_format,
add_param_overwrite,
add_param_run_name,
add_param_set,
base_params,
)
from promptflow._cli._pf._run import _parse_metadata_args, add_run_create_common, create_run
from promptflow._cli._pf_azure._utils import _get_azure_pf_client
from promptflow._cli._utils import (
_output_result_list_with_format,
_set_workspace_argument_for_subparsers,
activate_action,
exception_handler,
pretty_print_dataframe_as_table,
)
from promptflow._sdk._constants import MAX_SHOW_DETAILS_RESULTS, ListViewType
from promptflow._sdk._errors import InvalidRunStatusError
from promptflow._sdk._utils import print_red_error
from promptflow.azure._restclient.flow_service_caller import FlowRequestException
def add_parser_run(subparsers):
"""Add run parser to the pfazure subparsers."""
run_parser = subparsers.add_parser(
"run", description="A CLI tool to manage cloud runs for prompt flow.", help="Manage prompt flow runs."
)
subparsers = run_parser.add_subparsers()
add_run_create_cloud(subparsers)
add_parser_run_list(subparsers)
add_parser_run_stream(subparsers)
add_parser_run_show(subparsers)
add_parser_run_show_details(subparsers)
add_parser_run_show_metrics(subparsers)
add_parser_run_cancel(subparsers)
add_parser_run_visualize(subparsers)
add_parser_run_archive(subparsers)
add_parser_run_restore(subparsers)
add_parser_run_update(subparsers)
add_parser_run_download(subparsers)
run_parser.set_defaults(action="run")
def add_run_create_cloud(subparsers):
epilog = """
Example:
# Create a run with YAML file:
pfazure run create -f <yaml-filename>
# Create a run from flow directory and reference a run:
pfazure run create --flow <path-to-flow-directory> --data <path-to-data-file> --column-mapping groundtruth='${data.answer}' prediction='${run.outputs.category}' --run <run-name> --variant "${summarize_text_content.variant_0}" --stream
# Create a run from existing workspace flow
pfazure run create --flow azureml:<flow-name> --data <path-to-data-file> --column-mapping <key-value-pair>
# Create a run from existing registry flow
pfazure run create --flow azureml://registries/<registry-name>/models/<flow-name>/versions/<version> --data <path-to-data-file> --column-mapping <key-value-pair>
""" # noqa: E501
def add_param_data(parser):
# cloud pf can also accept remote data
parser.add_argument(
"--data", type=str, help="Local path to the data file or remote data. e.g. azureml:name:version."
)
add_param_runtime = lambda parser: parser.add_argument("--runtime", type=str, help=argparse.SUPPRESS) # noqa: E731
add_param_reset = lambda parser: parser.add_argument( # noqa: E731
"--reset-runtime", action="store_true", help=argparse.SUPPRESS
)
add_run_create_common(
subparsers,
[add_param_data, add_param_runtime, add_param_reset, _set_workspace_argument_for_subparsers],
epilog=epilog,
)
def add_parser_run_list(subparsers):
"""Add run list parser to the pfazure subparsers."""
epilog = """
Examples:
# List runs status:
pfazure run list
# List most recent 10 runs status:
pfazure run list --max-results 10
# List active and archived runs status:
pfazure run list --include-archived
# List archived runs status only:
pfazure run list --archived-only
# List all runs status as table:
pfazure run list --output table
"""
add_params = [
add_param_max_results,
add_param_archived_only,
add_param_include_archived,
add_param_output_format,
_set_workspace_argument_for_subparsers,
] + base_params
activate_action(
name="list",
description="A CLI tool to List all runs.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="List runs in a workspace.",
action_param_name="sub_action",
)
def add_parser_run_stream(subparsers):
"""Add run stream parser to the pfazure subparsers."""
epilog = """
Example:
# Stream run logs:
pfazure run stream --name <name>
"""
add_params = [
add_param_run_name,
_set_workspace_argument_for_subparsers,
] + base_params
activate_action(
name="stream",
description="A CLI tool to stream run logs to the console.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Stream run logs to the console.",
action_param_name="sub_action",
)
def add_parser_run_show(subparsers):
"""Add run show parser to the pfazure subparsers."""
epilog = """
Example:
# Show the status of a run:
pfazure run show --name <name>
"""
add_params = [
add_param_run_name,
_set_workspace_argument_for_subparsers,
] + base_params
activate_action(
name="show",
description="A CLI tool to show a run.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Show a run.",
action_param_name="sub_action",
)
def add_parser_run_show_details(subparsers):
"""Add run show details parser to the pfazure subparsers."""
epilog = """
Example:
# View input(s) and output(s) of a run:
pfazure run show-details --name <name>
"""
add_param_max_results = lambda parser: parser.add_argument( # noqa: E731
"-r",
"--max-results",
dest="max_results",
type=int,
default=MAX_SHOW_DETAILS_RESULTS,
help=f"Number of lines to show. Default is {MAX_SHOW_DETAILS_RESULTS}.",
)
add_params = [
add_param_max_results,
add_param_run_name,
add_param_all_results,
_set_workspace_argument_for_subparsers,
] + base_params
activate_action(
name="show-details",
description="A CLI tool to show a run details.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Show a run details.",
action_param_name="sub_action",
)
def add_parser_run_show_metrics(subparsers):
"""Add run show metrics parser to the pfazure subparsers."""
epilog = """
Example:
# View metrics of a run:
pfazure run show-metrics --name <name>
"""
add_params = [
add_param_run_name,
_set_workspace_argument_for_subparsers,
] + base_params
activate_action(
name="show-metrics",
description="A CLI tool to show run metrics.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Show run metrics.",
action_param_name="sub_action",
)
def add_parser_run_cancel(subparsers):
"""Add run cancel parser to the pfazure subparsers."""
epilog = """
Example:
# Cancel a run:
pfazure run cancel --name <name>
"""
add_params = [
add_param_run_name,
_set_workspace_argument_for_subparsers,
] + base_params
activate_action(
name="cancel",
description="A CLI tool to cancel a run.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Cancel a run.",
action_param_name="sub_action",
)
def add_parser_run_visualize(subparsers):
"""Add run visualize parser to the pfazure subparsers."""
epilog = """
Examples:
# Visualize a run:
pfazure run visualize -n <name>
# Visualize runs:
pfazure run visualize --names "<name1,name2>"
pfazure run visualize --names "<name1>, <name2>"
"""
add_param_name = lambda parser: parser.add_argument( # noqa: E731
"-n", "--names", type=str, required=True, help="Name of the runs, comma separated."
)
add_param_html_path = lambda parser: parser.add_argument( # noqa: E731
"--html-path", type=str, default=None, help=argparse.SUPPRESS
)
add_params = [
add_param_name,
add_param_html_path,
_set_workspace_argument_for_subparsers,
] + base_params
activate_action(
name="visualize",
description="A CLI tool to visualize a run.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Visualize a run.",
action_param_name="sub_action",
)
def add_parser_run_archive(subparsers):
"""Add run archive parser to the pfazure subparsers."""
epilog = """
Examples:
# Archive a run:
pfazure run archive -n <name>
"""
add_params = [
add_param_run_name,
_set_workspace_argument_for_subparsers,
] + base_params
activate_action(
name="archive",
description="A CLI tool to archive a run.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Archive a run.",
action_param_name="sub_action",
)
def add_parser_run_restore(subparsers):
"""Add run restore parser to the pfazure subparsers."""
epilog = """
Examples:
# Restore an archived run:
pfazure run restore -n <name>
"""
add_params = [
add_param_run_name,
_set_workspace_argument_for_subparsers,
] + base_params
activate_action(
name="restore",
description="A CLI tool to restore a run.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Restore a run.",
action_param_name="sub_action",
)
def add_parser_run_update(subparsers):
"""Add run update parser to the pfazure subparsers."""
epilog = """
Example:
# Update a run metadata:
pfazure run update --name <name> --set display_name="<display-name>" description="<description>" tags.key="<value>"
"""
add_params = [
add_param_run_name,
add_param_set,
_set_workspace_argument_for_subparsers,
] + base_params
activate_action(
name="update",
description="A CLI tool to update a run.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Update a run.",
action_param_name="sub_action",
)
def add_parser_run_download(subparsers):
"""Add run download parser to the pfazure subparsers."""
epilog = """
Example:
# Download a run data to local:
pfazure run download --name <name> --output <output-folder-path>
"""
add_params = [
add_param_run_name,
add_param_output,
add_param_overwrite,
_set_workspace_argument_for_subparsers,
] + base_params
activate_action(
name="download",
description="A CLI tool to download a run.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Download a run.",
action_param_name="sub_action",
)
def dispatch_run_commands(args: argparse.Namespace):
if args.sub_action == "create":
pf = _get_azure_pf_client(args.subscription, args.resource_group, args.workspace_name, debug=args.debug)
create_run(
create_func=functools.partial(
pf.runs.create_or_update, runtime=args.runtime, reset_runtime=args.reset_runtime
),
args=args,
)
elif args.sub_action == "list":
list_runs(
args.subscription,
args.resource_group,
args.workspace_name,
args.max_results,
args.archived_only,
args.include_archived,
args.output,
)
elif args.sub_action == "show":
show_run(args.subscription, args.resource_group, args.workspace_name, args.name)
elif args.sub_action == "show-details":
show_run_details(
args.subscription,
args.resource_group,
args.workspace_name,
args.name,
args.max_results,
args.all_results,
args.debug,
)
elif args.sub_action == "show-metrics":
show_metrics(args.subscription, args.resource_group, args.workspace_name, args.name)
elif args.sub_action == "stream":
stream_run(args.subscription, args.resource_group, args.workspace_name, args.name, args.debug)
elif args.sub_action == "visualize":
visualize(
args.subscription,
args.resource_group,
args.workspace_name,
args.names,
args.html_path,
args.debug,
)
elif args.sub_action == "archive":
archive_run(args.subscription, args.resource_group, args.workspace_name, args.name)
elif args.sub_action == "restore":
restore_run(args.subscription, args.resource_group, args.workspace_name, args.name)
elif args.sub_action == "update":
update_run(args.subscription, args.resource_group, args.workspace_name, args.name, params=args.params_override)
elif args.sub_action == "download":
download_run(args)
elif args.sub_action == "cancel":
cancel_run(args)
@exception_handler("List runs")
def list_runs(
subscription_id,
resource_group,
workspace_name,
max_results,
archived_only,
include_archived,
output,
):
"""List all runs from cloud."""
if max_results < 1:
raise ValueError(f"'max_results' must be a positive integer, got {max_results!r}")
# Default list_view_type is ACTIVE_ONLY
if archived_only and include_archived:
raise ValueError("Cannot specify both 'archived_only' and 'include_archived'")
list_view_type = ListViewType.ACTIVE_ONLY
if archived_only:
list_view_type = ListViewType.ARCHIVED_ONLY
if include_archived:
list_view_type = ListViewType.ALL
pf = _get_azure_pf_client(subscription_id, resource_group, workspace_name)
runs = pf.runs.list(max_results=max_results, list_view_type=list_view_type)
# hide additional info, debug info and properties in run list for better user experience
run_list = [
run._to_dict(exclude_additional_info=True, exclude_debug_info=True, exclude_properties=True) for run in runs
]
_output_result_list_with_format(result_list=run_list, output_format=output)
return runs
@exception_handler("Show run")
def show_run(subscription_id, resource_group, workspace_name, run_name):
"""Show a run from cloud."""
pf = _get_azure_pf_client(subscription_id, resource_group, workspace_name)
run = pf.runs.get(run=run_name)
print(json.dumps(run._to_dict(), indent=4))
@exception_handler("Show run details")
def show_run_details(subscription_id, resource_group, workspace_name, run_name, max_results, all_results, debug=False):
"""Show a run details from cloud."""
pf = _get_azure_pf_client(subscription_id, resource_group, workspace_name, debug=debug)
details = pf.runs.get_details(run=run_name, max_results=max_results, all_results=all_results)
details.fillna(value="(Failed)", inplace=True) # replace nan with explicit prompt
pretty_print_dataframe_as_table(details)
@exception_handler("Show run metrics")
def show_metrics(subscription_id, resource_group, workspace_name, run_name):
"""Show run metrics from cloud."""
pf = _get_azure_pf_client(subscription_id, resource_group, workspace_name)
metrics = pf.runs.get_metrics(run=run_name)
print(json.dumps(metrics, indent=4))
@exception_handler("Stream run")
def stream_run(subscription_id, resource_group, workspace_name, run_name, debug=False):
"""Stream run logs from cloud."""
pf = _get_azure_pf_client(subscription_id, resource_group, workspace_name, debug=debug)
run = pf.runs.stream(run_name)
print("\n")
print(json.dumps(run._to_dict(), indent=4))
@exception_handler("Visualize run")
def visualize(
subscription_id: str,
resource_group: str,
workspace_name: str,
names: str,
html_path: Optional[str] = None,
debug: bool = False,
):
run_names = [name.strip() for name in names.split(",")]
pf = _get_azure_pf_client(subscription_id, resource_group, workspace_name, debug=debug)
try:
pf.runs.visualize(run_names, html_path=html_path)
except FlowRequestException as e:
error_message = f"Visualize failed, request service error: {str(e)}"
print_red_error(error_message)
except InvalidRunStatusError as e:
error_message = f"Visualize failed: {str(e)}"
print_red_error(error_message)
@exception_handler("Archive run")
def archive_run(
subscription_id: str,
resource_group: str,
workspace_name: str,
run_name: str,
):
pf = _get_azure_pf_client(subscription_id, resource_group, workspace_name)
run = pf.runs.archive(run=run_name)
print(json.dumps(run._to_dict(), indent=4))
@exception_handler("Restore run")
def restore_run(
subscription_id: str,
resource_group: str,
workspace_name: str,
run_name: str,
):
pf = _get_azure_pf_client(subscription_id, resource_group, workspace_name)
run = pf.runs.restore(run=run_name)
print(json.dumps(run._to_dict(), indent=4))
@exception_handler("Update run")
def update_run(
subscription_id: str,
resource_group: str,
workspace_name: str,
run_name: str,
params: List[Dict[str, str]],
):
# params_override can have multiple items when user specifies with
# `--set key1=value1 key2=value`
# so we need to merge them first.
display_name, description, tags = _parse_metadata_args(params)
pf = _get_azure_pf_client(subscription_id, resource_group, workspace_name)
run = pf.runs.update(run=run_name, display_name=display_name, description=description, tags=tags)
print(json.dumps(run._to_dict(), indent=4))
@exception_handler("Download run")
def download_run(args: argparse.Namespace):
pf = _get_azure_pf_client(args.subscription, args.resource_group, args.workspace_name, debug=args.debug)
pf.runs.download(run=args.name, output=args.output, overwrite=args.overwrite)
@exception_handler("Cancel run")
def cancel_run(args: argparse.Namespace):
pf = _get_azure_pf_client(args.subscription, args.resource_group, args.workspace_name, debug=args.debug)
pf.runs.cancel(run=args.name)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf_azure/entry.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# pylint: disable=wrong-import-position
import json
import time
from promptflow._cli._pf.help import show_privacy_statement, show_welcome_message
from promptflow._cli._user_agent import USER_AGENT
from promptflow._cli._utils import _get_cli_activity_name, get_client_info_for_cli
from promptflow._sdk._telemetry import ActivityType, get_telemetry_logger, log_activity
# Log the start time
start_time = time.perf_counter()
# E402 module level import not at top of file
import argparse # noqa: E402
import logging # noqa: E402
import sys # noqa: E402
from promptflow._cli._pf_azure._flow import add_parser_flow, dispatch_flow_commands # noqa: E402
from promptflow._cli._pf_azure._run import add_parser_run, dispatch_run_commands # noqa: E402
from promptflow._sdk._utils import ( # noqa: E402
get_promptflow_sdk_version,
print_pf_version,
setup_user_agent_to_operation_context,
)
from promptflow._utils.logger_utils import get_cli_sdk_logger # noqa: E402
# get logger for CLI
logger = get_cli_sdk_logger()
def run_command(args):
# Log the init finish time
init_finish_time = time.perf_counter()
try:
# --verbose, enable info logging
if hasattr(args, "verbose") and args.verbose:
for handler in logger.handlers:
handler.setLevel(logging.INFO)
# --debug, enable debug logging
if hasattr(args, "debug") and args.debug:
for handler in logger.handlers:
handler.setLevel(logging.DEBUG)
if args.version:
print_pf_version()
elif args.action == "run":
dispatch_run_commands(args)
elif args.action == "flow":
dispatch_flow_commands(args)
except KeyboardInterrupt as ex:
logger.debug("Keyboard interrupt is captured.")
raise ex
except SystemExit as ex: # some code directly call sys.exit, this is to make sure command metadata is logged
exit_code = ex.code if ex.code is not None else 1
logger.debug(f"Code directly call sys.exit with code {exit_code}")
raise ex
except Exception as ex:
logger.debug(f"Command {args} execute failed. {str(ex)}")
raise ex
finally:
# Log the invoke finish time
invoke_finish_time = time.perf_counter()
logger.info(
"Command ran in %.3f seconds (init: %.3f, invoke: %.3f)",
invoke_finish_time - start_time,
init_finish_time - start_time,
invoke_finish_time - init_finish_time,
)
def get_parser_args(argv):
parser = argparse.ArgumentParser(
prog="pfazure",
formatter_class=argparse.RawDescriptionHelpFormatter,
description="pfazure: manage prompt flow assets in azure. Learn more: https://microsoft.github.io/promptflow.",
)
parser.add_argument(
"-v", "--version", dest="version", action="store_true", help="show current CLI version and exit"
)
subparsers = parser.add_subparsers()
add_parser_run(subparsers)
add_parser_flow(subparsers)
return parser.prog, parser.parse_args(argv)
def _get_workspace_info(args):
try:
subscription_id, resource_group_name, workspace_name = get_client_info_for_cli(
subscription_id=args.subscription,
resource_group_name=args.resource_group,
workspace_name=args.workspace_name,
)
return {
"subscription_id": subscription_id,
"resource_group_name": resource_group_name,
"workspace_name": workspace_name,
}
except Exception:
# fall back to empty dict if workspace info is not available
return {}
def entry(argv):
"""
Control plane CLI tools for promptflow cloud version.
"""
prog, args = get_parser_args(argv)
if hasattr(args, "user_agent"):
setup_user_agent_to_operation_context(args.user_agent)
logger = get_telemetry_logger()
custom_dimensions = _get_workspace_info(args)
with log_activity(
logger,
_get_cli_activity_name(cli=prog, args=args),
activity_type=ActivityType.PUBLICAPI,
custom_dimensions=custom_dimensions,
):
run_command(args)
def main():
"""Entrance of pf CLI."""
command_args = sys.argv[1:]
if len(command_args) == 1 and command_args[0] == "version":
version_dict = {"promptflow": get_promptflow_sdk_version()}
return json.dumps(version_dict, ensure_ascii=False, indent=2, sort_keys=True, separators=(",", ": ")) + "\n"
if len(command_args) == 0:
# print privacy statement & welcome message like azure-cli
show_privacy_statement()
show_welcome_message()
command_args.append("-h")
elif len(command_args) == 1:
# pfazure only has "pf --version" with 1 layer
if command_args[0] not in ["--version", "-v"]:
command_args.append("-h")
setup_user_agent_to_operation_context(USER_AGENT)
entry(command_args)
if __name__ == "__main__":
main()
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf_azure/_connection.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from pathlib import Path
from dotenv import dotenv_values
from promptflow._cli._params import add_param_connection_name, add_param_env, base_params
from promptflow._cli._utils import _set_workspace_argument_for_subparsers, activate_action, get_client_for_cli
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow.connections import CustomConnection
from promptflow.contracts.types import Secret
logger = get_cli_sdk_logger()
def add_connection_parser(subparsers):
connection_parser = subparsers.add_parser(
"connection", description="A CLI tool to manage connections for promptflow.", help="pf connection"
)
subparsers = connection_parser.add_subparsers()
add_connection_create(subparsers)
add_connection_get(subparsers)
connection_parser.set_defaults(action="connection")
def add_connection_create(subparsers):
add_param_type = lambda parser: parser.add_argument( # noqa: E731
"--type",
type=str,
help='Type of the connection, Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing", '
'"Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM',
)
add_params = [
_set_workspace_argument_for_subparsers,
add_param_connection_name,
add_param_type,
add_param_env,
] + base_params
activate_action(
name="create",
description="Create a connection for promptflow.",
epilog=None,
add_params=add_params,
subparsers=subparsers,
help_message="pf connection create",
action_param_name="sub_action",
)
def add_connection_get(subparsers):
add_params = [
_set_workspace_argument_for_subparsers,
add_param_connection_name,
add_param_env,
] + base_params
activate_action(
name="get",
description="Get a connection for promptflow.",
epilog=None,
add_params=add_params,
subparsers=subparsers,
help_message="pf connection get",
action_param_name="sub_action",
)
def _get_conn_operations(subscription_id, resource_group, workspace_name):
from promptflow.azure import PFClient
client = get_client_for_cli(
subscription_id=subscription_id, workspace_name=workspace_name, resource_group_name=resource_group
)
pf = PFClient(ml_client=client)
return pf._connections
def create_conn(name, type, env, subscription_id, resource_group, workspace_name):
from promptflow._sdk.entities._connection import _Connection
if not Path(env).exists():
raise ValueError(f"Env file {env} does not exist.")
try:
dot_env = dotenv_values(env)
except Exception as e:
raise ValueError(f"Failed to load env file {env}. Error: {e}")
custom_configs = CustomConnection(**{k: Secret(v) for k, v in dot_env.items()})
connection = _Connection(name=name, type=type, custom_configs=custom_configs, connection_scope="WorkspaceShared")
conn_ops = _get_conn_operations(subscription_id, resource_group, workspace_name)
result = conn_ops.create_or_update(connection=connection)
print(result._to_yaml())
def get_conn(name, subscription_id, resource_group, workspace_name):
conn_ops = _get_conn_operations(subscription_id, resource_group, workspace_name)
result = conn_ops.get(name=name)
print(result._to_yaml())
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf_azure/_flow.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import argparse
import json
from typing import Dict, List
from promptflow._cli._params import (
add_param_archived_only,
add_param_flow_name,
add_param_flow_type,
add_param_include_archived,
add_param_include_others,
add_param_max_results,
add_param_output_format,
add_param_set,
base_params,
)
from promptflow._cli._pf_azure._utils import _get_azure_pf_client
from promptflow._cli._utils import (
_output_result_list_with_format,
_set_workspace_argument_for_subparsers,
activate_action,
exception_handler,
)
from promptflow._sdk._constants import get_list_view_type
def add_parser_flow(subparsers):
"""Add flow parser to the pf subparsers."""
flow_parser = subparsers.add_parser(
"flow",
description="Manage flows for prompt flow.",
help="Manage prompt flows.",
)
flow_subparsers = flow_parser.add_subparsers()
add_parser_flow_create(flow_subparsers)
add_parser_flow_show(flow_subparsers)
add_parser_flow_list(flow_subparsers)
flow_parser.set_defaults(action="flow")
def add_parser_flow_create(subparsers):
"""Add flow create parser to the pf flow subparsers."""
epilog = """
Use "--set" to set flow properties like:
display_name: Flow display name that will be created in remote. Default to be flow folder name + timestamp if not specified.
type: Flow type. Default to be "standard" if not specified. Available types are: "standard", "evaluation", "chat".
description: Flow description. e.g. "--set description=<description>."
tags: Flow tags. e.g. "--set tags.key1=value1 tags.key2=value2."
Note:
In "--set" parameter, if the key name consists of multiple words, use snake-case instead of kebab-case. e.g. "--set display_name=<flow-display-name>"
Examples:
# Create a flow to azure portal with local flow folder.
pfazure flow create --flow <flow-folder-path> --set display_name=<flow-display-name> type=<flow-type>
# Create a flow with more properties
pfazure flow create --flow <flow-folder-path> --set display_name=<flow-display-name> type=<flow-type> description=<flow-description> tags.key1=value1 tags.key2=value2
""" # noqa: E501
add_param_source = lambda parser: parser.add_argument( # noqa: E731
"--flow", type=str, help="Source folder of the flow."
)
add_params = [
_set_workspace_argument_for_subparsers,
add_param_source,
add_param_set,
] + base_params
activate_action(
name="create",
description="A CLI tool to create a flow to Azure.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Create a flow to Azure with local flow folder.",
action_param_name="sub_action",
)
def add_parser_flow_list(subparsers):
"""Add flow list parser to the pf flow subparsers."""
epilog = """
Examples:
# List flows:
pfazure flow list
# List most recent 10 runs status:
pfazure flow list --max-results 10
# List active and archived flows:
pfazure flow list --include-archived
# List archived flow only:
pfazure flow list --archived-only
# List all flows as table:
pfazure flow list --output table
# List flows with specific type:
pfazure flow list --type standard
# List flows that are owned by all users:
pfazure flow list --include-others
"""
add_params = [
add_param_max_results,
add_param_include_others,
add_param_flow_type,
add_param_archived_only,
add_param_include_archived,
add_param_output_format,
_set_workspace_argument_for_subparsers,
] + base_params
activate_action(
name="list",
description="List flows for promptflow.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="pfazure flow list",
action_param_name="sub_action",
)
def add_parser_flow_show(subparsers):
"""Add flow get parser to the pf flow subparsers."""
epilog = """
Examples:
# Get flow:
pfazure flow show --name <flow-name>
"""
add_params = [add_param_flow_name, _set_workspace_argument_for_subparsers] + base_params
activate_action(
name="show",
description="Show a flow from Azure.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="pfazure flow show",
action_param_name="sub_action",
)
def add_parser_flow_download(subparsers):
"""Add flow download parser to the pf flow subparsers."""
add_param_source = lambda parser: parser.add_argument( # noqa: E731
"--source", type=str, help="The flow folder path on file share to download."
)
add_param_destination = lambda parser: parser.add_argument( # noqa: E731
"--destination", "-d", type=str, help="The destination folder path to download."
)
add_params = [
_set_workspace_argument_for_subparsers,
add_param_source,
add_param_destination,
] + base_params
activate_action(
name="download",
description="Download a flow from file share to local.",
epilog=None,
add_params=add_params,
subparsers=subparsers,
help_message="pf flow download",
action_param_name="sub_action",
)
def dispatch_flow_commands(args: argparse.Namespace):
if args.sub_action == "create":
create_flow(args)
elif args.sub_action == "show":
show_flow(args)
elif args.sub_action == "list":
list_flows(args)
def _get_flow_operation(subscription_id, resource_group, workspace_name):
pf_client = _get_azure_pf_client(subscription_id, resource_group, workspace_name)
return pf_client._flows
@exception_handler("Create flow")
def create_flow(args: argparse.Namespace):
"""Create a flow for promptflow."""
pf = _get_azure_pf_client(args.subscription, args.resource_group, args.workspace_name, debug=args.debug)
params = _parse_flow_metadata_args(args.params_override)
pf.flows.create_or_update(flow=args.flow, **params)
@exception_handler("Show flow")
def show_flow(args: argparse.Namespace):
"""Get a flow for promptflow."""
pf = _get_azure_pf_client(args.subscription, args.resource_group, args.workspace_name, debug=args.debug)
flow = pf.flows.get(args.name)
print(json.dumps(flow._to_dict(), indent=4))
def list_flows(args: argparse.Namespace):
"""List flows for promptflow."""
pf = _get_azure_pf_client(args.subscription, args.resource_group, args.workspace_name, debug=args.debug)
flows = pf.flows.list(
max_results=args.max_results,
include_others=args.include_others,
flow_type=args.type,
list_view_type=get_list_view_type(args.archived_only, args.include_archived),
)
flow_list = [flow._to_dict() for flow in flows]
_output_result_list_with_format(flow_list, args.output)
def _parse_flow_metadata_args(params: List[Dict[str, str]]) -> Dict:
result, tags = {}, {}
if not params:
return result
for param in params:
for k, v in param.items():
if k.startswith("tags."):
tag_key = k.replace("tags.", "")
tags[tag_key] = v
continue
result[k] = v
if tags:
result["tags"] = tags
return result
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/_cli | promptflow_repo/promptflow/src/promptflow/promptflow/_cli/_pf_azure/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/storage/_errors.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
class DuplicatedPrimaryKeyException(Exception):
pass
class NotFoundException(Exception):
pass
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/storage/_cache_storage.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from dataclasses import dataclass
from datetime import datetime
from promptflow.contracts.run_info import RunInfo
@dataclass
class CacheRecord:
run_id: str
hash_id: str
flow_run_id: str
flow_id: str
cache_string: str
end_time: datetime
class AbstractCacheStorage:
def get_cache_record_list(hash_id: str) -> CacheRecord:
pass
def persist_cache_result(run_info: RunInfo):
pass
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/storage/run_records.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
from dataclasses import asdict, dataclass
from datetime import datetime
from promptflow._utils.dataclass_serializer import serialize
from promptflow.contracts.run_info import FlowRunInfo, RunInfo
@dataclass
class NodeRunRecord:
"""Dataclass for storing the run record of each node during single line execution on the flow
:param str node_name: The name of the node
:param int line_number: The line number in the source file
:param str run_info: The information about the run
:param datetime start_time: The time the node started running
:param datetime end_time: The time the node finished running
:param str status: The status of the node run
"""
node_name: str
line_number: int
run_info: str
start_time: datetime
end_time: datetime
status: str
@staticmethod
def from_run_info(run_info: RunInfo) -> "NodeRunRecord":
"""Create a NodeRunRecord from a RunInfo object.
:param RunInfo run_info: The run info to create the NodeRunRecord from
:return: The created NodeRunRecord
:rtype: NodeRunRecord
"""
return NodeRunRecord(
node_name=run_info.node,
line_number=run_info.index,
run_info=serialize(run_info),
start_time=run_info.start_time.isoformat(),
end_time=run_info.end_time.isoformat(),
status=run_info.status.value,
)
def serialize(self) -> str:
"""Serialize the NodeRunRecord for storage in blob.
:return: The serialized result
:rtype: str
"""
return json.dumps(asdict(self))
@dataclass
class LineRunRecord:
"""A dataclass for storing the run record of a single line execution on the flow.
:param int line_number: The line number in the record
:param str run_info: The information about the line run
:param datetime start_time: The time the line started executing
:param datetime end_time: The time the line finished executing
:param str name: The name of the line run
:param str description: The description of the line run
:param str status: The status of the line execution
:param str tags: The tags associated with the line run
"""
line_number: int
run_info: str
start_time: datetime
end_time: datetime
name: str
description: str
status: str
tags: str
@staticmethod
def from_run_info(run_info: FlowRunInfo) -> "LineRunRecord":
"""Create a LineRunRecord from a FlowRunInfo object.
:param FlowRunInfo run_info: The run info to create the LineRunRecord from
:return: The created LineRunRecord
:rtype: LineRunRecord
"""
return LineRunRecord(
line_number=run_info.index,
run_info=serialize(run_info),
start_time=run_info.start_time.isoformat(),
end_time=run_info.end_time.isoformat(),
name=run_info.name,
description=run_info.description,
status=run_info.status.value,
tags=run_info.tags,
)
def serialize(self) -> str:
"""Serialize the LineRunRecord for storage in a blob.
:return: The serialized result
:rtype: str
"""
return json.dumps(asdict(self))
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/storage/_run_storage.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from functools import partial
from pathlib import Path
from typing import Union
from promptflow._utils.multimedia_utils import _process_recursively, get_file_reference_encoder
from promptflow.contracts.multimedia import Image
from promptflow.contracts.run_info import FlowRunInfo
from promptflow.contracts.run_info import RunInfo as NodeRunInfo
class AbstractRunStorage:
def persist_node_run(self, run_info: NodeRunInfo):
"""Write the node run info to somewhere immediately after the node is executed.
:param run_info: The run info of the node.
:type run_info: ~promptflow.contracts.run_info.RunInfo
"""
raise NotImplementedError("AbstractRunStorage is an abstract class, no implementation for persist_node_run.")
def persist_flow_run(self, run_info: FlowRunInfo):
"""Write the flow run info to somewhere immediately after one line data is executed for the flow.
:param run_info: The run info of the node.
:type run_info: ~promptflow.contracts.run_info.RunInfo
"""
raise NotImplementedError("AbstractRunStorage is an abstract class, no implementation for persist_flow_run.")
class DummyRunStorage(AbstractRunStorage):
def persist_node_run(self, run_info: NodeRunInfo):
"""Dummy implementation for persist_node_run
:param run_info: The run info of the node.
:type run_info: ~promptflow.contracts.run_info.RunInfo
"""
pass
def persist_flow_run(self, run_info: FlowRunInfo):
"""Dummy implementation for persist_flow_run
:param run_info: The run info of the node.
:type run_info: ~promptflow.contracts.run_info.RunInfo
"""
pass
class DefaultRunStorage(AbstractRunStorage):
def __init__(self, base_dir: Path = None, sub_dir: Path = None):
"""Initialize the default run storage.
:param base_dir: The base directory to store the multimedia data.
:type base_dir: Path
:param sub_dir: The sub directory to store the multimedia data.
:type sub_dir: Path
"""
self._base_dir = base_dir
self._sub_dir = sub_dir
def persist_run_info(self, run_info: Union[FlowRunInfo, NodeRunInfo]):
"""Persist the multimedia data in run info after execution.
:param run_info: The run info of the node or flow.
:type run_info: ~promptflow.contracts.run_info.RunInfo or ~promptflow.contracts.run_info.FlowRunInfo
"""
# Persist and convert images in inputs to path dictionaries.
# This replaces any image objects with their corresponding file path dictionaries.
if run_info.inputs:
run_info.inputs = self._persist_and_convert_images_to_path_dicts(run_info.inputs)
# Persist and convert images in output to path dictionaries.
# This replaces any image objects with their corresponding file path dictionaries.
if run_info.output:
serialized_output = self._persist_and_convert_images_to_path_dicts(run_info.output)
run_info.output = serialized_output
run_info.result = serialized_output
# Persist and convert images in api_calls to path dictionaries.
# The `inplace=True` parameter is used here to ensure that the original list structure holding generator outputs
# is maintained. This allows us to keep tracking the list as it dynamically changes when the generator is
# consumed. It is crucial to process the api_calls list in place to avoid losing the reference to the list that
# holds the generator items, which is essential for tracing generator execution.
if run_info.api_calls:
run_info.api_calls = self._persist_and_convert_images_to_path_dicts(run_info.api_calls, inplace=True)
def persist_node_run(self, run_info: NodeRunInfo):
"""Persist the multimedia data in node run info after the node is executed.
This method now delegates to the shared persist_run_info method.
:param run_info: The run info of the node.
:type run_info: NodeRunInfo
"""
self.persist_run_info(run_info)
def persist_flow_run(self, run_info: FlowRunInfo):
"""Persist the multimedia data in flow run info after one line data is executed for the flow.
This method now delegates to the shared persist_run_info method.
:param run_info: The run info of the flow.
:type run_info: FlowRunInfo
"""
self.persist_run_info(run_info)
def _persist_and_convert_images_to_path_dicts(self, value, inplace=False):
"""Persist image objects within a Python object to disk and convert them to path dictionaries.
This function recursively processes a given Python object, which can be a list, a dictionary, or a nested
combination of these, searching for image objects. Each image object encountered is serialized and saved to
disk in a pre-defined location using the `_base_dir` and `_sub_dir` attributes. The image object within the
original data structure is then replaced with a dictionary that indicates the file path of the serialized
image, following the format: `{'data:image/<ext>;path': '.promptflow/intermediate/<image_uuid>.<ext>'}`.
The operation can be performed in-place on the original object or on a new copy, depending on the value of
the `inplace` parameter. When `inplace` is set to `True`, the original object is modified; when set to `False`,
a new object with the converted path dictionaries is returned.
:param value: The Python object to be processed, potentially containing image objects.
:type value: Any
:param inplace: Whether to modify the original object in place (True) or to create a new object with converted
path dictionaries (False).
:type inplace: bool
:return: The original object with converted path dictionaries if `inplace` is True, otherwise a new object with
the conversions.
:rtype: Any
"""
if self._base_dir:
pfbytes_file_reference_encoder = get_file_reference_encoder(
folder_path=self._base_dir,
relative_path=self._sub_dir,
)
else:
pfbytes_file_reference_encoder = None
serialization_funcs = {Image: partial(Image.serialize, **{"encoder": pfbytes_file_reference_encoder})}
return _process_recursively(value, process_funcs=serialization_funcs, inplace=inplace)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/storage/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from ._cache_storage import AbstractCacheStorage # noqa: F401
from ._run_storage import AbstractRunStorage # noqa: F401
__all__ = ["AbstractCacheStorage", "AbstractRunStorage"]
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_internal/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
# flake8: noqa
"""Put some imports here for internal packages to minimize the effort of refactoring."""
from promptflow._constants import PROMPTFLOW_CONNECTIONS
from promptflow._core._errors import GenerateMetaUserError, PackageToolNotFoundError, ToolExecutionError
from promptflow._core.cache_manager import AbstractCacheManager, CacheManager, enable_cache
from promptflow._core.connection_manager import ConnectionManager
from promptflow._core.flow_execution_context import FlowExecutionContext
from promptflow._core.log_manager import NodeLogManager, NodeLogWriter
from promptflow._core.metric_logger import add_metric_logger
from promptflow._core.openai_injector import inject_openai_api
from promptflow._core.operation_context import OperationContext
from promptflow._core.run_tracker import RunRecordNotFound, RunTracker
from promptflow._core.tool import ToolInvoker, ToolProvider, tool
from promptflow._core.tool_meta_generator import (
JinjaParsingError,
MultipleToolsDefined,
NoToolDefined,
PythonParsingError,
ReservedVariableCannotBeUsed,
generate_prompt_meta,
generate_python_meta,
generate_tool_meta_dict_by_file,
is_tool,
)
from promptflow._core.tools_manager import (
BuiltinsManager,
CustomPythonToolLoadError,
EmptyCodeInCustomTool,
MissingTargetFunction,
ToolsManager,
builtins,
collect_package_tools,
gen_dynamic_list,
register_apis,
register_builtins,
register_connections,
retrieve_tool_func_result,
)
from promptflow._core.tracer import Tracer
from promptflow._sdk._constants import LOCAL_MGMT_DB_PATH
from promptflow._sdk._serving.response_creator import ResponseCreator
from promptflow._sdk._serving.swagger import generate_swagger
from promptflow._sdk._serving.utils import (
get_output_fields_to_remove,
get_sample_json,
handle_error_to_response,
load_request_data,
streaming_response_required,
validate_request_data,
)
from promptflow._sdk._utils import (
get_used_connection_names_from_environment_variables,
setup_user_agent_to_operation_context,
update_environment_variables_with_connections,
)
from promptflow._utils.context_utils import _change_working_dir, inject_sys_path
from promptflow._utils.credential_scrubber import CredentialScrubber
from promptflow._utils.dataclass_serializer import deserialize_dataclass, serialize
from promptflow._utils.exception_utils import (
ErrorResponse,
ExceptionPresenter,
JsonSerializedPromptflowException,
RootErrorCode,
infer_error_code_from_class,
)
from promptflow._utils.execution_utils import handle_line_failures
from promptflow._utils.feature_utils import Feature, FeatureState, get_feature_list
from promptflow._utils.logger_utils import (
DATETIME_FORMAT,
LOG_FORMAT,
CredentialScrubberFormatter,
FileHandler,
FileHandlerConcurrentWrapper,
LogContext,
bulk_logger,
flow_logger,
get_logger,
logger,
update_log_path,
)
from promptflow._utils.multimedia_data_converter import (
AbstractMultimediaInfoConverter,
MultimediaConverter,
MultimediaInfo,
ResourceType,
)
from promptflow._utils.multimedia_utils import (
_create_image_from_file,
convert_multimedia_data_to_base64,
is_multimedia_dict,
persist_multimedia_data,
resolve_multimedia_data_recursively,
)
from promptflow._utils.utils import (
AttrDict,
camel_to_snake,
count_and_log_progress,
load_json,
reverse_transpose,
set_context,
transpose,
)
from promptflow._version import VERSION
from promptflow.batch._batch_inputs_processor import apply_inputs_mapping
from promptflow.executor._errors import InputNotFound
from promptflow.executor._tool_invoker import DefaultToolInvoker
from promptflow.storage._run_storage import DefaultRunStorage
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/_errors.py | from traceback import TracebackException
from promptflow._utils.exception_utils import (
ADDITIONAL_INFO_USER_EXECUTION_ERROR,
is_pf_core_frame,
last_frame_info,
remove_suffix,
)
from promptflow.exceptions import ErrorTarget, SystemErrorException, UserErrorException, ValidationException
class UnexpectedError(SystemErrorException):
"""Exception raised for unexpected errors that should not occur under normal circumstances."""
pass
class NotSupported(UserErrorException):
"""This exception should be raised when a feature is not supported by the package or product.
Customers should take action, such as upgrading the package or using the product in the correct way, to resolve it.
"""
pass
class PackageToolNotFoundError(ValidationException):
"""Exception raised when package tool is not found in the current runtime environment."""
pass
class MissingRequiredInputs(ValidationException):
pass
class InputTypeMismatch(ValidationException):
pass
class ToolCanceledError(UserErrorException):
"""Exception raised when tool execution is canceled."""
pass
class InvalidSource(ValidationException):
pass
class ToolLoadError(UserErrorException):
"""Exception raised when tool load failed."""
def __init__(self, module: str = None, **kwargs):
super().__init__(target=ErrorTarget.TOOL, module=module, **kwargs)
class ToolExecutionError(UserErrorException):
"""Exception raised when tool execution failed."""
def __init__(self, *, node_name: str, module: str = None):
self._node_name = node_name
super().__init__(target=ErrorTarget.TOOL, module=module)
@property
def message(self):
if self.inner_exception:
error_type_and_message = f"({self.inner_exception.__class__.__name__}) {self.inner_exception}"
return remove_suffix(self._message, ".") + f": {error_type_and_message}"
else:
return self._message
@property
def message_format(self):
return "Execution failure in '{node_name}'."
@property
def message_parameters(self):
return {"node_name": self._node_name}
@property
def tool_last_frame_info(self):
"""Return the line number inside the tool where the error occurred."""
return last_frame_info(self.inner_exception)
@property
def tool_traceback(self):
"""Return the traceback inside the tool's source code scope.
The traceback inside the promptflow's internal code will be taken off.
"""
exc = self.inner_exception
if exc and exc.__traceback__ is not None:
tb = exc.__traceback__.tb_next
if tb is not None:
# The first frames are always our code invoking the tool.
# We do not want to dump it to user code's traceback.
# So, skip these frames from pf core module.
while is_pf_core_frame(tb.tb_frame) and tb.tb_next is not None:
tb = tb.tb_next
# We don't use traceback.format_exception since its interface differs between 3.8 and 3.10.
# Use this internal class to adapt to different python versions.
te = TracebackException(type(exc), exc, tb)
formatted_tb = "".join(te.format())
return formatted_tb
return None
@property
def additional_info(self):
"""Set the tool exception details as additional info."""
if not self.inner_exception:
# Only populate additional info when inner exception is present.
return None
info = {
"type": self.inner_exception.__class__.__name__,
"message": str(self.inner_exception),
"traceback": self.tool_traceback,
}
info.update(self.tool_last_frame_info)
return {
ADDITIONAL_INFO_USER_EXECUTION_ERROR: info,
}
class GenerateMetaUserError(UserErrorException):
"""Base exception raised when failed to validate tool."""
def __init__(self, **kwargs):
super().__init__(target=ErrorTarget.EXECUTOR, **kwargs)
class MetaFileNotFound(GenerateMetaUserError):
pass
class MetaFileReadError(GenerateMetaUserError):
pass
class RunRecordNotFound(SystemErrorException):
pass
class FlowOutputUnserializable(UserErrorException):
pass
class ProcessPoolError(SystemErrorException):
pass
class DuplicateToolMappingError(ValidationException):
"""Exception raised when multiple tools are linked to the same deprecated tool id."""
pass
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/operation_context.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from contextvars import ContextVar
from typing import Dict, Mapping
from promptflow._version import VERSION
class OperationContext(Dict):
"""The OperationContext class.
This class is used to store the context information for the current operation. It is a dictionary-like class
that can be used to store any primitive context information. The object is a context variable that can be
accessed from anywhere in the current context. The context information is used to provide additional information
to the service for logging and telemetry purposes.
"""
_CONTEXT_KEY = "operation_context"
_current_context = ContextVar(_CONTEXT_KEY, default=None)
USER_AGENT_KEY = "user_agent"
@classmethod
def get_instance(cls):
"""Get the OperationContext instance.
This method returns the OperationContext instance from the current context.
If there is no instance in the current context, it creates a new one and sets it in the current context.
Returns:
OperationContext: The OperationContext instance.
"""
# get the OperationContext instance from the current context
instance = cls._current_context.get()
if instance is None:
# create a new instance and set it in the current context
instance = OperationContext()
cls._current_context.set(instance)
return instance
def __setattr__(self, name, value):
"""Set the attribute.
This method sets an attribute with the given name and value in the OperationContext instance.
The name must be a string and the value must be a primitive.
Args:
name (str): The name of the attribute.
value (int, float, str, bool, or None): The value of the attribute.
Raises:
TypeError: If name is not a string or value is not a primitive.
"""
# check that name is a string
if not isinstance(name, str):
raise TypeError("Name must be a string")
# check that value is a primitive
if value is not None and not isinstance(value, (int, float, str, bool)):
raise TypeError("Value must be a primitive")
# set the item in the data attribute
self[name] = value
def __getattr__(self, name):
"""Get the attribute.
This method returns the attribute with the given name from the OperationContext instance.
If there is no such attribute, it returns the default attribute from the super class.
Args:
name (str): The name of the attribute.
Returns:
int, float, str, bool, or None: The value of the attribute.
"""
if name in self:
return self[name]
else:
super().__getattribute__(name)
def __delattr__(self, name):
"""Delete the attribute.
This method deletes the attribute with the given name from the OperationContext instance.
If there is no such attribute, it deletes the default attribute from the super class.
Args:
name (str): The name of the attribute.
"""
if name in self:
del self[name]
else:
super().__delattr__(name)
def get_user_agent(self):
"""Get the user agent string.
This method returns the user agent string for the OperationContext instance.
The user agent string consists of the promptflow-sdk version and any additional user agent information stored in
the user_agent attribute.
Returns:
str: The user agent string.
"""
def parts():
if OperationContext.USER_AGENT_KEY in self:
yield self.get(OperationContext.USER_AGENT_KEY)
yield f"promptflow/{VERSION}"
# strip to avoid leading or trailing spaces, which may cause error when sending request
ua = " ".join(parts()).strip()
return ua
def append_user_agent(self, user_agent: str):
"""Append the user agent string.
This method appends user agent information to the user_agent attribute of the OperationContext instance.
If there is no user_agent attribute, it creates one with the given user agent information.
Args:
user_agent (str): The user agent information to append.
"""
if OperationContext.USER_AGENT_KEY in self:
if user_agent not in self.user_agent:
self.user_agent = f"{self.user_agent.strip()} {user_agent.strip()}"
else:
self.user_agent = user_agent
def set_batch_input_source_from_inputs_mapping(self, inputs_mapping: Mapping[str, str]):
"""Infer the batch input source from the input mapping and set it in the OperationContext instance.
This method analyzes the `inputs_mapping` to ascertain the origin of the inputs for a batch operation.
The `inputs_mapping` should be a dictionary with keys representing input names and values specifying the sources
of these inputs. Inputs can originate from direct data or from the outputs of a previous run.
The `inputs_mapping` is dictated entirely by the external caller. For more details on column mapping, refer to
https://aka.ms/pf/column-mapping. The mapping can include references to both the inputs and outputs of previous
runs, using a reserved source name 'run' to indicate such references. However, this method specifically checks
for references to outputs of previous runs, which are denoted by values starting with "${run.outputs". When such
a reference is found, the `batch_input_source` attribute of the OperationContext instance is set to "Run" to
reflect that the batch operation is utilizing outputs from a prior run.
If no values in the `inputs_mapping` start with "${run.outputs", it is inferred that the inputs do not derive
from a previous run, and the `batch_input_source` is set to "Data".
Examples of `inputs_mapping`:
- Referencing a previous run's output:
{'input1': '${run.outputs.some_output}', 'input2': 'direct_data'}
In this case, 'input1' is sourced from a prior run's output, and 'input2' is from direct data.
The `batch_input_source` would be set to "Run".
- Sourcing directly from data:
{'input1': 'data_source1', 'input2': 'data_source2'}
Since no values start with "${run.outputs", the `batch_input_source` is set to "Data".
Args:
inputs_mapping (Mapping[str, str]): A dictionary mapping input names to their sources, where the sources
can be either direct data or outputs from a previous run. The structure and content of this mapping are
entirely under the control of the external caller.
Returns:
None
"""
if inputs_mapping and any(
isinstance(value, str) and value.startswith("${run.outputs") for value in inputs_mapping.values()
):
self.batch_input_source = "Run"
else:
self.batch_input_source = "Data"
def get_context_dict(self):
"""Get the context dictionary.
This method returns the context dictionary for the OperationContext instance.
The context dictionary is a dictionary that contains all the context information stored in the OperationContext
instance.
Returns:
dict: The context dictionary.
"""
return dict(self)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/token_provider.py | import threading
from abc import ABC, abstractmethod
from promptflow.exceptions import UserErrorException
# to access azure ai services, we need to get the token with this audience
COGNITIVE_AUDIENCE = "https://cognitiveservices.azure.com/"
class TokenProviderABC(ABC):
def __init__(self) -> None:
super().__init__()
@abstractmethod
def get_token(self) -> str:
pass
class StaticTokenProvider(TokenProviderABC):
def __init__(self, token: str) -> None:
super().__init__()
self.token = token
def get_token(self) -> str:
return self.token
class AzureTokenProvider(TokenProviderABC):
_instance_lock = threading.Lock()
_instance = None
def __new__(cls, *args, **kwargs):
with cls._instance_lock:
if not cls._instance:
cls._instance = super().__new__(cls)
cls._instance._init_instance()
return cls._instance
def _init_instance(self):
try:
# Initialize a credential instance
from azure.identity import DefaultAzureCredential
self.credential = DefaultAzureCredential()
except ImportError as ex:
raise UserErrorException(
"Failed to initialize AzureTokenProvider. " +
f"Please try 'pip install azure.identity' to install dependency, {ex.msg}."
)
def get_token(self):
audience = COGNITIVE_AUDIENCE
return self.credential.get_token(audience).token
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/log_manager.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import sys
from contextvars import ContextVar
from datetime import datetime, timezone
from io import StringIO, TextIOBase
from typing import Dict
from promptflow._utils.logger_utils import flow_logger, logger, scrub_credentials
class NodeInfo:
def __init__(self, run_id: str, node_name: str, line_number: int):
self.run_id = run_id
self.node_name = node_name
self.line_number = line_number
def __str__(self) -> str:
return f"{self.node_name} in line {self.line_number} (index starts from 0)"
class NodeLogManager:
"""Replace sys.stdout and sys.stderr with NodeLogWriter.
This class intercepts and saves logs to stdout/stderr when executing a node. For example:
with NodeLogManager() as log_manager:
print('test stdout')
print('test stderr', file=sys.stderr)
log_manager.get_logs() will return: {'stdout': 'test stdout\n', 'stderr': 'test stderr\n'}
"""
def __init__(self, record_datetime=True):
self.stdout_logger = NodeLogWriter(sys.stdout, record_datetime)
self.stderr_logger = NodeLogWriter(sys.stderr, record_datetime, is_stderr=True)
self.log_handler = None
def __enter__(self):
"""Replace sys.stdout and sys.stderr with NodeLogWriter."""
self._prev_stdout = sys.stdout
self._prev_stderr = sys.stderr
sys.stdout = self.stdout_logger
sys.stderr = self.stderr_logger
return self
def __exit__(self, *args):
"""Restore sys.stdout and sys.stderr."""
sys.stdout = self._prev_stdout
sys.stderr = self._prev_stderr
def set_node_context(self, run_id: str, node_name: str, line_number: int):
"""Set node context."""
self.stdout_logger.set_node_info(run_id, node_name, line_number)
self.stderr_logger.set_node_info(run_id, node_name, line_number)
def clear_node_context(self, run_id):
"""Clear node context."""
self.stdout_logger.clear_node_info(run_id)
self.stderr_logger.clear_node_info(run_id)
def get_logs(self, run_id) -> Dict[str, str]:
return {
"stdout": self.stdout_logger.get_log(run_id),
"stderr": self.stderr_logger.get_log(run_id),
}
class NodeLogWriter(TextIOBase):
"""Record node run logs."""
DATETIME_FORMAT = "%Y-%m-%dT%H:%M:%S%z"
def __init__(self, prev_stdout, record_datetime=True, is_stderr=False):
self.run_id_to_stdout = dict()
self._context = ContextVar("run_log_info", default=None)
self._prev_out = prev_stdout
self._record_datetime = record_datetime
self._is_stderr = is_stderr
def set_node_info(self, run_id: str, node_name: str, line_number: int = None):
"""Set node info to a context variable.
After set node info, write method will write to stringio associated with this node.
"""
run_log_info = NodeInfo(run_id, node_name, line_number)
self._context.set(run_log_info)
self.run_id_to_stdout.update({run_id: StringIO()})
def clear_node_info(self, run_id: str):
"""Clear context variable associated with run id."""
log_info: NodeInfo = self._context.get()
if log_info and log_info.run_id == run_id:
self._context.set(None)
if run_id in self.run_id_to_stdout:
self.run_id_to_stdout.pop(run_id)
def get_log(self, run_id: str) -> str:
"""Get log associated with run id."""
string_io: StringIO = self.run_id_to_stdout.get(run_id)
if string_io is None:
return None
return string_io.getvalue()
def write(self, s: str):
"""Override TextIO's write method and writes input string into a stringio
The written string is compliant without any credentials.
The string is also recorded to flow/bulk logger.
If node info is not set, write to previous stdout.
"""
log_info: NodeInfo = self._context.get()
s = scrub_credentials(s) # Remove credential from string.
if log_info is None:
self._prev_out.write(s)
else:
self._write_to_flow_log(log_info, s)
stdout: StringIO = self.run_id_to_stdout.get(log_info.run_id)
if self._record_datetime and s != "\n": # For line breaker, do not add datetime prefix.
s = f"[{datetime.now(timezone.utc).strftime(self.DATETIME_FORMAT)}] {s}"
stdout.write(s)
def flush(self):
"""Override TextIO's flush method."""
node_info: NodeInfo = self._context.get()
if node_info is None:
self._prev_out.flush()
else:
string_io = self.run_id_to_stdout.get(node_info.run_id)
if string_io is not None:
string_io.flush()
def _write_to_flow_log(self, log_info: NodeInfo, s: str):
"""Save stdout log to flow_logger and stderr log to logger."""
# If user uses "print('log message.')" to log, then
# "write" method will be called twice and the second time input is only '\n'.
# For this case, should not log '\n' in flow_logger.
if s != "\n":
if self._is_stderr:
flow_log = f"[{str(log_info)}] stderr> " + s.rstrip("\n")
# Log stderr in all scenarios so we can diagnose problems.
logger.warning(flow_log)
else:
flow_log = f"[{str(log_info)}] stdout> " + s.rstrip("\n")
# Log stdout only in flow mode.
flow_logger.info(flow_log)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/run_tracker.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import asyncio
import json
from contextvars import ContextVar
from datetime import datetime, timezone
from types import GeneratorType
from typing import Any, Dict, List, Mapping, Optional, Union
from promptflow._core._errors import FlowOutputUnserializable, RunRecordNotFound, ToolCanceledError
from promptflow._core.log_manager import NodeLogManager
from promptflow._core.thread_local_singleton import ThreadLocalSingleton
from promptflow._utils.dataclass_serializer import serialize
from promptflow._utils.exception_utils import ExceptionPresenter
from promptflow._utils.logger_utils import flow_logger
from promptflow._utils.multimedia_utils import default_json_encoder
from promptflow._utils.openai_metrics_calculator import OpenAIMetricsCalculator
from promptflow.contracts.run_info import FlowRunInfo, RunInfo, Status
from promptflow.contracts.run_mode import RunMode
from promptflow.contracts.tool import ConnectionType
from promptflow.exceptions import ErrorTarget
from promptflow.storage import AbstractRunStorage
from promptflow.storage._run_storage import DummyRunStorage
class RunTracker(ThreadLocalSingleton):
RUN_CONTEXT_NAME = "CurrentRun"
CONTEXT_VAR_NAME = "RunTracker"
context_var = ContextVar(CONTEXT_VAR_NAME, default=None)
@staticmethod
def init_dummy() -> "RunTracker":
return RunTracker(DummyRunStorage())
def __init__(self, run_storage: AbstractRunStorage, run_mode: RunMode = RunMode.Test, node_log_manager=None):
self._node_runs: Dict[str, RunInfo] = {}
self._flow_runs: Dict[str, FlowRunInfo] = {}
self._current_run_id = ""
self._run_context = ContextVar(self.RUN_CONTEXT_NAME, default="")
self._storage = run_storage
self._debug = True # TODO: Make this configurable
self.node_log_manager = node_log_manager or NodeLogManager()
self._has_failed_root_run = False
self._run_mode = run_mode
self._allow_generator_types = False
@property
def allow_generator_types(self):
return self._allow_generator_types
@allow_generator_types.setter
def allow_generator_types(self, value: bool):
self._allow_generator_types = value
@property
def node_run_list(self):
# Add list() to make node_run_list a new list object,
# therefore avoid iterating over a dictionary, which might be updated by another thread.
return list(self._node_runs.values())
@property
def flow_run_list(self):
# Add list() to make flow_run_list a new list object,
# therefore avoid iterating over a dictionary, which might be updated by another thread.
return list(self._flow_runs.values())
def set_current_run_in_context(self, run_id: str):
self._run_context.set(run_id)
def get_current_run_in_context(self) -> str:
return self._run_context.get()
def start_flow_run(
self,
flow_id,
root_run_id,
run_id,
parent_run_id="",
inputs=None,
index=None,
variant_id="",
) -> FlowRunInfo:
"""Create a flow run and save to run storage on demand."""
run_info = FlowRunInfo(
run_id=run_id,
status=Status.Running,
error=None,
inputs=inputs,
output=None,
metrics=None,
request=None,
parent_run_id=parent_run_id,
root_run_id=root_run_id,
source_run_id=None,
flow_id=flow_id,
start_time=datetime.utcnow(),
end_time=None,
index=index,
variant_id=variant_id,
)
self.persist_flow_run(run_info)
self._flow_runs[run_id] = run_info
self._current_run_id = run_id
return run_info
def start_node_run(
self,
node,
flow_run_id,
parent_run_id,
run_id,
index,
):
run_info = RunInfo(
node=node,
run_id=run_id,
flow_run_id=flow_run_id,
status=Status.Running,
inputs=None,
output=None,
metrics=None,
error=None,
parent_run_id=parent_run_id,
start_time=datetime.utcnow(),
end_time=None,
)
self._node_runs[run_id] = run_info
self._current_run_id = run_id
self.set_current_run_in_context(run_id)
self.node_log_manager.set_node_context(run_id, node, index)
return run_info
def bypass_node_run(
self,
node,
flow_run_id,
parent_run_id,
run_id,
index,
variant_id,
):
run_info = RunInfo(
node=node,
run_id=run_id,
flow_run_id=flow_run_id,
parent_run_id=parent_run_id,
status=Status.Bypassed,
inputs=None,
output=None,
metrics=None,
error=None,
start_time=datetime.utcnow(),
end_time=datetime.utcnow(),
result=None,
index=index,
variant_id=variant_id,
api_calls=[],
)
self._node_runs[run_id] = run_info
return run_info
def _flow_run_postprocess(self, run_info: FlowRunInfo, output, ex: Optional[Exception]):
if output:
try:
self._assert_flow_output_serializable(output)
except Exception as e:
output, ex = None, e
self._common_postprocess(run_info, output, ex)
def _update_flow_run_info_with_node_runs(self, run_info: FlowRunInfo):
run_id = run_info.run_id
child_run_infos = self.collect_child_node_runs(run_id)
run_info.system_metrics = run_info.system_metrics or {}
run_info.system_metrics.update(self.collect_metrics(child_run_infos, self.OPENAI_AGGREGATE_METRICS))
# TODO: Refactor Tracer to support flow level tracing,
# then we can remove the hard-coded root level api_calls here.
# It has to be a list for UI backward compatibility.
# TODO: Add input, output, error to top level. Adding them would require
# the same technique of handingling image and generator in Tracer,
# which introduces duplicated logic. We should do it in the refactoring.
start_timestamp = run_info.start_time.astimezone(timezone.utc).timestamp() if run_info.start_time else None
end_timestamp = run_info.end_time.astimezone(timezone.utc).timestamp() if run_info.end_time else None
run_info.api_calls = [
{
"name": "flow",
"node_name": "flow",
"type": "Flow",
"start_time": start_timestamp,
"end_time": end_timestamp,
"children": self._collect_traces_from_nodes(run_id),
"system_metrics": run_info.system_metrics,
}
]
def _node_run_postprocess(self, run_info: RunInfo, output, ex: Optional[Exception]):
run_id = run_info.run_id
self.set_openai_metrics(run_id)
logs = self.node_log_manager.get_logs(run_id)
run_info.logs = logs
self.node_log_manager.clear_node_context(run_id)
if run_info.inputs:
run_info.inputs = self._ensure_inputs_is_json_serializable(run_info.inputs, run_info.node)
if output is not None:
msg = f"Output of {run_info.node} is not json serializable, use str to store it."
output = self._ensure_serializable_value(output, msg)
self._common_postprocess(run_info, output, ex)
def _common_postprocess(self, run_info, output, ex):
if output is not None:
# Duplicated fields for backward compatibility.
run_info.result = output
run_info.output = output
if ex is not None:
self._enrich_run_info_with_exception(run_info=run_info, ex=ex)
else:
run_info.status = Status.Completed
run_info.end_time = datetime.utcnow()
if not isinstance(run_info.start_time, datetime):
flow_logger.warning(
f"Run start time {run_info.start_time} for {run_info.run_id} is not a datetime object, "
f"got {run_info.start_time}, type={type(run_info.start_time)}."
)
else:
duration = (run_info.end_time - run_info.start_time).total_seconds()
run_info.system_metrics = run_info.system_metrics or {}
run_info.system_metrics["duration"] = duration
def cancel_node_runs(self, msg: str, flow_run_id):
node_runs = self.collect_node_runs(flow_run_id)
for node_run_info in node_runs:
if node_run_info.status != Status.Running:
continue
msg = msg.rstrip(".") # Avoid duplicated "." in the end of the message.
err = ToolCanceledError(
message_format="Tool execution is canceled because of the error: {msg}.",
msg=msg,
target=ErrorTarget.EXECUTOR,
)
self.end_run(node_run_info.run_id, ex=err)
node_run_info.status = Status.Canceled
self.persist_node_run(node_run_info)
def end_run(
self,
run_id: str,
*,
result: Optional[dict] = None,
ex: Optional[Exception] = None,
traces: Optional[List] = None,
):
run_info = self._flow_runs.get(run_id) or self._node_runs.get(run_id)
if run_info is None:
raise RunRecordNotFound(
message_format=(
"Run record with ID '{run_id}' was not tracked in promptflow execution. "
"Please contact support for further assistance."
),
target=ErrorTarget.RUN_TRACKER,
run_id=run_id,
)
# If the run is already canceled, do nothing.
if run_info.status == Status.Canceled:
return run_info
if isinstance(run_info, FlowRunInfo):
self._flow_run_postprocess(run_info, result, ex)
if traces:
run_info.api_calls = traces
elif isinstance(run_info, RunInfo):
run_info.api_calls = traces
self._node_run_postprocess(run_info, result, ex)
return run_info
def _ensure_serializable_value(self, val, warning_msg: Optional[str] = None):
if ConnectionType.is_connection_value(val):
return ConnectionType.serialize_conn(val)
if self.allow_generator_types and isinstance(val, GeneratorType):
return str(val)
try:
json.dumps(val, default=default_json_encoder)
return val
except Exception:
if not warning_msg:
raise
flow_logger.warning(warning_msg)
return repr(val)
def _ensure_inputs_is_json_serializable(self, inputs: dict, node_name: str) -> dict:
return {
k: self._ensure_serializable_value(
v, f"Input '{k}' of {node_name} is not json serializable, use str to store it."
)
for k, v in inputs.items()
}
def _assert_flow_output_serializable(self, output: Any) -> Any:
def _wrap_serializable_error(value):
try:
return self._ensure_serializable_value(value)
except Exception as e:
# If a specific key-value pair is not serializable, raise an exception with the key.
error_type_and_message = f"({e.__class__.__name__}) {e}"
message_format = (
"The output '{output_name}' for flow is incorrect. The output value is not JSON serializable. "
"JSON dump failed: {error_type_and_message}. Please verify your flow output and "
"make sure the value serializable."
)
raise FlowOutputUnserializable(
message_format=message_format,
target=ErrorTarget.FLOW_EXECUTOR,
output_name=k,
error_type_and_message=error_type_and_message,
) from e
# support primitive outputs in eager mode
if not isinstance(output, dict):
return _wrap_serializable_error(output)
serializable_output = {}
for k, v in output.items():
serializable_output[k] = _wrap_serializable_error(v)
return serializable_output
def _enrich_run_info_with_exception(self, run_info: Union[RunInfo, FlowRunInfo], ex: Exception):
"""Update exception details into run info."""
# Update status to Cancelled the run terminates because of KeyboardInterruption or CancelledError.
if isinstance(ex, KeyboardInterrupt) or isinstance(ex, asyncio.CancelledError):
run_info.status = Status.Canceled
else:
run_info.error = ExceptionPresenter.create(ex).to_dict(include_debug_info=self._debug)
run_info.status = Status.Failed
def collect_all_run_infos_as_dicts(self) -> Mapping[str, List[Mapping[str, Any]]]:
flow_runs = self.flow_run_list
node_runs = self.node_run_list
return {
"flow_runs": [serialize(run) for run in flow_runs],
"node_runs": [serialize(run) for run in node_runs],
}
def collect_node_runs(self, flow_run_id: Optional[str] = None) -> List[RunInfo]:
"""If flow_run_id is None, return all node runs."""
if flow_run_id:
return [run_info for run_info in self.node_run_list if run_info.flow_run_id == flow_run_id]
return [run_info for run_info in self.node_run_list]
def collect_child_node_runs(self, parent_run_id: str) -> List[RunInfo]:
return [run_info for run_info in self.node_run_list if run_info.parent_run_id == parent_run_id]
def ensure_run_info(self, run_id: str) -> Union[RunInfo, FlowRunInfo]:
run_info = self._node_runs.get(run_id) or self._flow_runs.get(run_id)
if run_info is None:
raise RunRecordNotFound(
message_format=(
"Run record with ID '{run_id}' was not tracked in promptflow execution. "
"Please contact support for further assistance."
),
target=ErrorTarget.RUN_TRACKER,
run_id=run_id,
)
return run_info
def set_inputs(self, run_id: str, inputs: Mapping[str, Any]):
run_info = self.ensure_run_info(run_id)
run_info.inputs = inputs
def set_openai_metrics(self, run_id: str):
# TODO: Provide a common implementation for different internal metrics
run_info = self.ensure_run_info(run_id)
calls = run_info.api_calls or []
total_metrics = {}
calculator = OpenAIMetricsCalculator(flow_logger)
for call in calls:
metrics = calculator.get_openai_metrics_from_api_call(call)
calculator.merge_metrics_dict(total_metrics, metrics)
run_info.system_metrics = run_info.system_metrics or {}
run_info.system_metrics.update(total_metrics)
def _collect_traces_from_nodes(self, run_id):
child_run_infos = self.collect_child_node_runs(run_id)
traces = []
for node_run_info in child_run_infos:
traces.extend(node_run_info.api_calls or [])
return traces
OPENAI_AGGREGATE_METRICS = ["prompt_tokens", "completion_tokens", "total_tokens"]
def collect_metrics(self, run_infos: List[RunInfo], aggregate_metrics: List[str] = []):
if not aggregate_metrics:
return {}
total_metrics = {}
for run_info in run_infos:
if not run_info.system_metrics:
continue
for metric in aggregate_metrics:
total_metrics[metric] = total_metrics.get(metric, 0) + run_info.system_metrics.get(metric, 0)
return total_metrics
def get_run(self, run_id):
return self._node_runs.get(run_id) or self._flow_runs.get(run_id)
def persist_node_run(self, run_info: RunInfo):
self._storage.persist_node_run(run_info)
def persist_selected_node_runs(self, run_info: FlowRunInfo, node_names: List[str]):
"""
Persists the node runs for the specified node names.
:param run_info: The flow run information.
:type run_info: FlowRunInfo
:param node_names: The names of the nodes to persist.
:type node_names: List[str]
:returns: None
"""
run_id = run_info.run_id
selected_node_run_info = (
run_info for run_info in self.collect_child_node_runs(run_id) if run_info.node in node_names
)
for node_run_info in selected_node_run_info:
self.persist_node_run(node_run_info)
def persist_flow_run(self, run_info: FlowRunInfo):
self._storage.persist_flow_run(run_info)
def get_status_summary(self, run_id: str):
node_run_infos = self.collect_node_runs(run_id)
status_summary = {}
for run_info in node_run_infos:
node_name = run_info.node
if run_info.index is not None:
# Only consider Completed, Bypassed and Failed status, because the UX only support three status.
if run_info.status in (Status.Completed, Status.Bypassed, Status.Failed):
node_status_key = f"__pf__.nodes.{node_name}.{run_info.status.value.lower()}"
status_summary[node_status_key] = status_summary.setdefault(node_status_key, 0) + 1
# For reduce node, the index is None.
else:
status_summary[f"__pf__.nodes.{node_name}.completed"] = 1 if run_info.status == Status.Completed else 0
# Runtime will start root flow run with run_id == root_run_id,
# line flow run will have run id f"{root_run_id}_{line_number}"
# We filter out root flow run accordingly.
line_flow_run_infos = [
flow_run_info
for flow_run_info in self.flow_run_list
if flow_run_info.root_run_id == run_id and flow_run_info.run_id != run_id
]
total_lines = len(line_flow_run_infos)
completed_lines = len(
[flow_run_info for flow_run_info in line_flow_run_infos if flow_run_info.status == Status.Completed]
)
status_summary["__pf__.lines.completed"] = completed_lines
status_summary["__pf__.lines.failed"] = total_lines - completed_lines
return status_summary
def persist_status_summary(self, status_summary: Dict[str, int], run_id: str):
self._storage.persist_status_summary(status_summary, run_id)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/tools_manager.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import importlib
import importlib.util
import inspect
import logging
import traceback
import types
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Mapping, Optional, Tuple, Union
from promptflow._core._errors import (
InputTypeMismatch,
InvalidSource,
MissingRequiredInputs,
PackageToolNotFoundError,
ToolLoadError,
)
from promptflow._core.tool_meta_generator import (
_parse_tool_from_function,
collect_tool_function_in_module,
load_python_module_from_file,
)
from promptflow._utils.connection_utils import (
generate_custom_strong_type_connection_spec,
generate_custom_strong_type_connection_template,
)
from promptflow._utils.tool_utils import (
_DEPRECATED_TOOLS,
DynamicListError,
RetrieveToolFuncResultError,
_find_deprecated_tools,
append_workspace_triple_to_func_input_params,
function_to_tool_definition,
get_prompt_param_name_from_func,
load_function_from_function_path,
validate_dynamic_list_func_response_type,
validate_tool_func_result,
)
from promptflow._utils.yaml_utils import load_yaml
from promptflow.contracts.flow import InputAssignment, InputValueType, Node, ToolSourceType
from promptflow.contracts.tool import ConnectionType, Tool, ToolType
from promptflow.exceptions import ErrorTarget, SystemErrorException, UserErrorException, ValidationException
module_logger = logging.getLogger(__name__)
PACKAGE_TOOLS_ENTRY = "package_tools"
def collect_tools_from_directory(base_dir) -> dict:
tools = {}
for f in Path(base_dir).glob("**/*.yaml"):
with open(f, "r") as f:
tools_in_file = load_yaml(f)
for identifier, tool in tools_in_file.items():
tools[identifier] = tool
return tools
def _get_entry_points_by_group(group):
# lazy load to improve performance for scenarios that don't need to load package tools
import importlib.metadata
# In python3.10 and later, the entry_points() method returns a SelectableView of EntryPoint objects,
# which allows us to select entry points by group. In the previous versions, the entry_points() method
# returns a dictionary-like object, we can use group name directly as a key.
entry_points = importlib.metadata.entry_points()
if isinstance(entry_points, list):
return entry_points.select(group=group)
else:
return entry_points.get(group, [])
def collect_package_tools(keys: Optional[List[str]] = None) -> dict:
"""Collect all tools from all installed packages."""
all_package_tools = {}
if keys is not None:
keys = set(keys)
entry_points = _get_entry_points_by_group(PACKAGE_TOOLS_ENTRY)
for entry_point in entry_points:
try:
list_tool_func = entry_point.load()
package_tools = list_tool_func()
for identifier, tool in package_tools.items():
# Only load required tools to avoid unnecessary loading when keys is provided
if isinstance(keys, set) and identifier not in keys:
# Support to collect new tool id if node source tool is a deprecated tool.
deprecated_tool_ids = tool.get(_DEPRECATED_TOOLS, [])
if not set(deprecated_tool_ids).intersection(keys):
continue
m = tool["module"]
importlib.import_module(m) # Import the module to make sure it is valid
tool["package"] = entry_point.dist.metadata["Name"]
tool["package_version"] = entry_point.dist.version
all_package_tools[identifier] = tool
except Exception as e:
msg = (
f"Failed to load tools from package {entry_point.dist.metadata['Name']}: {e},"
+ f" traceback: {traceback.format_exc()}"
)
module_logger.warning(msg)
return all_package_tools
def collect_package_tools_and_connections(keys: Optional[List[str]] = None) -> dict:
"""Collect all tools and custom strong type connections from all installed packages."""
all_package_tools = {}
all_package_connection_specs = {}
all_package_connection_templates = {}
if keys is not None:
keys = set(keys)
entry_points = _get_entry_points_by_group(PACKAGE_TOOLS_ENTRY)
for entry_point in entry_points:
try:
list_tool_func = entry_point.load()
package_tools = list_tool_func()
for identifier, tool in package_tools.items():
# Only load required tools to avoid unnecessary loading when keys is provided
if isinstance(keys, set) and identifier not in keys:
continue
m = tool["module"]
module = importlib.import_module(m) # Import the module to make sure it is valid
tool["package"] = entry_point.dist.metadata["Name"]
tool["package_version"] = entry_point.dist.version
all_package_tools[identifier] = tool
# Get custom strong type connection definition
custom_strong_type_connections_classes = [
obj
for name, obj in inspect.getmembers(module)
if inspect.isclass(obj)
and ConnectionType.is_custom_strong_type(obj)
and (not ConnectionType.is_connection_class_name(name))
]
if custom_strong_type_connections_classes:
for cls in custom_strong_type_connections_classes:
identifier = f"{cls.__module__}.{cls.__name__}"
connection_spec = generate_custom_strong_type_connection_spec(
cls, entry_point.dist.metadata["Name"], entry_point.dist.version
)
all_package_connection_specs[identifier] = connection_spec
all_package_connection_templates[identifier] = generate_custom_strong_type_connection_template(
cls, connection_spec, entry_point.dist.metadata["Name"], entry_point.dist.version
)
except Exception as e:
msg = (
f"Failed to load tools from package {entry_point.dist.metadata['Name']}: {e},"
+ f" traceback: {traceback.format_exc()}"
)
module_logger.warning(msg)
return all_package_tools, all_package_connection_specs, all_package_connection_templates
def retrieve_tool_func_result(
func_call_scenario: str, func_path: str, func_input_params_dict: Dict, ws_triple_dict: Dict[str, str] = {}
):
func = load_function_from_function_path(func_path)
# get param names from func signature.
func_sig_params = inspect.signature(func).parameters
module_logger.warning(f"func_sig_params of func_path is: '{func_sig_params}'")
module_logger.warning(f"func_input_params_dict is: '{func_input_params_dict}'")
# Append workspace triple to func input params if func signature has kwargs param.
# Or append ws_triple_dict params that are in func signature.
combined_func_input_params = append_workspace_triple_to_func_input_params(
func_sig_params, func_input_params_dict, ws_triple_dict
)
try:
result = func(**combined_func_input_params)
except Exception as e:
raise RetrieveToolFuncResultError(f"Error when calling function {func_path}: {e}")
validate_tool_func_result(func_call_scenario, result)
return result
def gen_dynamic_list(func_path: str, func_input_params_dict: Dict, ws_triple_dict: Dict[str, str] = {}):
func = load_function_from_function_path(func_path)
# get param names from func signature.
func_sig_params = inspect.signature(func).parameters
module_logger.warning(f"func_sig_params of func_path is: '{func_sig_params}'")
module_logger.warning(f"func_input_params_dict is: '{func_input_params_dict}'")
combined_func_input_params = append_workspace_triple_to_func_input_params(
func_sig_params, func_input_params_dict, ws_triple_dict
)
try:
result = func(**combined_func_input_params)
except Exception as e:
raise DynamicListError(f"Error when calling function {func_path}: {e}")
# validate response is of required format. Throw correct message if response is empty.
validate_dynamic_list_func_response_type(result, func.__name__)
return result
class BuiltinsManager:
def __init__(self) -> None:
pass
@staticmethod
def _load_llm_api(api_name: str) -> Tool:
result = apis.get(api_name)
if result is None:
raise APINotFound(
message=f"The API '{api_name}' is not found.",
target=ErrorTarget.EXECUTOR,
)
return result
def load_builtin(
self,
tool: Tool,
node_inputs: Optional[dict] = None,
) -> Tuple[Callable, dict]:
return BuiltinsManager._load_package_tool(tool.name, tool.module, tool.class_name, tool.function, node_inputs)
@staticmethod
def _load_package_tool(tool_name, module_name, class_name, method_name, node_inputs):
module = importlib.import_module(module_name)
return BuiltinsManager._load_tool_from_module(
module, tool_name, module_name, class_name, method_name, node_inputs
)
@staticmethod
def _load_tool_from_module(
module, tool_name, module_name, class_name, method_name, node_inputs: Mapping[str, InputAssignment]
):
"""Load tool from given module with node inputs."""
if class_name is None:
return getattr(module, method_name), {}
provider_class = getattr(module, class_name)
# Note: v -- type is InputAssignment
init_inputs = provider_class.get_initialize_inputs()
init_inputs_values = {}
for k, v in node_inputs.items():
if k not in init_inputs:
continue
if v.value_type != InputValueType.LITERAL:
raise InputTypeMismatch(
message_format=(
"Invalid input for '{tool_name}': Initialization input '{input_name}' requires a literal "
"value, but {input_value} was received."
),
tool_name=tool_name,
input_name=k,
input_value=v.serialize(),
target=ErrorTarget.EXECUTOR,
)
init_inputs_values[k] = v.value
missing_inputs = set(provider_class.get_required_initialize_inputs()) - set(init_inputs_values)
if missing_inputs:
raise MissingRequiredInputs(
message=f"Required inputs {list(missing_inputs)} are not provided for tool '{tool_name}'.",
target=ErrorTarget.EXECUTOR,
)
try:
api = getattr(provider_class(**init_inputs_values), method_name)
except Exception as ex:
error_type_and_message = f"({ex.__class__.__name__}) {ex}"
raise ToolLoadError(
module=module_name,
message_format="Failed to load package tool '{tool_name}': {error_type_and_message}",
tool_name=tool_name,
error_type_and_message=error_type_and_message,
) from ex
# Return the init_inputs to update node inputs in the afterward steps
return api, init_inputs
@staticmethod
def load_tool_by_api_name(api_name: str) -> Tool:
if api_name is None:
return None
return BuiltinsManager._load_llm_api(api_name)
def load_prompt_with_api(self, tool: Tool, api: Tool, node_inputs: Optional[dict] = None) -> Tuple[Callable, dict]:
"""Load a prompt template tool with action."""
# Load provider action function
api_func, init_inputs = self.load_builtin(api, node_inputs)
# Find the prompt template parameter name and parse tool code to it.
prompt_tpl_param_name = get_prompt_param_name_from_func(api_func)
api_func = partial(api_func, **{prompt_tpl_param_name: tool.code}) if prompt_tpl_param_name else api_func
# Return the init_inputs to update node inputs in the afterward steps
return api_func, init_inputs
def load_prompt_rendering(self, tool: Tool):
if not tool.code:
tool.code = ""
from promptflow.tools.template_rendering import render_template_jinja2
return partial(render_template_jinja2, template=tool.code)
@staticmethod
def parse_builtin_tool_method(tool: Tool) -> tuple:
module_name = tool.module
class_name = tool.class_name
method_name = tool.function
return module_name, class_name, method_name
@staticmethod
def is_builtin(tool: Tool) -> bool:
"""Check if the tool is a builtin tool."""
return tool.type == ToolType.PYTHON and tool.code is None and tool.source is None
@staticmethod
def is_llm(tool: Tool) -> bool:
"""Check if the tool is a LLM tool."""
return tool.type == ToolType.LLM
@staticmethod
def is_custom_python(tool: Tool) -> bool:
"""Check if the tool is a custom python tool."""
return tool.type == ToolType.PYTHON and not BuiltinsManager.is_builtin(tool)
class ToolsManager:
"""Manage all builtins and user-defined tools."""
def __init__(
self,
loaded_tools: Optional[Mapping[str, Callable]] = None,
) -> None:
loaded_tools = loaded_tools or {}
self._tools = {k: v for k, v in loaded_tools.items()}
def load_tools(self, tools: Mapping[str, Callable]) -> None:
"""Load new tools to the manager."""
self._tools.update(tools)
def loaded(self, tool: str) -> bool:
return tool in self._tools
def get_tool(self, key: str) -> Callable:
if key not in self._tools:
raise ValueError(f"Tool for {key} is not loaded")
return self._tools[key]
def wrap_tool(self, key: str, wrapper: Callable):
"""Wraps the tool with specific name by a given wrapper.
Sometimes we may want to wrap the tool with a decorator, but we don't want to modify the original tool.
i.e. We may want to pass additional arguments to the tool by wrapping it with a decorator,
such as turning on the stream response for AzureOpenAI.chat() by adding a "stream=True" argument.
"""
tool = self.get_tool(key)
self._tools.update({key: wrapper(tool)})
def assert_loaded(self, tool: str):
if tool not in self._tools:
raise ValueError(f"Tool {tool} is not loaded")
# TODO: Remove this method. The code path will not be used in code-first experience.
# Customers are familiar with the term "node", so we use it in error message.
@staticmethod
def _load_custom_tool(tool: Tool, node_name: str) -> Callable:
func_name = tool.function or tool.name
if tool.source and Path(tool.source).exists(): # If source file is provided, load the function from the file
m = load_python_module_from_file(tool.source)
if m is None:
raise CustomToolSourceLoadError(f"Cannot load module from source {tool.source} for node {node_name}.")
return getattr(m, func_name)
if not tool.code:
raise EmptyCodeInCustomTool(f"Missing code in node {node_name}.")
func_code = tool.code
try:
f_globals = {}
exec(func_code, f_globals)
except Exception as e:
raise CustomPythonToolLoadError(f"Error when loading code of node {node_name}: {e}") from e
if func_name not in f_globals:
raise MissingTargetFunction(f"Cannot find function {func_name} in the code of node {node_name}.")
return f_globals[func_name]
class ToolLoader:
def __init__(self, working_dir: str, package_tool_keys: Optional[List[str]] = None) -> None:
self._working_dir = working_dir
self._package_tools = collect_package_tools(package_tool_keys) if package_tool_keys else {}
# Used to handle backward compatibility of tool ID changes.
self._deprecated_tools = _find_deprecated_tools(self._package_tools)
# TODO: Replace NotImplementedError with NotSupported in the future.
def load_tool_for_node(self, node: Node) -> Tool:
if node.source is None:
raise UserErrorException(f"Node {node.name} does not have source defined.")
if node.type == ToolType.PYTHON:
if node.source.type == ToolSourceType.Package:
return self.load_tool_for_package_node(node)
elif node.source.type == ToolSourceType.Code:
_, tool = self.load_tool_for_script_node(node)
return tool
raise NotImplementedError(f"Tool source type {node.source.type} for python tool is not supported yet.")
elif node.type == ToolType.CUSTOM_LLM:
if node.source.type == ToolSourceType.PackageWithPrompt:
return self.load_tool_for_package_node(node)
raise NotImplementedError(f"Tool source type {node.source.type} for custom_llm tool is not supported yet.")
else:
raise NotImplementedError(f"Tool type {node.type} is not supported yet.")
def load_tool_for_package_node(self, node: Node) -> Tool:
if node.source.tool in self._package_tools:
return Tool.deserialize(self._package_tools[node.source.tool])
# If node source tool is not in package tools, try to find the tool ID in deprecated tools.
# If found, load the tool with the new tool ID for backward compatibility.
if node.source.tool in self._deprecated_tools:
new_tool_id = self._deprecated_tools[node.source.tool]
# Used to collect deprecated tool usage and warn user to replace the deprecated tool with the new one.
module_logger.warning(f"Tool ID '{node.source.tool}' is deprecated. Please use '{new_tool_id}' instead.")
return Tool.deserialize(self._package_tools[new_tool_id])
raise PackageToolNotFoundError(
f"Package tool '{node.source.tool}' is not found in the current environment. "
f"All available package tools are: {list(self._package_tools.keys())}.",
target=ErrorTarget.EXECUTOR,
)
def load_tool_for_script_node(self, node: Node) -> Tuple[types.ModuleType, Tool]:
if node.source.path is None:
raise InvalidSource(
target=ErrorTarget.EXECUTOR,
message_format="Load tool failed for node '{node_name}'. The source path is 'None'.",
node_name=node.name,
)
path = node.source.path
if not (self._working_dir / path).is_file():
raise InvalidSource(
target=ErrorTarget.EXECUTOR,
message_format="Load tool failed for node '{node_name}'. Tool file '{source_path}' can not be found.",
source_path=path,
node_name=node.name,
)
m = load_python_module_from_file(self._working_dir / path)
if m is None:
raise CustomToolSourceLoadError(f"Cannot load module from {path}.")
f, init_inputs = collect_tool_function_in_module(m)
return m, _parse_tool_from_function(f, init_inputs, gen_custom_type_conn=True)
def load_tool_for_llm_node(self, node: Node) -> Tool:
api_name = f"{node.provider}.{node.api}"
return BuiltinsManager._load_llm_api(api_name)
builtins = {}
apis = {}
connections = {}
connection_type_to_api_mapping = {}
def _register(provider_cls, collection, type):
from promptflow._core.tool import ToolProvider
if not issubclass(provider_cls, ToolProvider):
raise Exception(f"Class {provider_cls.__name__!r} must be a subclass of promptflow.ToolProvider.")
initialize_inputs = provider_cls.get_initialize_inputs()
# Build tool/provider definition
for name, value in provider_cls.__dict__.items():
if hasattr(value, "__original_function"):
name = value.__original_function.__qualname__
value.__tool = function_to_tool_definition(value, type=type, initialize_inputs=initialize_inputs)
collection[name] = value.__tool
module_logger.debug(f"Registered {name} as a builtin function")
# Get the connection type - provider name mapping for execution use
# Tools/Providers related connection must have been imported
for param in initialize_inputs.values():
if not param.annotation:
continue
annotation_type_name = param.annotation.__name__
if annotation_type_name in connections:
api_name = provider_cls.__name__
module_logger.debug(f"Add connection type {annotation_type_name} to api {api_name} mapping")
connection_type_to_api_mapping[annotation_type_name] = api_name
break
def _register_method(provider_method, collection, type):
name = provider_method.__qualname__
provider_method.__tool = function_to_tool_definition(provider_method, type=type)
collection[name] = provider_method.__tool
module_logger.debug(f"Registered {name} as {type} function")
def register_builtins(provider_cls):
_register(provider_cls, builtins, ToolType.PYTHON)
def register_apis(provider_cls):
_register(provider_cls, apis, ToolType._ACTION)
def register_builtin_method(provider_method):
_register_method(provider_method, builtins, ToolType.PYTHON)
def register_api_method(provider_method):
_register_method(provider_method, apis, ToolType._ACTION)
def register_connections(connection_classes: Union[type, List[type]]):
connection_classes = [connection_classes] if not isinstance(connection_classes, list) else connection_classes
connections.update({cls.__name__: cls for cls in connection_classes})
class CustomToolSourceLoadError(SystemErrorException):
pass
class CustomToolError(UserErrorException):
"""Base exception raised when failed to validate tool."""
def __init__(self, message):
super().__init__(message, target=ErrorTarget.TOOL)
class EmptyCodeInCustomTool(CustomToolError):
pass
class CustomPythonToolLoadError(CustomToolError):
pass
class MissingTargetFunction(CustomToolError):
pass
class APINotFound(ValidationException):
pass
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/tool_meta_generator.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
"""
This file can generate a meta file for the given prompt template or a python file.
"""
import importlib.util
import inspect
import json
import re
import types
from dataclasses import asdict
from pathlib import Path
from traceback import TracebackException
from jinja2 import TemplateSyntaxError
from jinja2.environment import COMMENT_END_STRING, COMMENT_START_STRING
from promptflow._core._errors import MetaFileNotFound, MetaFileReadError, NotSupported
from promptflow._core.tool import ToolProvider
from promptflow._utils.exception_utils import ADDITIONAL_INFO_USER_CODE_STACKTRACE, get_tb_next, last_frame_info
from promptflow._utils.tool_utils import function_to_interface, get_inputs_for_prompt_template
from promptflow.contracts.tool import Tool, ToolType
from promptflow.exceptions import ErrorTarget, UserErrorException
PF_MAIN_MODULE_NAME = "__pf_main__"
def asdict_without_none(obj):
return asdict(obj, dict_factory=lambda x: {k: v for (k, v) in x if v})
def generate_prompt_tool(name, content, prompt_only=False, source=None):
"""Generate meta for a single jinja template file."""
# Get all the variable name from a jinja template
tool_type = ToolType.PROMPT if prompt_only else ToolType.LLM
try:
inputs = get_inputs_for_prompt_template(content)
except TemplateSyntaxError as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise JinjaParsingError(
message_format=(
"Generate tool meta failed for {tool_type} tool. Jinja parsing failed at line {line_number}: "
"{error_type_and_message}"
),
tool_type=tool_type.value,
line_number=e.lineno,
error_type_and_message=error_type_and_message,
) from e
except Exception as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise JinjaParsingError(
message_format=(
"Generate tool meta failed for {tool_type} tool. Jinja parsing failed: {error_type_and_message}"
),
tool_type=tool_type.value,
error_type_and_message=error_type_and_message,
) from e
pattern = f"{COMMENT_START_STRING}(((?!{COMMENT_END_STRING}).)*){COMMENT_END_STRING}"
match_result = re.match(pattern, content)
description = match_result.groups()[0].strip() if match_result else None
# Construct the Tool structure
tool = Tool(
name=name,
description=description,
type=tool_type,
inputs=inputs,
outputs={},
)
if source is None:
tool.code = content
else:
tool.source = source
return tool
def generate_prompt_meta_dict(name, content, prompt_only=False, source=None):
return asdict_without_none(generate_prompt_tool(name, content, prompt_only, source))
def is_tool(f):
if not isinstance(f, types.FunctionType):
return False
if not hasattr(f, "__tool"):
return False
return True
def collect_tool_functions_in_module(m):
tools = []
for _, obj in inspect.getmembers(m):
if is_tool(obj):
# Note that the tool should be in defined in exec but not imported in exec,
# so it should also have the same module with the current function.
if getattr(obj, "__module__", "") != m.__name__:
continue
tools.append(obj)
return tools
def collect_tool_methods_in_module(m):
tools = []
for _, obj in inspect.getmembers(m):
if isinstance(obj, type) and issubclass(obj, ToolProvider) and obj.__module__ == m.__name__:
for _, method in inspect.getmembers(obj):
if is_tool(method):
tools.append(method)
return tools
def collect_tool_methods_with_init_inputs_in_module(m):
tools = []
for _, obj in inspect.getmembers(m):
if isinstance(obj, type) and issubclass(obj, ToolProvider) and obj.__module__ == m.__name__:
for _, method in inspect.getmembers(obj):
if is_tool(method):
tools.append((method, obj.get_initialize_inputs()))
return tools
def _parse_tool_from_function(f, initialize_inputs=None, gen_custom_type_conn=False, skip_prompt_template=False):
try:
tool_type = getattr(f, "__type", None) or ToolType.PYTHON
except Exception as e:
raise e
tool_name = getattr(f, "__name", None)
description = getattr(f, "__description", None)
if hasattr(f, "__tool") and isinstance(f.__tool, Tool):
return f.__tool
if hasattr(f, "__original_function"):
f = f.__original_function
try:
inputs, _, _, enable_kwargs = function_to_interface(
f,
initialize_inputs=initialize_inputs,
gen_custom_type_conn=gen_custom_type_conn,
skip_prompt_template=skip_prompt_template,
)
except Exception as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise BadFunctionInterface(
message_format="Parse interface for tool '{tool_name}' failed: {error_type_and_message}",
tool_name=f.__name__,
error_type_and_message=error_type_and_message,
) from e
class_name = None
if "." in f.__qualname__:
class_name = f.__qualname__.replace(f".{f.__name__}", "")
# Construct the Tool structure
return Tool(
name=tool_name or f.__qualname__,
description=description or inspect.getdoc(f),
inputs=inputs,
type=tool_type,
class_name=class_name,
function=f.__name__,
module=f.__module__,
enable_kwargs=enable_kwargs,
)
def generate_python_tools_in_module(module):
tool_functions = collect_tool_functions_in_module(module)
tool_methods = collect_tool_methods_in_module(module)
return [_parse_tool_from_function(f) for f in tool_functions + tool_methods]
def generate_python_tools_in_module_as_dict(module):
tools = generate_python_tools_in_module(module)
return {f"{t.module}.{t.name}": asdict_without_none(t) for t in tools}
def load_python_module_from_file(src_file: Path):
# Here we hard code the module name as __pf_main__ since it is invoked as a main script in pf.
src_file = Path(src_file).resolve() # Make sure the path is absolute to align with python import behavior.
spec = importlib.util.spec_from_file_location("__pf_main__", location=src_file)
if spec is None or spec.loader is None:
raise PythonLoaderNotFound(
message_format="Failed to load python file '{src_file}'. Please make sure it is a valid .py file.",
src_file=src_file,
)
m = importlib.util.module_from_spec(spec)
try:
spec.loader.exec_module(m)
except Exception as e:
# TODO: add stacktrace to additional info
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise PythonLoadError(
message_format="Failed to load python module from file '{src_file}': {error_type_and_message}",
src_file=src_file,
error_type_and_message=error_type_and_message,
) from e
return m
def load_python_module(content, source=None):
# Source represents code first experience.
if source is not None and Path(source).exists():
return load_python_module_from_file(Path(source))
try:
m = types.ModuleType(PF_MAIN_MODULE_NAME)
exec(content, m.__dict__)
return m
except Exception as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise PythonParsingError(
message_format="Failed to load python module. Python parsing failed: {error_type_and_message}",
error_type_and_message=error_type_and_message,
) from e
def collect_tool_function_in_module(m):
tool_functions = collect_tool_functions_in_module(m)
tool_methods = collect_tool_methods_with_init_inputs_in_module(m)
num_tools = len(tool_functions) + len(tool_methods)
if num_tools == 0:
raise NoToolDefined(
message_format=(
"No tool found in the python script. "
"Please make sure you have one and only one tool definition in your script."
)
)
elif num_tools > 1:
tool_names = ", ".join(t.__name__ for t in tool_functions + tool_methods)
raise MultipleToolsDefined(
message_format=(
"Expected 1 but collected {tool_count} tools: {tool_names}. "
"Please make sure you have one and only one tool definition in your script."
),
tool_count=num_tools,
tool_names=tool_names,
)
if tool_functions:
return tool_functions[0], None
else:
return tool_methods[0]
def generate_python_tool(name, content, source=None):
m = load_python_module(content, source)
f, initialize_inputs = collect_tool_function_in_module(m)
tool = _parse_tool_from_function(f, initialize_inputs=initialize_inputs)
tool.module = None
if name is not None:
tool.name = name
if source is None:
tool.code = content
else:
tool.source = source
return tool
def generate_python_meta_dict(name, content, source=None):
return asdict_without_none(generate_python_tool(name, content, source))
# Only used in non-code first experience.
def generate_python_meta(name, content, source=None):
return json.dumps(generate_python_meta_dict(name, content, source), indent=2)
def generate_prompt_meta(name, content, prompt_only=False, source=None):
return json.dumps(generate_prompt_meta_dict(name, content, prompt_only, source), indent=2)
def generate_tool_meta_dict_by_file(path: str, tool_type: ToolType):
"""Generate meta for a single tool file, which can be a python file or a jinja template file,
note that if a python file is passed, correct working directory must be set and should be added to sys.path.
"""
tool_type = ToolType(tool_type)
file = Path(path)
if not file.is_file():
raise MetaFileNotFound(
message_format="Generate tool meta failed for {tool_type} tool. Meta file '{file_path}' can not be found.",
tool_type=tool_type.value,
file_path=path, # Use a relative path here to make the error message more readable.
)
try:
content = file.read_text(encoding="utf-8")
except Exception as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise MetaFileReadError(
message_format=(
"Generate tool meta failed for {tool_type} tool. "
"Read meta file '{file_path}' failed: {error_type_and_message}"
),
tool_type=tool_type.value,
file_path=path,
error_type_and_message=error_type_and_message,
) from e
name = file.stem
if tool_type == ToolType.PYTHON:
return generate_python_meta_dict(name, content, path)
elif tool_type == ToolType.LLM:
return generate_prompt_meta_dict(name, content, source=path)
elif tool_type == ToolType.PROMPT:
return generate_prompt_meta_dict(name, content, prompt_only=True, source=path)
else:
raise NotSupported(
message_format=(
"Generate tool meta failed. "
"The type '{tool_type}' is currently unsupported. "
"Please choose from available types: {supported_tool_types} and try again."
),
tool_type=tool_type.value,
supported_tool_types=",".join([ToolType.PYTHON, ToolType.LLM, ToolType.PROMPT]),
)
class ToolValidationError(UserErrorException):
"""Base exception raised when failed to validate tool."""
def __init__(self, **kwargs):
super().__init__(target=ErrorTarget.TOOL, **kwargs)
class JinjaParsingError(ToolValidationError):
pass
class ReservedVariableCannotBeUsed(JinjaParsingError):
pass
class PythonParsingError(ToolValidationError):
pass
class PythonLoaderNotFound(ToolValidationError):
pass
class NoToolDefined(PythonParsingError):
pass
class MultipleToolsDefined(PythonParsingError):
pass
class BadFunctionInterface(PythonParsingError):
pass
class PythonLoadError(PythonParsingError):
@property
def python_load_traceback(self):
"""Return the traceback inside user's source code scope.
The traceback inside the promptflow's internal code will be taken off.
"""
exc = self.inner_exception
if exc and exc.__traceback__ is not None:
tb = exc.__traceback__
# The first three frames are always the code in tool.py who invokes the tool.
# We do not want to dump it to user code's traceback.
tb = get_tb_next(tb, next_cnt=3)
if tb is not None:
te = TracebackException(type(exc), exc, tb)
formatted_tb = "".join(te.format())
return formatted_tb
return None
@property
def additional_info(self):
"""Set the python load exception details as additional info."""
if not self.inner_exception:
return None
info = {
"type": self.inner_exception.__class__.__name__,
"message": str(self.inner_exception),
"traceback": self.python_load_traceback,
}
info.update(last_frame_info(self.inner_exception))
return {
ADDITIONAL_INFO_USER_CODE_STACKTRACE: info,
}
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/flow_execution_context.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import asyncio
import functools
import inspect
import logging
import threading
import time
import uuid
from contextvars import ContextVar
from logging import WARNING
from typing import Callable
from promptflow._core._errors import ToolExecutionError, UnexpectedError
from promptflow._core.cache_manager import AbstractCacheManager, CacheInfo, CacheResult
from promptflow._core.operation_context import OperationContext
from promptflow._utils.logger_utils import flow_logger, logger
from promptflow._utils.thread_utils import RepeatLogTimer
from promptflow._utils.utils import generate_elapsed_time_messages
from promptflow.contracts.flow import Node
from promptflow.contracts.run_info import RunInfo
from promptflow.exceptions import PromptflowException
from .run_tracker import RunTracker
from .thread_local_singleton import ThreadLocalSingleton
from .tracer import Tracer
class FlowExecutionContext(ThreadLocalSingleton):
"""The context for a flow execution."""
CONTEXT_VAR_NAME = "Flow"
context_var = ContextVar(CONTEXT_VAR_NAME, default=None)
def __init__(
self,
name,
run_tracker: RunTracker,
cache_manager: AbstractCacheManager = None,
run_id=None,
flow_id=None,
line_number=None,
variant_id=None,
):
self._name = name
self._run_tracker = run_tracker
self._cache_manager = cache_manager or AbstractCacheManager.init_from_env()
self._run_id = run_id or str(uuid.uuid4())
self._flow_id = flow_id or self._run_id
self._line_number = line_number
self._variant_id = variant_id
def copy(self):
return FlowExecutionContext(
name=self._name,
run_tracker=self._run_tracker,
cache_manager=self._cache_manager,
run_id=self._run_id,
flow_id=self._flow_id,
line_number=self._line_number,
variant_id=self._variant_id,
)
def _update_operation_context(self):
flow_context_info = {"flow-id": self._flow_id, "root-run-id": self._run_id}
OperationContext.get_instance().update(flow_context_info)
def cancel_node_runs(self, msg):
self._run_tracker.cancel_node_runs(msg, self._run_id)
def invoke_tool(self, node: Node, f: Callable, kwargs):
run_info = self._prepare_node_run(node, f, kwargs)
node_run_id = run_info.run_id
traces = []
try:
hit_cache = False
# Get result from cache. If hit cache, no need to execute f.
cache_info: CacheInfo = self._cache_manager.calculate_cache_info(self._flow_id, f, [], kwargs)
if node.enable_cache and cache_info:
cache_result: CacheResult = self._cache_manager.get_cache_result(cache_info)
if cache_result and cache_result.hit_cache:
# Assign cached_flow_run_id and cached_run_id.
run_info.cached_flow_run_id = cache_result.cached_flow_run_id
run_info.cached_run_id = cache_result.cached_run_id
result = cache_result.result
hit_cache = True
if not hit_cache:
Tracer.start_tracing(node_run_id, node.name)
result = self._invoke_tool_with_timer(node, f, kwargs)
traces = Tracer.end_tracing(node_run_id)
self._run_tracker.end_run(node_run_id, result=result, traces=traces)
# Record result in cache so that future run might reuse its result.
if not hit_cache and node.enable_cache:
self._persist_cache(cache_info, run_info)
flow_logger.info(f"Node {node.name} completes.")
return result
except Exception as e:
logger.exception(f"Node {node.name} in line {self._line_number} failed. Exception: {e}.")
if not traces:
traces = Tracer.end_tracing(node_run_id)
self._run_tracker.end_run(node_run_id, ex=e, traces=traces)
raise
finally:
self._run_tracker.persist_node_run(run_info)
def _prepare_node_run(self, node: Node, f, kwargs={}):
# Ensure this thread has a valid operation context
self._update_operation_context()
node_run_id = self._generate_node_run_id(node)
flow_logger.info(f"Executing node {node.name}. node run id: {node_run_id}")
parent_run_id = f"{self._run_id}_{self._line_number}" if self._line_number is not None else self._run_id
run_info: RunInfo = self._run_tracker.start_node_run(
node=node.name,
flow_run_id=self._run_id,
parent_run_id=parent_run_id,
run_id=node_run_id,
index=self._line_number,
)
run_info.index = self._line_number
run_info.variant_id = self._variant_id
self._run_tracker.set_inputs(node_run_id, {key: value for key, value in kwargs.items() if key != "self"})
return run_info
async def invoke_tool_async(self, node: Node, f: Callable, kwargs):
if not inspect.iscoroutinefunction(f):
raise UnexpectedError(
message_format="Tool '{function}' in node '{node}' is not a coroutine function.",
function=f,
node=node.name,
)
run_info = self._prepare_node_run(node, f, kwargs=kwargs)
node_run_id = run_info.run_id
traces = []
try:
Tracer.start_tracing(node_run_id, node.name)
result = await self._invoke_tool_async_inner(node, f, kwargs)
traces = Tracer.end_tracing(node_run_id)
self._run_tracker.end_run(node_run_id, result=result, traces=traces)
flow_logger.info(f"Node {node.name} completes.")
return result
# User tool should reraise the CancelledError after its own handling logic,
# so that the error can propagate to the scheduler for handling.
# Otherwise, the node would end with Completed status.
except asyncio.CancelledError as e:
logger.info(f"Node {node.name} in line {self._line_number} is canceled.")
traces = Tracer.end_tracing(node_run_id)
self._run_tracker.end_run(node_run_id, ex=e, traces=traces)
raise
except Exception as e:
logger.exception(f"Node {node.name} in line {self._line_number} failed. Exception: {e}.")
traces = Tracer.end_tracing(node_run_id)
self._run_tracker.end_run(node_run_id, ex=e, traces=traces)
raise
finally:
self._run_tracker.persist_node_run(run_info)
async def _invoke_tool_async_inner(self, node: Node, f: Callable, kwargs):
module = f.func.__module__ if isinstance(f, functools.partial) else f.__module__
try:
return await f(**kwargs)
except PromptflowException as e:
# All the exceptions from built-in tools are PromptflowException.
# For these cases, raise the exception directly.
if module is not None:
e.module = module
raise e
except Exception as e:
# Otherwise, we assume the error comes from user's tool.
# For these cases, raise ToolExecutionError, which is classified as UserError
# and shows stack trace in the error message to make it easy for user to troubleshoot.
raise ToolExecutionError(node_name=node.name, module=module) from e
def _invoke_tool_with_timer(self, node: Node, f: Callable, kwargs):
module = f.func.__module__ if isinstance(f, functools.partial) else f.__module__
node_name = node.name
try:
logging_name = node_name
if self._line_number is not None:
logging_name = f"{node_name} in line {self._line_number}"
interval_seconds = 60
start_time = time.perf_counter()
thread_id = threading.current_thread().ident
with RepeatLogTimer(
interval_seconds=interval_seconds,
logger=logger,
level=WARNING,
log_message_function=generate_elapsed_time_messages,
args=(logging_name, start_time, interval_seconds, thread_id),
):
return f(**kwargs)
except PromptflowException as e:
# All the exceptions from built-in tools are PromptflowException.
# For these cases, raise the exception directly.
if module is not None:
e.module = module
raise e
except Exception as e:
# Otherwise, we assume the error comes from user's tool.
# For these cases, raise ToolExecutionError, which is classified as UserError
# and shows stack trace in the error message to make it easy for user to troubleshoot.
raise ToolExecutionError(node_name=node_name, module=module) from e
def bypass_node(self, node: Node):
"""Update teh bypassed node run info."""
node_run_id = self._generate_node_run_id(node)
flow_logger.info(f"Bypassing node {node.name}. node run id: {node_run_id}")
parent_run_id = f"{self._run_id}_{self._line_number}" if self._line_number is not None else self._run_id
run_info = self._run_tracker.bypass_node_run(
node=node.name,
flow_run_id=self._run_id,
parent_run_id=parent_run_id,
run_id=node_run_id,
index=self._line_number,
variant_id=self._variant_id,
)
self._run_tracker.persist_node_run(run_info)
def _persist_cache(self, cache_info: CacheInfo, run_info: RunInfo):
"""Record result in cache storage if hash_id is valid."""
if cache_info and cache_info.hash_id is not None and len(cache_info.hash_id) > 0:
try:
self._cache_manager.persist_result(run_info, cache_info, self._flow_id)
except Exception as ex:
# Not a critical path, swallow the exception.
logging.warning(f"Failed to persist cache result. run_id: {run_info.run_id}. Exception: {ex}")
def _generate_node_run_id(self, node: Node) -> str:
if node.aggregation:
# For reduce node, the id should be constructed by the flow run info run id
return f"{self._run_id}_{node.name}_reduce"
if self._line_number is None:
return f"{self._run_id}_{node.name}_{uuid.uuid4()}"
return f"{self._run_id}_{node.name}_{self._line_number}"
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/generator_proxy.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
class GeneratorProxy:
"""A proxy for generator that can record all items that have been yielded from the generator."""
def __init__(self, generator):
self._generator = generator
self._items = []
def __iter__(self):
return self
def __next__(self):
item = next(self._generator)
self._items.append(item)
return item
@property
def items(self):
return self._items
def generate_from_proxy(proxy: GeneratorProxy):
yield from proxy
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/cache_manager.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import hashlib
import json
from dataclasses import dataclass
from typing import Callable, List
from promptflow._utils.logger_utils import flow_logger
from promptflow.contracts.run_info import RunInfo
from promptflow.storage import AbstractCacheStorage, AbstractRunStorage
PROMPTFLOW_HASH_ATTR = "__promptflow_hash_func"
def get_calculate_cache_func(tool_func):
return getattr(tool_func, PROMPTFLOW_HASH_ATTR, None)
def set_calculate_cache_func(tool_func, calculate_cache_func):
setattr(tool_func, PROMPTFLOW_HASH_ATTR, calculate_cache_func)
def enable_cache(calculate_cache_func):
def decorator_enable_cache(func):
set_calculate_cache_func(func, calculate_cache_func)
return func
return decorator_enable_cache
@dataclass
class CacheInfo:
hash_id: str = None
cache_string: str = None
@dataclass
class CacheResult:
result: object = None
cached_run_id: str = None
cached_flow_run_id: str = None
hit_cache: bool = False
class AbstractCacheManager:
@staticmethod
def init_from_env() -> "AbstractCacheManager":
# TODO: Return CacheManager after local execution is enabled.
return DummyCacheManager()
def calculate_cache_info(self, flow_id: str, tool_method: Callable, args, kwargs) -> CacheInfo:
raise NotImplementedError("AbstractCacheManager has not implemented method calculate_cache_info.")
def get_cache_result(self, cache_info: CacheInfo) -> CacheResult:
raise NotImplementedError("AbstractCacheManager has not implemented method get_cache_result.")
def persist_result(self, run_info: RunInfo, hash_id: str, cache_string: str, flow_id: str):
raise NotImplementedError("AbstractCacheManager has not implemented method persist_result.")
class DummyCacheManager(AbstractCacheManager):
def __init__(self):
pass
def calculate_cache_info(self, flow_id: str, tool_method: Callable, args, kwargs) -> CacheInfo:
return None
def get_cache_result(self, cache_info: CacheInfo) -> CacheResult:
return None
def persist_result(self, run_info: RunInfo, hash_id: str, cache_string: str, flow_id: str):
pass
class CacheManager(AbstractCacheManager):
def __init__(self, run_storage: AbstractRunStorage, cache_storage: AbstractCacheStorage):
self._run_storage = run_storage
self._cache_storage = cache_storage
def calculate_cache_info(self, flow_id: str, tool_method: Callable, args, kwargs) -> CacheInfo:
cache_function = get_calculate_cache_func(tool_method)
# Cache function is not registered with this tool.
if cache_function is None:
return None
# Calculate cache string and hash id.
try:
cache_string = cache_function(*args, **kwargs)
except Exception as ex:
flow_logger.warning(f"Failed to calculate cache string. Exception: {ex}")
return None
# Add flow_id and tool_name in the cache string.
# So that different flow_id and tool_name cannot reuse.
other_cache_string = json.dumps(
{
"flow_id": flow_id,
"tool_name": tool_method.__qualname__,
}
)
cache_string += other_cache_string
hash_id = self._calculate_hash_id(cache_string)
return CacheInfo(hash_id=hash_id, cache_string=cache_string)
def get_cache_result(self, cache_info: CacheInfo) -> CacheResult:
hash_id = cache_info.hash_id
# Query if cache result existed by hash_id.
cache_result_list: List[CacheInfo] = self._cache_storage.get_cache_record_list(hash_id=hash_id)
if len(cache_result_list) == 0:
return None
# Get the latest cache result.
cache_result = sorted(cache_result_list, reverse=True, key=lambda i: i.end_time)[0]
try:
cached_run_info = self._run_storage.get_node_run(cache_result.run_id)
except Exception as ex:
flow_logger.warning(
f"Failed to get cached run result. \
Run id:{cached_run_info.run_id}, flow run id: {cached_run_info.flow_run_id} \
Exception: {ex}"
)
return None
flow_logger.info(
f"Hit cached result of previous run: run id: \
{cached_run_info.run_id}, flow run id: {cached_run_info.flow_run_id}"
)
return CacheResult(
result=cached_run_info.result,
cached_run_id=cached_run_info.run_id,
cached_flow_run_id=cached_run_info.flow_run_id,
hit_cache=True,
)
def persist_result(self, run_info: RunInfo, cache_info: CacheInfo, flow_id: str):
self._cache_storage.persist_cache_result(run_info, cache_info.hash_id, cache_info.cache_string, flow_id)
@staticmethod
def _calculate_hash_id(cache_string: str):
return hashlib.sha1(cache_string.encode("utf-8")).hexdigest()
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/tracer.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import functools
import inspect
import json
import logging
import uuid
from collections.abc import Iterator
from contextvars import ContextVar
from datetime import datetime
from typing import Callable, Optional, Dict
from promptflow._core.generator_proxy import GeneratorProxy, generate_from_proxy
from promptflow._utils.dataclass_serializer import serialize
from promptflow._utils.multimedia_utils import default_json_encoder
from promptflow.contracts.tool import ConnectionType
from promptflow.contracts.trace import Trace, TraceType
from .thread_local_singleton import ThreadLocalSingleton
class Tracer(ThreadLocalSingleton):
CONTEXT_VAR_NAME = "Tracer"
context_var = ContextVar(CONTEXT_VAR_NAME, default=None)
def __init__(self, run_id, node_name: Optional[str] = None):
self._run_id = run_id
self._node_name = node_name
self._traces = []
self._current_trace_id = ContextVar("current_trace_id", default="")
self._id_to_trace: Dict[str, Trace] = {}
@classmethod
def start_tracing(cls, run_id, node_name: Optional[str] = None):
current_run_id = cls.current_run_id()
if current_run_id is not None:
msg = f"Try to start tracing for run {run_id} but {current_run_id} is already active."
logging.warning(msg)
return
tracer = cls(run_id, node_name)
tracer._activate_in_context()
@classmethod
def current_run_id(cls):
tracer = cls.active_instance()
if not tracer:
return None
return tracer._run_id
@classmethod
def end_tracing(cls, run_id: Optional[str] = None, raise_ex=False):
tracer = cls.active_instance()
if not tracer:
msg = "Try end tracing but no active tracer in current context."
if raise_ex:
raise Exception(msg)
logging.warning(msg)
return []
if run_id is not None and tracer._run_id != run_id:
msg = f"Try to end tracing for run {run_id} but {tracer._run_id} is active."
logging.warning(msg)
return []
tracer._deactivate_in_context()
return tracer.to_json()
@classmethod
def push(cls, trace: Trace):
obj = cls.active_instance()
if not obj:
logging.warning("Try to push trace but no active tracer in current context.")
return
obj._push(trace)
@staticmethod
def to_serializable(obj):
if isinstance(obj, dict) and all(isinstance(k, str) for k in obj.keys()):
return {k: Tracer.to_serializable(v) for k, v in obj.items()}
if isinstance(obj, GeneratorProxy):
return obj
try:
obj = serialize(obj)
json.dumps(obj, default=default_json_encoder)
except Exception:
# We don't want to fail the whole function call because of a serialization error,
# so we simply convert it to str if it cannot be serialized.
obj = str(obj)
return obj
def _get_current_trace(self):
trace_id = self._current_trace_id.get()
if not trace_id:
return None
return self._id_to_trace[trace_id]
def _push(self, trace: Trace):
if not trace.id:
trace.id = str(uuid.uuid4())
if trace.inputs:
trace.inputs = self.to_serializable(trace.inputs)
trace.children = []
if not trace.start_time:
trace.start_time = datetime.utcnow().timestamp()
parent_trace = self._get_current_trace()
if not parent_trace:
self._traces.append(trace)
trace.node_name = self._node_name
else:
parent_trace.children.append(trace)
trace.parent_id = parent_trace.id
self._current_trace_id.set(trace.id)
self._id_to_trace[trace.id] = trace
@classmethod
def pop(cls, output=None, error: Optional[Exception] = None):
obj = cls.active_instance()
return obj._pop(output, error)
def _pop(self, output=None, error: Optional[Exception] = None):
last_trace = self._get_current_trace()
if not last_trace:
logging.warning("Try to pop trace but no active trace in current context.")
return output
if isinstance(output, Iterator):
output = GeneratorProxy(output)
if output is not None:
last_trace.output = self.to_serializable(output)
if error is not None:
last_trace.error = self._format_error(error)
last_trace.end_time = datetime.utcnow().timestamp()
self._current_trace_id.set(last_trace.parent_id)
if isinstance(output, GeneratorProxy):
return generate_from_proxy(output)
else:
return output
def to_json(self) -> list:
return serialize(self._traces)
@staticmethod
def _format_error(error: Exception) -> dict:
return {
"message": str(error),
"type": type(error).__qualname__,
}
def _create_trace_from_function_call(f, *, args=[], kwargs={}, trace_type=TraceType.FUNCTION):
"""Initialize a trace object from a function call."""
sig = inspect.signature(f).parameters
all_kwargs = {**{k: v for k, v in zip(sig.keys(), args)}, **kwargs}
all_kwargs = {
k: ConnectionType.serialize_conn(v) if ConnectionType.is_connection_value(v) else v
for k, v in all_kwargs.items()
}
# TODO: put parameters in self to inputs for builtin tools
all_kwargs.pop("self", None)
return Trace(
name=f.__qualname__,
type=trace_type,
start_time=datetime.utcnow().timestamp(),
inputs=all_kwargs,
children=[],
)
def _traced(func: Callable = None, *, trace_type=TraceType.FUNCTION) -> Callable:
"""A wrapper to add trace to a function.
When a function is wrapped by this wrapper, the function name,
inputs, outputs, start time, end time, and error (if any) will be recorded.
It can be used for both sync and async functions.
For sync functions, it will return a sync function.
For async functions, it will return an async function.
:param func: The function to be traced.
:type func: Callable
:param trace_type: The type of the trace. Defaults to TraceType.FUNCTION.
:type trace_type: TraceType, optional
:return: The wrapped function with trace enabled.
:rtype: Callable
"""
def create_trace(func, args, kwargs):
return _create_trace_from_function_call(func, args=args, kwargs=kwargs, trace_type=trace_type)
if inspect.iscoroutinefunction(func):
@functools.wraps(func)
async def wrapped(*args, **kwargs):
if Tracer.active_instance() is None:
return await func(*args, **kwargs) # Do nothing if no tracing is enabled.
# Should not extract these codes to a separate function here.
# We directly call func instead of calling Tracer.invoke,
# because we want to avoid long stack trace when hitting an exception.
try:
Tracer.push(create_trace(func, args, kwargs))
output = await func(*args, **kwargs)
return Tracer.pop(output)
except Exception as e:
Tracer.pop(None, e)
raise
else:
@functools.wraps(func)
def wrapped(*args, **kwargs):
if Tracer.active_instance() is None:
return func(*args, **kwargs) # Do nothing if no tracing is enabled.
# Should not extract these codes to a separate function here.
# We directly call func instead of calling Tracer.invoke,
# because we want to avoid long stack trace when hitting an exception.
try:
Tracer.push(create_trace(func, args, kwargs))
output = func(*args, **kwargs)
return Tracer.pop(output)
except Exception as e:
Tracer.pop(None, e)
raise
wrapped.__original_function = func
return wrapped
def trace(func: Callable = None) -> Callable:
"""A decorator to add trace to a function.
When a function is wrapped by this decorator, the function name,
inputs, outputs, start time, end time, and error (if any) will be recorded.
It can be used for both sync and async functions.
For sync functions, it will return a sync function.
For async functions, it will return an async function.
:param func: The function to be traced.
:type func: Callable
:return: The wrapped function with trace enabled.
:rtype: Callable
:Examples:
Synchronous function usage:
.. code-block:: python
@trace
def greetings(user_id):
name = get_name(user_id)
return f"Hello, {name}"
Asynchronous function usage:
.. code-block:: python
@trace
async def greetings_async(user_id):
name = await get_name_async(user_id)
return f"Hello, {name}"
"""
return _traced(func, trace_type=TraceType.FUNCTION)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/metric_logger.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import inspect
from typing import Callable
class MetricLoggerManager:
_instance = None
def __init__(self):
self._metric_loggers = []
@staticmethod
def get_instance() -> "MetricLoggerManager":
if MetricLoggerManager._instance is None:
MetricLoggerManager._instance = MetricLoggerManager()
return MetricLoggerManager._instance
def log_metric(self, key, value, variant_id=None):
for logger in self._metric_loggers:
if len(inspect.signature(logger).parameters) == 2:
logger(key, value) # If the logger only accepts two parameters, we don't pass variant_id
else:
logger(key, value, variant_id)
def add_metric_logger(self, logger_func: Callable):
existing_logger = next((logger for logger in self._metric_loggers if logger is logger_func), None)
if existing_logger:
return
if not callable(logger_func):
return
sign = inspect.signature(logger_func)
# We accept two kinds of metric loggers:
# def log_metric(k, v)
# def log_metric(k, v, variant_id)
if len(sign.parameters) not in [2, 3]:
return
self._metric_loggers.append(logger_func)
def remove_metric_logger(self, logger_func: Callable):
self._metric_loggers.remove(logger_func)
def log_metric(key, value, variant_id=None):
"""Log a metric for current promptflow run.
:param key: Metric name.
:type key: str
:param value: Metric value.
:type value: float
:param variant_id: Variant id for the metric.
:type variant_id: str
"""
MetricLoggerManager.get_instance().log_metric(key, value, variant_id)
def add_metric_logger(logger_func: Callable):
MetricLoggerManager.get_instance().add_metric_logger(logger_func)
def remove_metric_logger(logger_func: Callable):
MetricLoggerManager.get_instance().remove_metric_logger(logger_func)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/tool.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import inspect
import logging
from abc import ABC
from dataclasses import InitVar, asdict, dataclass, field
from enum import Enum
from typing import Callable, Dict, List, Optional, Union
from promptflow._core.tracer import _traced
from promptflow.contracts.trace import TraceType
module_logger = logging.getLogger(__name__)
STREAMING_OPTION_PARAMETER_ATTR = "_streaming_option_parameter"
# copied from promptflow.contracts.tool import ToolType
class ToolType(str, Enum):
LLM = "llm"
PYTHON = "python"
CSHARP = "csharp"
PROMPT = "prompt"
_ACTION = "action"
CUSTOM_LLM = "custom_llm"
class ToolInvoker(ABC):
_active_tool_invoker: Optional["ToolInvoker"] = None
def invoke_tool(self, f, *args, **kwargs):
raise NotImplementedError()
@classmethod
def activate(cls, tool_invoker: "ToolInvoker"):
cls._active_tool_invoker = tool_invoker
@classmethod
def deactivate(cls):
cls._active_tool_invoker = None
@classmethod
def active_instance(cls) -> Optional["ToolInvoker"]:
return cls._active_tool_invoker
def tool(
func=None,
*,
name: str = None,
description: str = None,
type: str = None,
input_settings=None,
streaming_option_parameter: Optional[str] = None,
**kwargs,
) -> Callable:
"""Decorator for tool functions. The decorated function will be registered as a tool and can be used in a flow.
:param name: The tool name.
:type name: str
:param description: The tool description.
:type description: str
:param type: The tool type.
:type type: str
:param input_settings: Dict of input setting.
:type input_settings: Dict[str, promptflow.entities.InputSetting]
:return: The decorated function.
:rtype: Callable
"""
def tool_decorator(func: Callable) -> Callable:
from promptflow.exceptions import UserErrorException
if type is not None and type not in [k.value for k in ToolType]:
raise UserErrorException(f"Tool type {type} is not supported yet.")
# Calls to tool functions should be traced automatically.
new_f = _traced(func, trace_type=TraceType.TOOL)
new_f.__tool = None # This will be set when generating the tool definition.
new_f.__name = name
new_f.__description = description
new_f.__type = type
new_f.__input_settings = input_settings
new_f.__extra_info = kwargs
if streaming_option_parameter and isinstance(streaming_option_parameter, str):
setattr(new_f, STREAMING_OPTION_PARAMETER_ATTR, streaming_option_parameter)
return new_f
# enable use decorator without "()" if all arguments are default values
if func is not None:
return tool_decorator(func)
return tool_decorator
def parse_all_args(argnames, args, kwargs) -> dict:
"""Parse args + kwargs to kwargs."""
all_args = {name: value for name, value in zip(argnames, args)}
all_args.update(kwargs)
return all_args
class ToolProvider(ABC):
"""The base class of tool class."""
_initialize_inputs = None
_required_initialize_inputs = None
_instance_init_params = None
def __new__(cls, *args, **kwargs):
# Record the init parameters, use __new__ so that user doesn't need to
# repeat parameters when calling super().__init__()
cls._instance_init_params = parse_all_args(cls.get_initialize_inputs().keys(), args, kwargs)
return super(ToolProvider, cls).__new__(cls)
def __init__(self):
"""
Define the base inputs of each tool.
All the parameters of __init__ will be added to inputs of each @tool in the class.
"""
self._init_params = self._instance_init_params
@classmethod
def get_initialize_inputs(cls):
if not cls._initialize_inputs:
cls._initialize_inputs = {
k: v for k, v in inspect.signature(cls.__init__).parameters.items() if k != "self"
}
return cls._initialize_inputs
@classmethod
def get_required_initialize_inputs(cls):
if not cls._required_initialize_inputs:
cls._required_initialize_inputs = {
k: v
for k, v in inspect.signature(cls.__init__).parameters.items()
if k != "self" and v.default is inspect.Parameter.empty
}
return cls._required_initialize_inputs
@dataclass
class DynamicList:
function: InitVar[Union[str, Callable]]
"""The dynamic list function."""
input_mapping: InitVar[Dict] = None
"""The mapping between dynamic list function inputs and tool inputs."""
func_path: str = field(init=False)
func_kwargs: List = field(init=False)
def __post_init__(self, function, input_mapping):
from promptflow._sdk._constants import SKIP_FUNC_PARAMS
from promptflow._utils.tool_utils import _get_function_path, function_to_interface
self._func_obj, self.func_path = _get_function_path(function)
self._input_mapping = input_mapping or {}
dynamic_list_func_inputs, _, _, _ = function_to_interface(
self._func_obj, gen_custom_type_conn=True, skip_prompt_template=True
)
# Get function input info
self.func_kwargs = []
inputs = inspect.signature(self._func_obj).parameters
for name, value in dynamic_list_func_inputs.items():
if name not in SKIP_FUNC_PARAMS:
input_info = {"name": name}
input_info.update(asdict(value, dict_factory=lambda x: {k: v for (k, v) in x if v}))
if name in self._input_mapping:
input_info["reference"] = f"${{inputs.{self._input_mapping[name]}}}"
input_info["optional"] = inputs[name].default is not inspect.Parameter.empty
if input_info["optional"]:
input_info["default"] = inputs[name].default
self.func_kwargs.append(input_info)
@dataclass
class GeneratedBy:
"""Settings of the generated by"""
function: InitVar[Union[str, Callable]]
"""The generated by function."""
reverse_function: InitVar[Union[str, Callable]]
"""The reverse generated by function."""
input_settings: InitVar[Dict[str, object]] = None
"""The input settings of generated by function."""
func_path: str = field(init=False)
func_kwargs: List = field(init=False)
reverse_func_path: str = field(init=False)
def __post_init__(self, function, reverse_function, input_settings):
from promptflow._sdk._constants import SKIP_FUNC_PARAMS, UIONLY_HIDDEN
from promptflow._utils.tool_utils import _get_function_path, function_to_interface
self._func_obj, self.func_path = _get_function_path(function=function)
self._reverse_func_obj, self.reverse_func_path = _get_function_path(function=reverse_function)
self._input_settings = {}
generated_func_inputs, _, _, _ = function_to_interface(
self._func_obj, gen_custom_type_conn=True, skip_prompt_template=True
)
# Get function input info
self.func_kwargs = []
func_inputs = inspect.signature(self._func_obj).parameters
for name, value in generated_func_inputs.items():
if name not in SKIP_FUNC_PARAMS:
# Update kwargs in generated_by settings
input_info = {"name": name}
input_info.update(asdict(value, dict_factory=lambda x: {k: v for (k, v) in x if v}))
input_info["reference"] = f"${{inputs.{name}}}"
input_info["optional"] = func_inputs[name].default is not inspect.Parameter.empty
self.func_kwargs.append(input_info)
# Generated generated_by input settings in tool func
if name in input_settings:
self._input_settings[name] = asdict(
input_settings[name], dict_factory=lambda x: {k: v for (k, v) in x if v}
)
if "type" in input_info:
self._input_settings[name]["type"] = input_info["type"]
self._input_settings[name]["input_type"] = UIONLY_HIDDEN
@dataclass
class InputSetting:
"""Settings of the tool input"""
is_multi_select: bool = None
"""Allow user to select multiple values."""
allow_manual_entry: bool = None
"""Allow user to enter input value manually."""
enabled_by: str = None
"""The input field which must be an enum type, that controls the visibility of the dependent input field."""
enabled_by_value: List = None
"""Defines the accepted enum values from the enabled_by field that will make this dependent input field visible."""
dynamic_list: DynamicList = None
"""Settings of dynamic list function."""
generated_by: GeneratedBy = None
"""Settings of generated by function."""
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/openai_injector.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import asyncio
import functools
import importlib
import inspect
import logging
import os
from datetime import datetime
from importlib.metadata import version
import openai
from promptflow._core.operation_context import OperationContext
from promptflow.contracts.trace import Trace, TraceType
from .tracer import Tracer
USER_AGENT_HEADER = "x-ms-useragent"
PROMPTFLOW_PREFIX = "ms-azure-ai-promptflow-"
IS_LEGACY_OPENAI = version("openai").startswith("0.")
def inject_function_async(args_to_ignore=None, trace_type=TraceType.LLM):
args_to_ignore = args_to_ignore or []
args_to_ignore = set(args_to_ignore)
def wrapper(f):
sig = inspect.signature(f).parameters
@functools.wraps(f)
async def wrapped_method(*args, **kwargs):
if not Tracer.active():
return await f(*args, **kwargs)
all_kwargs = {**{k: v for k, v in zip(sig.keys(), args)}, **kwargs}
for key in args_to_ignore:
all_kwargs.pop(key, None)
name = f.__qualname__ if not f.__module__ else f.__module__ + "." + f.__qualname__
trace = Trace(
name=name,
type=trace_type,
inputs=all_kwargs,
start_time=datetime.utcnow().timestamp(),
)
Tracer.push(trace)
try:
result = await f(*args, **kwargs)
except Exception as ex:
Tracer.pop(error=ex)
raise
else:
result = Tracer.pop(result)
return result
return wrapped_method
return wrapper
def inject_function_sync(args_to_ignore=None, trace_type=TraceType.LLM):
args_to_ignore = args_to_ignore or []
args_to_ignore = set(args_to_ignore)
def wrapper(f):
sig = inspect.signature(f).parameters
@functools.wraps(f)
def wrapped_method(*args, **kwargs):
if not Tracer.active():
return f(*args, **kwargs)
all_kwargs = {**{k: v for k, v in zip(sig.keys(), args)}, **kwargs}
for key in args_to_ignore:
all_kwargs.pop(key, None)
name = f.__qualname__ if not f.__module__ else f.__module__ + "." + f.__qualname__
trace = Trace(
name=name,
type=trace_type,
inputs=all_kwargs,
start_time=datetime.utcnow().timestamp(),
)
Tracer.push(trace)
try:
result = f(*args, **kwargs)
except Exception as ex:
Tracer.pop(error=ex)
raise
else:
result = Tracer.pop(result)
return result
return wrapped_method
return wrapper
def get_aoai_telemetry_headers() -> dict:
"""Get the http headers for AOAI request.
The header, whose name starts with "ms-azure-ai-" or "x-ms-", is used to track the request in AOAI. The
value in this dict will be recorded as telemetry, so please do not put any sensitive information in it.
Returns:
A dictionary of http headers.
"""
# get promptflow info from operation context
operation_context = OperationContext.get_instance()
context_info = operation_context.get_context_dict()
promptflow_info = {k.replace("_", "-"): v for k, v in context_info.items()}
# init headers
headers = {USER_AGENT_HEADER: operation_context.get_user_agent()}
# update header with promptflow info
headers.update({f"{PROMPTFLOW_PREFIX}{k}": str(v) if v is not None else "" for k, v in promptflow_info.items()})
return headers
def inject_operation_headers(f):
def inject_headers(kwargs):
# Inject headers from operation context, overwrite injected header with headers from kwargs.
injected_headers = get_aoai_telemetry_headers()
original_headers = kwargs.get("headers" if IS_LEGACY_OPENAI else "extra_headers")
if original_headers and isinstance(original_headers, dict):
injected_headers.update(original_headers)
kwargs["headers" if IS_LEGACY_OPENAI else "extra_headers"] = injected_headers
if asyncio.iscoroutinefunction(f):
@functools.wraps(f)
async def wrapper(*args, **kwargs):
inject_headers(kwargs)
return await f(*args, **kwargs)
else:
@functools.wraps(f)
def wrapper(*args, **kwargs):
inject_headers(kwargs)
return f(*args, **kwargs)
return wrapper
def inject_async(f):
wrapper_fun = inject_operation_headers((inject_function_async(["api_key", "headers", "extra_headers"])(f)))
wrapper_fun._original = f
return wrapper_fun
def inject_sync(f):
wrapper_fun = inject_operation_headers((inject_function_sync(["api_key", "headers", "extra_headers"])(f)))
wrapper_fun._original = f
return wrapper_fun
def _openai_api_list():
if IS_LEGACY_OPENAI:
sync_apis = (
("openai", "Completion", "create"),
("openai", "ChatCompletion", "create"),
("openai", "Embedding", "create"),
)
async_apis = (
("openai", "Completion", "acreate"),
("openai", "ChatCompletion", "acreate"),
("openai", "Embedding", "acreate"),
)
else:
sync_apis = (
("openai.resources.chat", "Completions", "create"),
("openai.resources", "Completions", "create"),
("openai.resources", "Embeddings", "create"),
)
async_apis = (
("openai.resources.chat", "AsyncCompletions", "create"),
("openai.resources", "AsyncCompletions", "create"),
("openai.resources", "AsyncEmbeddings", "create"),
)
yield sync_apis, inject_sync
yield async_apis, inject_async
def _generate_api_and_injector(apis):
for apis, injector in apis:
for module_name, class_name, method_name in apis:
try:
module = importlib.import_module(module_name)
api = getattr(module, class_name)
if hasattr(api, method_name):
yield api, method_name, injector
except AttributeError as e:
# Log the attribute exception with the missing class information
logging.warning(
f"AttributeError: The module '{module_name}' does not have the class '{class_name}'. {str(e)}"
)
except Exception as e:
# Log other exceptions as a warning, as we're not sure what they might be
logging.warning(f"An unexpected error occurred: {str(e)}")
def available_openai_apis_and_injectors():
"""
Generates a sequence of tuples containing OpenAI API classes, method names, and
corresponding injector functions based on whether the legacy OpenAI interface is used.
This function handles the discrepancy reported in https://github.com/openai/openai-python/issues/996,
where async interfaces were not recognized as coroutines. It ensures that decorators
are applied correctly to both synchronous and asynchronous methods.
Yields:
Tuples of (api_class, method_name, injector_function)
"""
yield from _generate_api_and_injector(_openai_api_list())
def inject_openai_api():
"""This function:
1. Modifies the create methods of the OpenAI API classes to inject logic before calling the original methods.
It stores the original methods as _original attributes of the create methods.
2. Updates the openai api configs from environment variables.
"""
for api, method, injector in available_openai_apis_and_injectors():
# Check if the create method of the openai_api class has already been modified
if not hasattr(getattr(api, method), "_original"):
setattr(api, method, injector(getattr(api, method)))
if IS_LEGACY_OPENAI:
# For the openai versions lower than 1.0.0, it reads api configs from environment variables only at
# import time. So we need to update the openai api configs from environment variables here.
# Please refer to this issue: https://github.com/openai/openai-python/issues/557.
# The issue has been fixed in openai>=1.0.0.
openai.api_key = os.environ.get("OPENAI_API_KEY", openai.api_key)
openai.api_key_path = os.environ.get("OPENAI_API_KEY_PATH", openai.api_key_path)
openai.organization = os.environ.get("OPENAI_ORGANIZATION", openai.organization)
openai.api_base = os.environ.get("OPENAI_API_BASE", openai.api_base)
openai.api_type = os.environ.get("OPENAI_API_TYPE", openai.api_type)
openai.api_version = os.environ.get("OPENAI_API_VERSION", openai.api_version)
def recover_openai_api():
"""This function restores the original create methods of the OpenAI API classes
by assigning them back from the _original attributes of the modified methods.
"""
for api, method, _ in available_openai_apis_and_injectors():
if hasattr(getattr(api, method), "_original"):
setattr(api, method, getattr(getattr(api, method), "_original"))
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/thread_local_singleton.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from contextvars import ContextVar
from typing import Type, TypeVar
T = TypeVar("T")
class ThreadLocalSingleton:
# Use context variable to enable thread local singleton
# See reference: https://docs.python.org/3/library/contextvars.html#contextvars.ContextVar
CONTEXT_VAR_NAME = "ThreadLocalSingleton"
context_var = ContextVar(CONTEXT_VAR_NAME, default=None)
@classmethod
def active_instance(cls: Type[T]) -> T:
return cls.context_var.get()
@classmethod
def active(cls) -> bool:
return cls.active_instance() is not None
def _activate_in_context(self, force=False):
instance = self.active_instance()
if instance is not None and instance is not self and not force:
raise NotImplementedError(f"Cannot set active since there is another active instance: {instance}")
self.context_var.set(self)
def _deactivate_in_context(self):
self.context_var.set(None)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/connection_manager.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import json
import os
from dataclasses import fields, is_dataclass
from pathlib import Path
from typing import Any, Dict, List
from promptflow._constants import CONNECTION_NAME_PROPERTY, CONNECTION_SECRET_KEYS, PROMPTFLOW_CONNECTIONS
from promptflow._sdk._constants import CustomStrongTypeConnectionConfigs
from promptflow._utils.utils import try_import
from promptflow.contracts.tool import ConnectionType
from promptflow.contracts.types import Secret
class ConnectionManager:
"""This class will be used for construction mode to run flow. Do not include it into tool code."""
instance = None
def __init__(self, _dict: Dict[str, dict] = None):
if _dict is None and PROMPTFLOW_CONNECTIONS in os.environ:
# !!! Important !!!: Do not leverage this environment variable in any production code, this is test only.
if PROMPTFLOW_CONNECTIONS not in os.environ:
raise ValueError(f"Required environment variable {PROMPTFLOW_CONNECTIONS!r} not set.")
connection_path = Path(os.environ[PROMPTFLOW_CONNECTIONS]).resolve().absolute()
if not connection_path.exists():
raise ValueError(f"Connection file not exists. Path {connection_path.as_posix()}.")
_dict = json.loads(open(connection_path).read())
self._connections_dict = _dict or {}
self._connections = self._build_connections(self._connections_dict)
@classmethod
def _build_connections(cls, _dict: Dict[str, dict]):
"""Build connection dict."""
from promptflow._core.tools_manager import connections as cls_mapping
cls.import_requisites(_dict)
connections = {} # key to connection object
for key, connection_dict in _dict.items():
typ = connection_dict.get("type")
if typ not in cls_mapping:
supported = [key for key in cls_mapping.keys() if not key.startswith("_")]
raise ValueError(f"Unknown connection {key!r} type {typ!r}, supported are {supported}.")
value = connection_dict.get("value", {})
connection_class = cls_mapping[typ]
from promptflow.connections import CustomConnection
if connection_class is CustomConnection:
# Note: CustomConnection definition can not be got, secret keys will be provided in connection dict.
secret_keys = connection_dict.get("secret_keys", [])
secrets = {k: v for k, v in value.items() if k in secret_keys}
configs = {k: v for k, v in value.items() if k not in secrets}
connection_value = connection_class(configs=configs, secrets=secrets)
if CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY in configs:
connection_value.custom_type = configs[CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY]
else:
"""
Note: Ignore non exists keys of connection class,
because there are some keys just used by UX like resource id, while not used by backend.
"""
if is_dataclass(connection_class):
# Do not delete this branch, as promptflow_vectordb.connections is dataclass type.
cls_fields = {f.name: f for f in fields(connection_class)}
connection_value = connection_class(**{k: v for k, v in value.items() if k in cls_fields})
secret_keys = [f.name for f in cls_fields.values() if f.type == Secret]
else:
connection_value = connection_class(**{k: v for k, v in value.items()})
secrets = getattr(connection_value, "secrets", {})
secret_keys = list(secrets.keys()) if isinstance(secrets, dict) else []
# Set secret keys for log scrubbing
setattr(connection_value, CONNECTION_SECRET_KEYS, secret_keys)
# Use this hack to make sure serialization works
setattr(connection_value, CONNECTION_NAME_PROPERTY, key)
connections[key] = connection_value
return connections
@classmethod
def init_from_env(cls):
return ConnectionManager()
def get(self, connection_info: Any) -> Any:
"""Get Connection by connection info.
connection_info:
connection name as string or connection object
"""
if isinstance(connection_info, str):
return self._connections.get(connection_info)
elif ConnectionType.is_connection_value(connection_info):
return connection_info
return None
def get_secret_list(self) -> List[str]:
def secrets():
for connection in self._connections.values():
secret_keys = getattr(connection, CONNECTION_SECRET_KEYS, [])
for secret_key in secret_keys:
yield getattr(connection, secret_key)
return list(secrets())
@classmethod
def import_requisites(cls, _dict: Dict[str, dict]):
"""Import connection required modules."""
modules = set()
for key, connection_dict in _dict.items():
module = connection_dict.get("module")
if module:
modules.add(module)
for module in modules:
# Suppress import error, as we have legacy module promptflow.tools.connections.
try_import(module, f"Import connection module {module!r} failed.", raise_error=False)
@staticmethod
def is_legacy_connections(_dict: Dict[str, dict]):
"""Detect if is legacy connections. Legacy connections dict doesn't have module and type.
So import requisites can not be performed. Only request from MT will hit this.
Legacy connection example: {"aoai_config": {"api_key": "..."}}
"""
has_module = any(isinstance(v, dict) and "module" in v for k, v in _dict.items())
return not has_module
def to_connections_dict(self) -> dict:
"""Get all connections and reformat to key-values format."""
# Value returned: {"aoai_config": {"api_key": "..."}}
return copy.deepcopy(self._connections_dict)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/_core/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_pf_client.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import os
from os import PathLike
from typing import Dict, List, Optional, Union
from azure.ai.ml import MLClient
from azure.core.credentials import TokenCredential
from promptflow._sdk._constants import MAX_SHOW_DETAILS_RESULTS
from promptflow._sdk._errors import RunOperationParameterError
from promptflow._sdk._user_agent import USER_AGENT
from promptflow._sdk._utils import ClientUserAgentUtil, setup_user_agent_to_operation_context
from promptflow._sdk.entities import Run
from promptflow.azure._restclient.service_caller_factory import _FlowServiceCallerFactory
from promptflow.azure.operations import RunOperations
from promptflow.azure.operations._arm_connection_operations import ArmConnectionOperations
from promptflow.azure.operations._connection_operations import ConnectionOperations
from promptflow.azure.operations._flow_operations import FlowOperations
from promptflow.exceptions import UserErrorException
class PFClient:
"""A client class to interact with Promptflow service.
Use this client to manage promptflow resources, e.g. runs.
:param credential: Credential to use for authentication, optional
:type credential: ~azure.core.credentials.TokenCredential
:param subscription_id: Azure subscription ID, optional for registry assets only, optional
:type subscription_id: typing.Optional[str]
:param resource_group_name: Azure resource group, optional for registry assets only, optional
:type resource_group_name: typing.Optional[str]
:param workspace_name: Workspace to use in the client, optional for non workspace dependent operations only,
optional.
:type workspace_name: typing.Optional[str]
:param kwargs: A dictionary of additional configuration parameters.
:type kwargs: dict
"""
def __init__(
self,
credential: TokenCredential = None,
subscription_id: Optional[str] = None,
resource_group_name: Optional[str] = None,
workspace_name: Optional[str] = None,
**kwargs,
):
self._validate_config_information(subscription_id, resource_group_name, workspace_name, kwargs)
# add user agent from kwargs if any
if isinstance(kwargs.get("user_agent", None), str):
ClientUserAgentUtil.append_user_agent(kwargs["user_agent"])
# append SDK ua to context
user_agent = setup_user_agent_to_operation_context(USER_AGENT)
kwargs.setdefault("user_agent", user_agent)
self._ml_client = kwargs.pop("ml_client", None) or MLClient(
credential=credential,
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
**kwargs,
)
try:
workspace = self._ml_client.workspaces.get(name=self._ml_client._operation_scope.workspace_name)
except Exception as e:
raise UserErrorException(message=str(e), error=e)
self._service_caller = _FlowServiceCallerFactory.get_instance(
workspace=workspace,
credential=self._ml_client._credential,
operation_scope=self._ml_client._operation_scope,
**kwargs,
)
self._flows = FlowOperations(
operation_scope=self._ml_client._operation_scope,
operation_config=self._ml_client._operation_config,
all_operations=self._ml_client._operation_container,
credential=self._ml_client._credential,
service_caller=self._service_caller,
workspace=workspace,
**kwargs,
)
self._runs = RunOperations(
operation_scope=self._ml_client._operation_scope,
operation_config=self._ml_client._operation_config,
all_operations=self._ml_client._operation_container,
credential=self._ml_client._credential,
flow_operations=self._flows,
service_caller=self._service_caller,
workspace=workspace,
**kwargs,
)
self._connections = ConnectionOperations(
operation_scope=self._ml_client._operation_scope,
operation_config=self._ml_client._operation_config,
all_operations=self._ml_client._operation_container,
credential=self._ml_client._credential,
service_caller=self._service_caller,
**kwargs,
)
self._arm_connections = ArmConnectionOperations(
operation_scope=self._ml_client._operation_scope,
operation_config=self._ml_client._operation_config,
all_operations=self._ml_client._operation_container,
credential=self._ml_client._credential,
service_caller=self._service_caller,
**kwargs,
)
@staticmethod
def _validate_config_information(subscription_id, resource_group_name, workspace_name, kwargs):
"""Validate the config information in case wrong parameter name is passed into the constructor."""
sub_name, wrong_sub_name = "subscription_id", "subscription"
rg_name, wrong_rg_name = "resource_group_name", "resource_group"
ws_name, wrong_ws_name = "workspace_name", "workspace"
error_message = (
"You have passed in the wrong parameter name to initialize the PFClient, please use {0!r} instead of {1!r}."
)
if not subscription_id and kwargs.get(wrong_sub_name, None) is not None:
raise RunOperationParameterError(error_message.format(sub_name, wrong_sub_name))
if not resource_group_name and kwargs.get(wrong_rg_name, None) is not None:
raise RunOperationParameterError(error_message.format(rg_name, wrong_rg_name))
if not workspace_name and kwargs.get(wrong_ws_name, None) is not None:
raise RunOperationParameterError(error_message.format(ws_name, wrong_ws_name))
@property
def ml_client(self):
"""Return a client to interact with Azure ML services."""
return self._ml_client
@property
def runs(self):
"""Return the run operation object that can manage runs."""
return self._runs
@property
def flows(self):
"""Return the flow operation object that can manage flows."""
return self._flows
@classmethod
def from_config(
cls,
credential: TokenCredential,
*,
path: Optional[Union[os.PathLike, str]] = None,
file_name=None,
**kwargs,
) -> "PFClient":
"""Return a PFClient object connected to Azure Machine Learning workspace.
Reads workspace configuration from a file. Throws an exception if the config file can't be found.
The method provides a simple way to reuse the same workspace across multiple Python notebooks or projects.
Users can save the workspace Azure Resource Manager (ARM) properties using the
[workspace.write_config](https://aka.ms/ml-workspace-class) method,
and use this method to load the same workspace in different Python notebooks or projects without
retyping the workspace ARM properties.
:param credential: The credential object for the workspace.
:type credential: ~azure.core.credentials.TokenCredential
:param path: The path to the config file or starting directory to search.
The parameter defaults to starting the search in the current directory.
optional
:type path: typing.Union[os.PathLike, str]
:param file_name: Allows overriding the config file name to search for when path is a directory path.
(Default value = None)
:type file_name: str
"""
ml_client = MLClient.from_config(credential=credential, path=path, file_name=file_name, **kwargs)
return PFClient(
ml_client=ml_client,
**kwargs,
)
def run(
self,
flow: Union[str, PathLike],
*,
data: Union[str, PathLike] = None,
run: Union[str, Run] = None,
column_mapping: dict = None,
variant: str = None,
connections: dict = None,
environment_variables: dict = None,
name: str = None,
display_name: str = None,
tags: Dict[str, str] = None,
**kwargs,
) -> Run:
"""Run flow against provided data or run.
.. note:: at least one of data or run must be provided.
.. admonition::
Data can be local file or remote path.
- Example:
- `data = "path/to/local/file"`
- `data = "azureml:data_name:data_version"`
- `data = "azureml://datastores/datastore_name/path/to/file"`
- `data = "https://example.com/data.jsonl"`
Column mapping is a mapping from flow input name to specified values.
If specified, the flow will be executed with provided value for specified inputs.
The value can be:
- from data:
- ``data.col1``
- from run:
- ``run.inputs.col1``: if need reference run's inputs
- ``run.output.col1``: if need reference run's outputs
- Example:
- ``{"ground_truth": "${data.answer}", "prediction": "${run.outputs.answer}"}``
:param flow: path to flow directory to run evaluation
:type flow: Union[str, PathLike]
:param data: pointer to test data (of variant bulk runs) for eval runs
:type data: Union[str, PathLike]
:param run: flow run id or flow run, keep lineage between current run and variant runs,
batch outputs can be referenced as ${run.outputs.col_name} in inputs_mapping
:type run: Union[str, ~promptflow.entities.Run]
:param column_mapping: define a data flow logic to map input data.
:type column_mapping: dict
:param variant: Node & variant name in format of ${node_name.variant_name}, will use default variant
if not specified.
:type variant: str
:param connections: Overwrite node level connections with provided value.
Example: ``{"node1": {"connection": "new_connection", "deployment_name": "gpt-35-turbo"}}``
:type connections: dict
:param environment_variables: Environment variables to set by specifying a property path and value.
Example: ``{"key1": "${my_connection.api_key}", "key2"="value2"}``
The value reference to connection keys will be resolved to the actual value,
and all environment variables specified will be set into os.environ.
:type environment_variables: dict
:param name: Name of the run.
:type name: str
:param display_name: Display name of the run.
:type display_name: str
:param tags: Tags of the run.
:type tags: Dict[str, str]
:return: flow run info.
:rtype: ~promptflow.entities.Run
"""
# TODO(2887134): support cloud eager Run CRUD
run = Run(
name=name,
display_name=display_name,
tags=tags,
data=data,
column_mapping=column_mapping,
run=run,
variant=variant,
flow=flow,
connections=connections,
environment_variables=environment_variables,
)
return self.runs.create_or_update(run=run, **kwargs)
def stream(self, run: Union[str, Run], raise_on_error: bool = True) -> Run:
"""Stream run logs to the console.
:param run: Run object or name of the run.
:type run: Union[str, ~promptflow.sdk.entities.Run]
:param raise_on_error: Raises an exception if a run fails or canceled.
:type raise_on_error: bool
:return: flow run info.
"""
if isinstance(run, Run):
run = run.name
return self.runs.stream(run, raise_on_error)
def get_details(
self, run: Union[str, Run], max_results: int = MAX_SHOW_DETAILS_RESULTS, all_results: bool = False
) -> "DataFrame":
"""Get the details from the run including inputs and outputs.
.. note::
If `all_results` is set to True, `max_results` will be overwritten to sys.maxsize.
:param run: The run name or run object
:type run: Union[str, ~promptflow.sdk.entities.Run]
:param max_results: The max number of runs to return, defaults to 100
:type max_results: int
:param all_results: Whether to return all results, defaults to False
:type all_results: bool
:raises RunOperationParameterError: If `max_results` is not a positive integer.
:return: The details data frame.
:rtype: pandas.DataFrame
"""
return self.runs.get_details(run=run, max_results=max_results, all_results=all_results)
def get_metrics(self, run: Union[str, Run]) -> dict:
"""Print run metrics to the console.
:param run: Run object or name of the run.
:type run: Union[str, ~promptflow.sdk.entities.Run]
:return: The run's metrics
:rtype: dict
"""
if isinstance(run, Run):
run = run.name
return self.runs.get_metrics(run=run)
def visualize(self, runs: Union[List[str], List[Run]]) -> None:
"""Visualize run(s).
:param run: Run object or name of the run.
:type run: Union[str, ~promptflow.sdk.entities.Run]
"""
self.runs.visualize(runs)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_load_functions.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from os import PathLike
from pathlib import Path
from typing import IO, AnyStr, Optional, Union
from ._utils import is_arm_id
def load_flow(
source: Union[str, PathLike, IO[AnyStr]],
*,
relative_origin: Optional[str] = None,
**kwargs,
):
"""Construct a flow object from a yaml file.
:param source: The local yaml source of a compute. Must be either a
path to a local file, or an already-open file.
If the source is a path, it will be open and read.
An exception is raised if the file does not exist.
If the source is an open file, the file will be read directly,
and an exception is raised if the file is not readable.
:type source: Union[PathLike, str, io.TextIOWrapper]
:param relative_origin: The origin to be used when deducing
the relative locations of files referenced in the parsed yaml.
Defaults to the inputted source's directory if it is a file or file path input.
Defaults to "./" if the source is a stream input with no name value.
:type relative_origin: str
:param params_override: Fields to overwrite on top of the yaml file.
Format is [{"field1": "value1"}, {"field2": "value2"}]
:type params_override: List[Dict]
:return: Loaded flow object.
:rtype: promptflow.azure.Flow
"""
from promptflow.azure._entities._flow import Flow
if is_arm_id(source):
return source
return Flow(path=Path(source))
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
from ._pf_client import PFClient
__all__ = ["PFClient"]
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow_service_caller.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
"""service_caller.py, module for interacting with the AzureML service."""
import json
import os
import sys
import time
import uuid
from functools import wraps, cached_property
import pydash
from azure.core.exceptions import HttpResponseError, ResourceExistsError
from azure.core.pipeline.policies import RetryPolicy
from promptflow._sdk._telemetry import request_id_context
from promptflow._sdk._telemetry import TelemetryMixin
from promptflow._utils.logger_utils import LoggerFactory
from promptflow.azure._constants._flow import AUTOMATIC_RUNTIME, SESSION_CREATION_TIMEOUT_ENV_VAR
from promptflow.azure._restclient.flow import AzureMachineLearningDesignerServiceClient
from promptflow.azure._utils.gerneral import get_authorization, get_arm_token, get_aml_token
from promptflow.exceptions import UserErrorException, PromptflowException, SystemErrorException
logger = LoggerFactory.get_logger(__name__)
class FlowRequestException(SystemErrorException):
"""FlowRequestException."""
def __init__(self, message, **kwargs):
super().__init__(message, **kwargs)
class RequestTelemetryMixin(TelemetryMixin):
def __init__(self):
super().__init__()
self._refresh_request_id_for_telemetry()
self._from_cli = False
def _get_telemetry_values(self, *args, **kwargs):
return {"request_id": self._request_id, "from_cli": self._from_cli}
def _set_from_cli_for_telemetry(self):
self._from_cli = True
def _refresh_request_id_for_telemetry(self):
# refresh request id from current request id context
self._request_id = request_id_context.get() or str(uuid.uuid4())
def _request_wrapper():
"""Wrapper for request. Will refresh request id and pretty print exception."""
def exception_wrapper(func):
@wraps(func)
def wrapper(self, *args, **kwargs):
if not isinstance(self, RequestTelemetryMixin):
raise PromptflowException(f"Wrapped function is not RequestTelemetryMixin, got {type(self)}")
# refresh request before each request
self._refresh_request_id_for_telemetry()
try:
return func(self, *args, **kwargs)
except HttpResponseError as e:
raise FlowRequestException(
f"Calling {func.__name__} failed with request id: {self._request_id} \n"
f"Status code: {e.status_code} \n"
f"Reason: {e.reason} \n"
f"Error message: {e.message} \n"
)
return wrapper
return exception_wrapper
class FlowServiceCaller(RequestTelemetryMixin):
"""FlowServiceCaller.
:param workspace: workspace
:type workspace: Workspace
:param base_url: base url
:type base_url: Service URL
"""
# The default namespace placeholder is used when namespace is None for get_module API.
DEFAULT_COMPONENT_NAMESPACE_PLACEHOLDER = "-"
DEFAULT_MODULE_WORKING_MECHANISM = "OutputToDataset"
DEFAULT_DATATYPE_MECHANISM = "RegisterBuildinDataTypeOnly"
FLOW_CLUSTER_ADDRESS = "FLOW_CLUSTER_ADDRESS"
WORKSPACE_INDEPENDENT_ENDPOINT_ADDRESS = "WORKSPACE_INDEPENDENT_ENDPOINT_ADDRESS"
DEFAULT_BASE_URL = "https://{}.api.azureml.ms"
MASTER_BASE_API = "https://master.api.azureml-test.ms"
DEFAULT_BASE_REGION = "westus2"
AML_USE_ARM_TOKEN = "AML_USE_ARM_TOKEN"
def __init__(self, workspace, credential, operation_scope, base_url=None, region=None, **kwargs):
"""Initializes DesignerServiceCaller."""
if "get_instance" != sys._getframe().f_back.f_code.co_name:
raise UserErrorException(
"Please use `_FlowServiceCallerFactory.get_instance()` to get service caller "
"instead of creating a new one."
)
super().__init__()
# self._service_context = workspace.service_context
if base_url is None:
# handle vnet scenario, it's discovery url will have workspace id after discovery
base_url = workspace.discovery_url.split("discovery")[0]
# for dev test, change base url with environment variable
base_url = os.environ.get(self.FLOW_CLUSTER_ADDRESS, default=base_url)
self._workspace = workspace
self._operation_scope = operation_scope
self._service_endpoint = base_url
self._credential = credential
retry_policy = RetryPolicy()
# stop retry 500 since it will cause 409 for run creation scenario
retry_policy._retry_on_status_codes.remove(500)
self.caller = AzureMachineLearningDesignerServiceClient(base_url=base_url, retry_policy=retry_policy, **kwargs)
def _get_headers(self):
custom_header = {
"Authorization": get_authorization(credential=self._credential),
"x-ms-client-request-id": self._request_id,
}
return custom_header
def _set_headers_with_user_aml_token(self, headers):
aml_token = get_aml_token(credential=self._credential)
headers["aml-user-token"] = aml_token
def _get_user_identity_info(self):
import jwt
token = get_arm_token(credential=self._credential)
decoded_token = jwt.decode(token, options={"verify_signature": False})
user_object_id, user_tenant_id = decoded_token["oid"], decoded_token["tid"]
return user_object_id, user_tenant_id
@cached_property
def _common_azure_url_pattern(self):
operation_scope = self._operation_scope
pattern = (
f"/subscriptions/{operation_scope.subscription_id}"
f"/resourceGroups/{operation_scope.resource_group_name}"
f"/providers/Microsoft.MachineLearningServices"
f"/workspaces/{operation_scope.workspace_name}"
)
return pattern
@_request_wrapper()
def create_flow(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
experiment_id=None, # type: Optional[str]
body=None, # type: Optional["_models.CreateFlowRequest"]
**kwargs, # type: Any
):
headers = self._get_headers()
return self.caller.flows.create_flow(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
experiment_id=experiment_id,
body=body,
headers=headers,
**kwargs,
)
@_request_wrapper()
def create_component_from_flow(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
body=None, # type: Optional["_models.LoadFlowAsComponentRequest"]
**kwargs, # type: Any
):
headers = self._get_headers()
try:
return self.caller.flows.load_as_component(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
body=body,
headers=headers,
**kwargs,
)
except ResourceExistsError:
return (
f"/subscriptions/{subscription_id}/resourceGroups/{resource_group_name}"
f"/providers/Microsoft.MachineLearningServices/workspaces/{workspace_name}"
f"/components/{body.component_name}/versions/{body.component_version}"
)
@_request_wrapper()
def list_flows(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
experiment_id=None, # type: Optional[str]
owned_only=None, # type: Optional[bool]
flow_type=None, # type: Optional[Union[str, "_models.FlowType"]]
list_view_type=None, # type: Optional[Union[str, "_models.ListViewType"]]
**kwargs, # type: Any
):
headers = self._get_headers()
return self.caller.flows.list_flows(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
experiment_id=experiment_id,
owned_only=owned_only,
flow_type=flow_type,
list_view_type=list_view_type,
headers=headers,
**kwargs,
)
@_request_wrapper()
def submit_flow(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
experiment_id, # type: str
endpoint_name=None, # type: Optional[str]
body=None, # type: Optional["_models.SubmitFlowRequest"]
**kwargs, # type: Any
):
headers = self._get_headers()
return self.caller.flows.submit_flow(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
experiment_id=experiment_id,
endpoint_name=endpoint_name,
body=body,
headers=headers,
**kwargs,
)
@_request_wrapper()
def get_flow(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
experiment_id, # type: str
**kwargs, # type: Any
):
headers = self._get_headers()
return self.caller.flows.get_flow(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
experiment_id=experiment_id,
flow_id=flow_id,
headers=headers,
**kwargs,
)
@_request_wrapper()
def get_flow_run(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
**kwargs, # type: Any
):
"""Get flow run."""
headers = self._get_headers()
return self.caller.bulk_runs.get_flow_run_info(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
headers=headers,
**kwargs,
)
@_request_wrapper()
def create_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
body=None, # type: Optional["_models.CreateOrUpdateConnectionRequest"]
**kwargs, # type: Any
):
headers = self._get_headers()
return self.caller.connections.create_connection(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
body=body,
headers=headers,
**kwargs,
)
@_request_wrapper()
def update_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
body=None, # type: Optional["_models.CreateOrUpdateConnectionRequestDto"]
**kwargs, # type: Any
):
headers = self._get_headers()
return self.caller.connections.update_connection(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
body=body,
headers=headers,
**kwargs,
)
@_request_wrapper()
def get_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs, # type: Any
):
headers = self._get_headers()
return self.caller.connections.get_connection(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
headers=headers,
**kwargs,
)
@_request_wrapper()
def delete_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs, # type: Any
):
headers = self._get_headers()
return self.caller.connections.delete_connection(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
headers=headers,
**kwargs,
)
@_request_wrapper()
def list_connections(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs, # type: Any
):
headers = self._get_headers()
return self.caller.connections.list_connections(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
headers=headers,
**kwargs,
)
@_request_wrapper()
def list_connection_specs(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs, # type: Any
):
headers = self._get_headers()
return self.caller.connections.list_connection_specs(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
headers=headers,
**kwargs,
)
@_request_wrapper()
def submit_bulk_run(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
body=None, # type: Optional["_models.SubmitBulkRunRequest"]
**kwargs, # type: Any
):
"""submit_bulk_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param body:
:type body: ~flow.models.SubmitBulkRunRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
headers = self._get_headers()
# pass user aml token to flow run submission
self._set_headers_with_user_aml_token(headers)
return self.caller.bulk_runs.submit_bulk_run(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
headers=headers,
body=body,
**kwargs,
)
@_request_wrapper()
def create_flow_session(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
session_id, # type: str
body, # type: Optional["_models.CreateFlowSessionRequest"]
**kwargs, # type: Any
):
from azure.core.exceptions import (
ClientAuthenticationError,
HttpResponseError,
ResourceExistsError,
ResourceNotFoundError,
map_error,
)
from promptflow.azure._restclient.flow.operations._flow_sessions_operations import (
build_create_flow_session_request,
_convert_request,
_models,
)
from promptflow.azure._constants._flow import SESSION_CREATION_TIMEOUT_SECONDS
from promptflow.azure._restclient.flow.models import SetupFlowSessionAction
headers = self._get_headers()
# pass user aml token to session create so user don't need to do authentication again in CI
self._set_headers_with_user_aml_token(headers)
# did not call self.caller.flow_sessions.create_flow_session because it does not support return headers
cls = kwargs.pop("cls", None) # type: ClsType[Any]
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
error_map.update(kwargs.pop("error_map", {}))
content_type = kwargs.pop("content_type", "application/json") # type: Optional[str]
_json = self.caller.flow_sessions._serialize.body(body, "CreateFlowSessionRequest")
request = build_create_flow_session_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
session_id=session_id,
content_type=content_type,
json=_json,
template_url=self.caller.flow_sessions.create_flow_session.metadata["url"],
headers=headers,
)
request = _convert_request(request)
request.url = self.caller.flow_sessions._client.format_url(request.url)
pipeline_response = self.caller.flow_sessions._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self.caller.flow_sessions._deserialize.failsafe_deserialize(
_models.ErrorResponse, pipeline_response
)
raise HttpResponseError(response=response, model=error)
if response.status_code == 200:
return
action = body.action or SetupFlowSessionAction.INSTALL.value
if action == SetupFlowSessionAction.INSTALL.value:
action = "creation"
else:
action = "reset"
logger.info(f"Start polling until session {action} is completed...")
# start polling status here.
if "azure-asyncoperation" not in response.headers:
raise FlowRequestException(
"No polling url found in response headers. "
f"Request id: {headers['x-ms-client-request-id']}. "
f"Response headers: {response.headers}."
)
polling_url = response.headers["azure-asyncoperation"]
time_run = 0
sleep_period = 5
status = None
timeout_seconds = SESSION_CREATION_TIMEOUT_SECONDS
# polling timeout, if user set SESSION_CREATION_TIMEOUT_SECONDS in environment var, use it
if os.environ.get(SESSION_CREATION_TIMEOUT_ENV_VAR):
try:
timeout_seconds = float(os.environ.get(SESSION_CREATION_TIMEOUT_ENV_VAR))
except ValueError:
raise UserErrorException(
"Environment variable {} with value {} set but failed to parse. "
"Please reset the value to a number.".format(
SESSION_CREATION_TIMEOUT_ENV_VAR, os.environ.get(SESSION_CREATION_TIMEOUT_ENV_VAR)
)
)
# InProgress is only known non-terminal status for now.
while status in [None, "InProgress"]:
if time_run + sleep_period > timeout_seconds:
message = (
f"Polling timeout for session {session_id} {action} "
f"for {AUTOMATIC_RUNTIME} after {timeout_seconds} seconds.\n"
f"To proceed the {action} for {AUTOMATIC_RUNTIME}, you can retry using the same flow, "
"and we will continue polling status of previous session. \n"
)
raise Exception(message)
time_run += sleep_period
time.sleep(sleep_period)
response = self.poll_operation_status(url=polling_url, **kwargs)
status = response["status"]
logger.debug(f"Current polling status: {status}")
if time_run % 30 == 0:
# print the message every 30 seconds to avoid users feeling stuck during the operation
print(f"Waiting for session {action}, current status: {status}")
else:
logger.debug(f"Waiting for session {action}, current status: {status}")
if status == "Succeeded":
error_msg = pydash.get(response, "error.message", None)
if error_msg:
logger.warning(
f"Session {action} finished with status {status}. "
f"But there are warnings when installing the packages: {error_msg}."
)
else:
logger.info(f"Session {action} finished with status {status}.")
else:
# refine response error message
try:
response["error"]["message"] = json.loads(response["error"]["message"])
except Exception:
pass
raise FlowRequestException(
f"Session {action} failed for {session_id}. \n"
f"Session {action} status: {status}. \n"
f"Request id: {headers['x-ms-client-request-id']}. \n"
f"{json.dumps(response, indent=2)}."
)
@_request_wrapper()
def poll_operation_status(
self, url, **kwargs # type: Any
):
from azure.core.rest import HttpRequest
from azure.core.exceptions import (
ClientAuthenticationError,
HttpResponseError,
ResourceExistsError,
ResourceNotFoundError,
map_error,
)
from promptflow.azure._restclient.flow.operations._flow_sessions_operations import _models
headers = self._get_headers()
request = HttpRequest(method="GET", url=url, headers=headers, **kwargs)
pipeline_response = self.caller.flow_sessions._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError}
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self.caller.flow_sessions._deserialize.failsafe_deserialize(
_models.ErrorResponse, pipeline_response
)
raise HttpResponseError(response=response, model=error)
deserialized = self.caller.flow_sessions._deserialize("object", pipeline_response)
if "status" not in deserialized:
raise FlowRequestException(
f"Status not found in response. Request id: {headers['x-ms-client-request-id']}. "
f"Response headers: {response.headers}."
)
return deserialized
@_request_wrapper()
def get_child_runs(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
index=None, # type: Optional[int]
start_index=None, # type: Optional[int]
end_index=None, # type: Optional[int]
**kwargs, # type: Any
):
"""Get child runs of a flow run."""
headers = self._get_headers()
return self.caller.bulk_runs.get_flow_child_runs(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
index=index,
start_index=start_index,
end_index=end_index,
headers=headers,
**kwargs,
)
@_request_wrapper()
def cancel_flow_run(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
**kwargs, # type: Any
):
"""Cancel a flow run."""
headers = self._get_headers()
return self.caller.bulk_runs.cancel_flow_run(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
headers=headers,
**kwargs,
)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/service_caller_factory.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from threading import Lock
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
class _FlowServiceCallerFactory:
caller_cache_by_workspace_id = {}
_instance_lock = Lock()
@classmethod
def get_instance(cls, workspace, credential, operation_scope, region=None, **kwargs) -> FlowServiceCaller:
"""Get instance of flow service caller.
:param workspace: workspace
"""
cache_id = workspace.id if workspace else region
cache = cls.caller_cache_by_workspace_id
if cache_id not in cache:
with _FlowServiceCallerFactory._instance_lock:
if cache_id not in cache:
cache[cache_id] = FlowServiceCaller(workspace, credential=credential, operation_scope=operation_scope, region=region, **kwargs)
return cache[cache_id]
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/swagger.json | {
"openapi": "3.0.1",
"info": {
"title": "Azure Machine Learning Designer Service Client",
"version": "1.0.0"
},
"paths": {
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/submit": {
"post": {
"tags": [
"BulkRuns"
],
"operationId": "BulkRuns_SubmitBulkRun",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/SubmitBulkRunRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "string"
}
}
}
},
"202": {
"description": "Accepted",
"content": {
"application/json": {
"schema": {
"type": "string"
}
}
}
},
"204": {
"description": "No Content",
"content": {
"application/json": {
"schema": {
"type": "string"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/cancel": {
"post": {
"tags": [
"BulkRuns"
],
"operationId": "BulkRuns_CancelFlowRun",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"text/plain": {
"schema": {
"type": "string"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/clone": {
"post": {
"tags": [
"BulkRuns"
],
"operationId": "BulkRuns_CloneFlowFromFlowRun",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CreateFlowRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}": {
"get": {
"tags": [
"BulkRuns"
],
"operationId": "BulkRuns_GetFlowRunInfo",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowRunInfo"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/childRuns": {
"get": {
"tags": [
"BulkRuns"
],
"operationId": "BulkRuns_GetFlowChildRuns",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "index",
"in": "query",
"schema": {
"type": "integer",
"format": "int32"
}
},
{
"name": "startIndex",
"in": "query",
"schema": {
"type": "integer",
"format": "int32"
}
},
{
"name": "endIndex",
"in": "query",
"schema": {
"type": "integer",
"format": "int32"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": { }
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/nodeRuns/{nodeName}": {
"get": {
"tags": [
"BulkRuns"
],
"operationId": "BulkRuns_GetFlowNodeRuns",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "nodeName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "index",
"in": "query",
"schema": {
"type": "integer",
"format": "int32"
}
},
{
"name": "startIndex",
"in": "query",
"schema": {
"type": "integer",
"format": "int32"
}
},
{
"name": "endIndex",
"in": "query",
"schema": {
"type": "integer",
"format": "int32"
}
},
{
"name": "aggregation",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": { }
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/nodeRuns/{nodeName}/basePath": {
"get": {
"tags": [
"BulkRuns"
],
"operationId": "BulkRuns_GetFlowNodeRunBasePath",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "nodeName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowRunBasePath"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/logContent": {
"get": {
"tags": [
"BulkRuns"
],
"operationId": "BulkRuns_GetFlowRunLogContent",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "string"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}": {
"post": {
"tags": [
"Connection"
],
"operationId": "Connection_CreateConnection",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "connectionName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CreateOrUpdateConnectionRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ConnectionEntity"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"put": {
"tags": [
"Connection"
],
"operationId": "Connection_UpdateConnection",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "connectionName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CreateOrUpdateConnectionRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ConnectionEntity"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"get": {
"tags": [
"Connection"
],
"operationId": "Connection_GetConnection",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "connectionName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ConnectionEntity"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"delete": {
"tags": [
"Connection"
],
"operationId": "Connection_DeleteConnection",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "connectionName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "connectionScope",
"in": "query",
"schema": {
"$ref": "#/components/schemas/ConnectionScope"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ConnectionEntity"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection": {
"get": {
"tags": [
"Connection"
],
"operationId": "Connection_ListConnections",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ConnectionEntity"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/specs": {
"get": {
"tags": [
"Connection"
],
"operationId": "Connection_ListConnectionSpecs",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ConnectionSpec"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}": {
"post": {
"tags": [
"Connections"
],
"operationId": "Connections_CreateConnection",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "connectionName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CreateOrUpdateConnectionRequestDto"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ConnectionDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"put": {
"tags": [
"Connections"
],
"operationId": "Connections_UpdateConnection",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "connectionName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CreateOrUpdateConnectionRequestDto"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ConnectionDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"get": {
"tags": [
"Connections"
],
"operationId": "Connections_GetConnection",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "connectionName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ConnectionDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"delete": {
"tags": [
"Connections"
],
"operationId": "Connections_DeleteConnection",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "connectionName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ConnectionDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}/listsecrets": {
"get": {
"tags": [
"Connections"
],
"operationId": "Connections_GetConnectionWithSecrets",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "connectionName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ConnectionDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections": {
"get": {
"tags": [
"Connections"
],
"operationId": "Connections_ListConnections",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ConnectionDto"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/specs": {
"get": {
"tags": [
"Connections"
],
"operationId": "Connections_ListConnectionSpecs",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/WorkspaceConnectionSpec"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}/AzureOpenAIDeployments": {
"get": {
"tags": [
"Connections"
],
"operationId": "Connections_ListAzureOpenAIDeployments",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "connectionName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AzureOpenAIDeploymentDto"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/submit": {
"post": {
"tags": [
"FlowRunsAdmin"
],
"operationId": "FlowRunsAdmin_SubmitBulkRunAsync",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "bulkRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "errorHandlingMode",
"in": "query",
"schema": {
"$ref": "#/components/schemas/ErrorHandlingMode"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/SubmitBulkRunResponse"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/policy": {
"post": {
"tags": [
"FlowRunsAdmin"
],
"operationId": "FlowRunsAdmin_SendPolicyValidationAsync",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "bulkRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/PolicyValidationResponse"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"get": {
"tags": [
"FlowRunsAdmin"
],
"operationId": "FlowRunsAdmin_CheckPolicyValidationAsync",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "bulkRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/PolicyValidationResponse"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/LogResult": {
"post": {
"tags": [
"FlowRunsAdmin"
],
"operationId": "FlowRunsAdmin_LogResultForBulkRun",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "bulkRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/KeyValuePairStringObject"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/storageInfo": {
"get": {
"tags": [
"FlowRunsAdmin"
],
"operationId": "FlowRunsAdmin_GetStorageInfo",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/StorageInfo"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/flowRuns/{flowRunId}/runtime/{runtimeVersion}/logEvent": {
"post": {
"tags": [
"FlowRunsAdmin"
],
"operationId": "FlowRunsAdmin_LogFlowRunEvent",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "runtimeVersion",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "string"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/flowRuns/{flowRunId}/logEvent": {
"post": {
"tags": [
"FlowRunsAdmin"
],
"operationId": "FlowRunsAdmin_LogFlowRunEventV2",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "runtimeVersion",
"in": "query",
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "string"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/flowRuns/{flowRunId}/logTerminatedEvent": {
"post": {
"tags": [
"FlowRunsAdmin"
],
"operationId": "FlowRunsAdmin_LogFlowRunTerminatedEvent",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "lastCheckedTime",
"in": "query",
"schema": {
"type": "string",
"format": "date-time"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/LogRunTerminatedEventDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/serviceLogs": {
"post": {
"tags": [
"FlowRunsAdmin"
],
"operationId": "FlowRunsAdmin_UpdateServiceLogs",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "bulkRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ServiceLogRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/Task"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/serviceLogs/batch": {
"post": {
"tags": [
"FlowRunsAdmin"
],
"operationId": "FlowRunsAdmin_BatchUpdateServiceLogs",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "bulkRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ServiceLogRequest"
}
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/Task"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}": {
"post": {
"tags": [
"FlowRuntimes"
],
"operationId": "FlowRuntimes_CreateRuntime",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "runtimeName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "asyncCall",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
},
{
"name": "msiToken",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
},
{
"name": "skipPortCheck",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CreateFlowRuntimeRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowRuntimeDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"put": {
"tags": [
"FlowRuntimes"
],
"operationId": "FlowRuntimes_UpdateRuntime",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "runtimeName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "asyncCall",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
},
{
"name": "msiToken",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
},
{
"name": "skipPortCheck",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/UpdateFlowRuntimeRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowRuntimeDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"get": {
"tags": [
"FlowRuntimes"
],
"operationId": "FlowRuntimes_GetRuntime",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "runtimeName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowRuntimeDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"delete": {
"tags": [
"FlowRuntimes"
],
"operationId": "FlowRuntimes_DeleteRuntime",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "runtimeName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "asyncCall",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
},
{
"name": "msiToken",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowRuntimeDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkCiAvailability": {
"get": {
"tags": [
"FlowRuntimes"
],
"operationId": "FlowRuntimes_CheckCiAvailability",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "computeInstanceName",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "customAppName",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/AvailabilityResponse"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkMirAvailability": {
"get": {
"tags": [
"FlowRuntimes"
],
"operationId": "FlowRuntimes_CheckMirAvailability",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "endpointName",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "deploymentName",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/AvailabilityResponse"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/needUpgrade": {
"get": {
"tags": [
"FlowRuntimes"
],
"operationId": "FlowRuntimes_CheckRuntimeUpgrade",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "runtimeName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "boolean"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/capability": {
"get": {
"tags": [
"FlowRuntimes"
],
"operationId": "FlowRuntimes_GetRuntimeCapability",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "runtimeName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowRuntimeCapability"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/latestConfig": {
"get": {
"tags": [
"FlowRuntimes"
],
"operationId": "FlowRuntimes_GetRuntimeLatestConfig",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/RuntimeConfiguration"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes": {
"get": {
"tags": [
"FlowRuntimes"
],
"operationId": "FlowRuntimes_ListRuntimes",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/FlowRuntimeDto"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/runtimes/latestConfig": {
"get": {
"tags": [
"FlowRuntimesWorkspaceIndependent"
],
"operationId": "FlowRuntimesWorkspaceIndependent_GetRuntimeLatestConfig",
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/RuntimeConfiguration"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_CreateFlow",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "experimentId",
"in": "query",
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CreateFlowRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_ListFlows",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "experimentId",
"in": "query",
"schema": {
"type": "string"
}
},
{
"name": "ownedOnly",
"in": "query",
"schema": {
"type": "boolean"
}
},
{
"name": "flowType",
"in": "query",
"schema": {
"$ref": "#/components/schemas/FlowType"
}
},
{
"name": "listViewType",
"in": "query",
"schema": {
"$ref": "#/components/schemas/ListViewType"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/FlowBaseDto"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/clone": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_CloneFlow",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "experimentId",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CreateFlowRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/fromsample": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_CreateFlowFromSample",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "experimentId",
"in": "query",
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CreateFlowFromSampleRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}": {
"put": {
"tags": [
"Flows"
],
"operationId": "Flows_UpdateFlow",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "experimentId",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/UpdateFlowRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "string"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"patch": {
"tags": [
"Flows"
],
"operationId": "Flows_PatchFlow",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "experimentId",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json-patch+json": {
"schema": {
"$ref": "#/components/schemas/PatchFlowRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "string"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_GetFlow",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "experimentId",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/submit": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_SubmitFlow",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "experimentId",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "endpointName",
"in": "query",
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/SubmitFlowRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowRunResult"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/{flowRunId}/status": {
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_GetFlowRunStatus",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "experimentId",
"in": "query",
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowRunResult"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}": {
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_GetFlowRunInfo",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "experimentId",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowRunInfo"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/childRuns": {
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_GetFlowChildRuns",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "index",
"in": "query",
"schema": {
"type": "integer",
"format": "int32"
}
},
{
"name": "startIndex",
"in": "query",
"schema": {
"type": "integer",
"format": "int32"
}
},
{
"name": "endIndex",
"in": "query",
"schema": {
"type": "integer",
"format": "int32"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": { }
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/nodeRuns/{nodeName}": {
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_GetFlowNodeRuns",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "nodeName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "index",
"in": "query",
"schema": {
"type": "integer",
"format": "int32"
}
},
{
"name": "startIndex",
"in": "query",
"schema": {
"type": "integer",
"format": "int32"
}
},
{
"name": "endIndex",
"in": "query",
"schema": {
"type": "integer",
"format": "int32"
}
},
{
"name": "aggregation",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": { }
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/nodeRuns/{nodeName}/basePath": {
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_GetFlowNodeRunBasePath",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "nodeName",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowRunBasePath"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/clone": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_CloneFlowFromFlowRun",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "experimentId",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CreateFlowRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/bulkTests": {
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_ListBulkTests",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "experimentId",
"in": "query",
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/BulkTestDto"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/bulkTests/{bulkTestId}": {
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_GetBulkTest",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "bulkTestId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/BulkTestDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/samples": {
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_GetSamples",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "useSnapshot",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/FlowSampleDto"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/evaluateSamples": {
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_GetEvaluateFlowSamples",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "useSnapshot",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/FlowSampleDto"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/DeployReservedEnvironmentVariableNames": {
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_GetFlowDeployReservedEnvironmentVariableNames",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"type": "string"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/deploy": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_DeployFlow",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "asyncCall",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
},
{
"name": "msiToken",
"in": "query",
"schema": {
"type": "boolean",
"default": false
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/DeployFlowRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "string"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/logContent": {
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_GetFlowRunLogContent",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "string"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/runs/{flowRunId}/cancel": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_CancelFlowRun",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"text/plain": {
"schema": {
"type": "string"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/flowTests/{flowRunId}/cancel": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_CancelFlowTest",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "flowRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"text/plain": {
"schema": {
"type": "string"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/bulkTests/{bulkTestRunId}/cancel": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_CancelBulkTestRun",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "bulkTestRunId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"text/plain": {
"schema": {
"type": "string"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/FlowSnapshot": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_GetFlowSnapshot",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CreateFlowRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowSnapshot"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/connectionOverride": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_GetConnectionOverrideSettings",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "runtimeName",
"in": "query",
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowGraphReference"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ConnectionOverrideSetting"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/flowInputs": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_GetFlowInputs",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowGraphReference"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/FlowInputDefinition"
},
"description": "This is a dictionary"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/LoadAsComponent": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_LoadAsComponent",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/LoadFlowAsComponentRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "string"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/flowTools": {
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_GetFlowTools",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "flowRuntimeName",
"in": "query",
"schema": {
"type": "string"
}
},
{
"name": "experimentId",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowToolsDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/sessions": {
"post": {
"tags": [
"Flows"
],
"operationId": "Flows_SetupFlowSession",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "experimentId",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/SetupFlowSessionRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/IActionResult"
}
}
}
},
"202": {
"description": "Accepted",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/IActionResult"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"delete": {
"tags": [
"Flows"
],
"operationId": "Flows_DeleteFlowSession",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "experimentId",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/IActionResult"
}
}
}
},
"202": {
"description": "Accepted",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/IActionResult"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/sessions/status": {
"get": {
"tags": [
"Flows"
],
"operationId": "Flows_GetFlowSessionStatus",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "flowId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "experimentId",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/FlowSessionDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}": {
"post": {
"tags": [
"FlowSessions"
],
"operationId": "FlowSessions_CreateFlowSession",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "sessionId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CreateFlowSessionRequest"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/IActionResult"
}
}
}
},
"202": {
"description": "Accepted",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/IActionResult"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"get": {
"tags": [
"FlowSessions"
],
"operationId": "FlowSessions_GetFlowSession",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "sessionId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/GetTrainingSessionDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
},
"delete": {
"tags": [
"FlowSessions"
],
"operationId": "FlowSessions_DeleteFlowSession",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "sessionId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/IActionResult"
}
}
}
},
"202": {
"description": "Accepted",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/IActionResult"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}/{actionType}/locations/{location}/operations/{operationId}": {
"get": {
"tags": [
"FlowSessions"
],
"operationId": "FlowSessions_PollOperationStatus",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "sessionId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "actionType",
"in": "path",
"required": true,
"schema": {
"$ref": "#/components/schemas/SetupFlowSessionAction"
}
},
{
"name": "location",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "operationId",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "api-version",
"in": "query",
"schema": {
"type": "string"
}
},
{
"name": "type",
"in": "query",
"schema": {
"type": "string"
}
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/IActionResult"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/standbypools": {
"get": {
"tags": [
"FlowSessions"
],
"operationId": "FlowSessions_GetStandbyPools",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "array",
"items": {
"$ref": "#/components/schemas/StandbyPoolProperties"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/v1.0/flows/getIndexEntities": {
"post": {
"tags": [
"FlowsProvider"
],
"operationId": "FlowsProvider_GetIndexEntityById",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/UnversionedEntityRequestDto"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/UnversionedEntityResponseDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/v1.0/flows/rebuildIndex": {
"post": {
"tags": [
"FlowsProvider"
],
"operationId": "FlowsProvider_GetUpdatedEntityIdsForWorkspace",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/UnversionedRebuildIndexDto"
}
}
}
},
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/UnversionedRebuildResponseDto"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/setting": {
"get": {
"tags": [
"Tools"
],
"operationId": "Tools_GetToolSetting",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ToolSetting"
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/samples": {
"get": {
"tags": [
"Tools"
],
"operationId": "Tools_GetSamples",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
}
],
"responses": {
"200": {
"description": "Success",
"content": {
"application/json": {
"schema": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/Tool"
}
}
}
}
},
"default": {
"description": "Error response describing why the operation failed.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ErrorResponse"
}
}
}
}
}
}
},
"/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/meta": {
"post": {
"tags": [
"Tools"
],
"operationId": "Tools_GetToolMeta",
"parameters": [
{
"$ref": "#/components/parameters/subscriptionIdParameter"
},
{
"$ref": "#/components/parameters/resourceGroupNameParameter"
},
{
"$ref": "#/components/parameters/workspaceNameParameter"
},
{
"name": "toolName",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "toolType",
"in": "query",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "endpointName",
"in": "query",
"schema": {
"type": "string"
}
},
{
"name": "flowRuntimeName",
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"File",
"Folder"
],
"type": "string"
},
"AetherFileSystem": {
"type": "object",
"properties": {
"connection": {
"type": "string",
"nullable": true
},
"path": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"AetherForecastHorizon": {
"type": "object",
"properties": {
"mode": {
"$ref": "#/components/schemas/AetherForecastHorizonMode"
},
"value": {
"type": "integer",
"format": "int32"
}
},
"additionalProperties": false
},
"AetherForecastHorizonMode": {
"enum": [
"Auto",
"Custom"
],
"type": "string"
},
"AetherForecastingSettings": {
"type": "object",
"properties": {
"countryOrRegionForHolidays": {
"type": "string",
"nullable": true
},
"timeColumnName": {
"type": "string",
"nullable": true
},
"targetLags": {
"$ref": "#/components/schemas/AetherTargetLags"
},
"targetRollingWindowSize": {
"$ref": "#/components/schemas/AetherTargetRollingWindowSize"
},
"forecastHorizon": {
"$ref": "#/components/schemas/AetherForecastHorizon"
},
"timeSeriesIdColumnNames": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"frequency": {
"type": "string",
"nullable": true
},
"featureLags": {
"type": "string",
"nullable": true
},
"seasonality": {
"$ref": "#/components/schemas/AetherSeasonality"
},
"shortSeriesHandlingConfig": {
"$ref": "#/components/schemas/AetherShortSeriesHandlingConfiguration"
},
"useStl": {
"$ref": "#/components/schemas/AetherUseStl"
},
"targetAggregateFunction": {
"$ref": "#/components/schemas/AetherTargetAggregationFunction"
},
"cvStepSize": {
"type": "integer",
"format": "int32",
"nullable": true
},
"featuresUnknownAtForecastTime": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
}
},
"additionalProperties": false
},
"AetherGeneralSettings": {
"type": "object",
"properties": {
"primaryMetric": {
"$ref": "#/components/schemas/AetherPrimaryMetrics"
},
"taskType": {
"$ref": "#/components/schemas/AetherTaskType"
},
"logVerbosity": {
"$ref": "#/components/schemas/AetherLogVerbosity"
}
},
"additionalProperties": false
},
"AetherGlobsOptions": {
"type": "object",
"properties": {
"globPatterns": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
}
},
"additionalProperties": false
},
"AetherGraphControlNode": {
"type": "object",
"properties": {
"id": {
"type": "string",
"nullable": true
},
"controlType": {
"$ref": "#/components/schemas/AetherControlType"
},
"controlParameter": {
"$ref": "#/components/schemas/AetherParameterAssignment"
},
"runAttribution": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"AetherGraphControlReferenceNode": {
"type": "object",
"properties": {
"id": {
"type": "string",
"nullable": true
},
"name": {
"type": "string",
"nullable": true
},
"comment": {
"type": "string",
"nullable": true
},
"controlFlowType": {
"$ref": "#/components/schemas/AetherControlFlowType"
},
"referenceNodeId": {
"type": "string",
"nullable": true
},
"doWhileControlFlowInfo": {
"$ref": "#/components/schemas/AetherDoWhileControlFlowInfo"
},
"parallelForControlFlowInfo": {
"$ref": "#/components/schemas/AetherParallelForControlFlowInfo"
},
"runAttribution": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"AetherGraphDatasetNode": {
"type": "object",
"properties": {
"id": {
"type": "string",
"nullable": true
},
"datasetId": {
"type": "string",
"nullable": true
},
"dataPathParameterName": {
"type": "string",
"nullable": true
},
"dataSetDefinition": {
"$ref": "#/components/schemas/AetherDataSetDefinition"
}
},
"additionalProperties": false
},
"AetherGraphEdge": {
"type": "object",
"properties": {
"sourceOutputPort": {
"$ref": "#/components/schemas/AetherPortInfo"
},
"destinationInputPort": {
"$ref": "#/components/schemas/AetherPortInfo"
}
},
"additionalProperties": false
},
"AetherGraphEntity": {
"type": "object",
"properties": {
"moduleNodes": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherGraphModuleNode"
},
"nullable": true
},
"datasetNodes": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherGraphDatasetNode"
},
"nullable": true
},
"subGraphNodes": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherGraphReferenceNode"
},
"nullable": true
},
"controlReferenceNodes": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherGraphControlReferenceNode"
},
"nullable": true
},
"controlNodes": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherGraphControlNode"
},
"nullable": true
},
"edges": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherGraphEdge"
},
"nullable": true
},
"defaultCompute": {
"$ref": "#/components/schemas/AetherComputeSetting"
},
"defaultDatastore": {
"$ref": "#/components/schemas/AetherDatastoreSetting"
},
"defaultCloudPriority": {
"$ref": "#/components/schemas/AetherCloudPrioritySetting"
},
"parentSubGraphModuleIds": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"id": {
"type": "string",
"nullable": true
},
"workspaceId": {
"type": "string",
"nullable": true
},
"etag": {
"type": "string",
"nullable": true
},
"tags": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"createdDate": {
"type": "string",
"format": "date-time"
},
"lastModifiedDate": {
"type": "string",
"format": "date-time"
},
"entityStatus": {
"$ref": "#/components/schemas/AetherEntityStatus"
}
},
"additionalProperties": false
},
"AetherGraphModuleNode": {
"type": "object",
"properties": {
"cloudPriority": {
"type": "integer",
"format": "int32"
},
"defaultDataRetentionHint": {
"type": "integer",
"format": "int32",
"nullable": true
},
"complianceCluster": {
"type": "string",
"nullable": true
},
"euclidWorkspaceId": {
"type": "string",
"nullable": true
},
"attachedModules": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"acceptableMachineClusters": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"customDataLocationId": {
"type": "string",
"nullable": true
},
"alertTimeoutDuration": {
"type": "string",
"format": "date-span",
"nullable": true
},
"runconfig": {
"type": "string",
"nullable": true
},
"id": {
"type": "string",
"nullable": true
},
"moduleId": {
"type": "string",
"nullable": true
},
"comment": {
"type": "string",
"nullable": true
},
"name": {
"type": "string",
"nullable": true
},
"moduleParameters": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherParameterAssignment"
},
"nullable": true
},
"moduleMetadataParameters": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherParameterAssignment"
},
"nullable": true
},
"moduleOutputSettings": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherOutputSetting"
},
"nullable": true
},
"moduleInputSettings": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherInputSetting"
},
"nullable": true
},
"useGraphDefaultCompute": {
"type": "boolean"
},
"useGraphDefaultDatastore": {
"type": "boolean"
},
"regenerateOutput": {
"type": "boolean"
},
"controlInputs": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherControlInput"
},
"nullable": true
},
"cloudSettings": {
"$ref": "#/components/schemas/AetherCloudSettings"
},
"executionPhase": {
"$ref": "#/components/schemas/AetherExecutionPhase"
},
"runAttribution": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"AetherGraphReferenceNode": {
"type": "object",
"properties": {
"graphId": {
"type": "string",
"nullable": true
},
"defaultCompute": {
"$ref": "#/components/schemas/AetherComputeSetting"
},
"defaultDatastore": {
"$ref": "#/components/schemas/AetherDatastoreSetting"
},
"id": {
"type": "string",
"nullable": true
},
"moduleId": {
"type": "string",
"nullable": true
},
"comment": {
"type": "string",
"nullable": true
},
"name": {
"type": "string",
"nullable": true
},
"moduleParameters": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherParameterAssignment"
},
"nullable": true
},
"moduleMetadataParameters": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherParameterAssignment"
},
"nullable": true
},
"moduleOutputSettings": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherOutputSetting"
},
"nullable": true
},
"moduleInputSettings": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherInputSetting"
},
"nullable": true
},
"useGraphDefaultCompute": {
"type": "boolean"
},
"useGraphDefaultDatastore": {
"type": "boolean"
},
"regenerateOutput": {
"type": "boolean"
},
"controlInputs": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherControlInput"
},
"nullable": true
},
"cloudSettings": {
"$ref": "#/components/schemas/AetherCloudSettings"
},
"executionPhase": {
"$ref": "#/components/schemas/AetherExecutionPhase"
},
"runAttribution": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"AetherHdfsReference": {
"type": "object",
"properties": {
"amlDataStoreName": {
"type": "string",
"nullable": true
},
"relativePath": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"AetherHdiClusterComputeInfo": {
"type": "object",
"properties": {
"address": {
"type": "string",
"nullable": true
},
"username": {
"type": "string",
"nullable": true
},
"password": {
"type": "string",
"nullable": true
},
"privateKey": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"AetherHdiRunConfiguration": {
"type": "object",
"properties": {
"file": {
"type": "string",
"nullable": true
},
"className": {
"type": "string",
"nullable": true
},
"files": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"archives": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"jars": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"pyFiles": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"computeName": {
"type": "string",
"nullable": true
},
"queue": {
"type": "string",
"nullable": true
},
"driverMemory": {
"type": "string",
"nullable": true
},
"driverCores": {
"type": "integer",
"format": "int32",
"nullable": true
},
"executorMemory": {
"type": "string",
"nullable": true
},
"executorCores": {
"type": "integer",
"format": "int32",
"nullable": true
},
"numberExecutors": {
"type": "integer",
"format": "int32",
"nullable": true
},
"conf": {
"type": "object",
"additionalProperties": {
"type": "string",
"nullable": true
},
"nullable": true
},
"name": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"AetherHyperDriveConfiguration": {
"type": "object",
"properties": {
"hyperDriveRunConfig": {
"type": "string",
"nullable": true
},
"primaryMetricGoal": {
"type": "string",
"nullable": true
},
"primaryMetricName": {
"type": "string",
"nullable": true
},
"arguments": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AetherArgumentAssignment"
},
"nullable": true
}
},
"additionalProperties": false
},
"AetherIdentitySetting": {
"type": "object",
"properties": {
"type": {
"$ref": "#/components/schemas/AetherIdentityType"
},
"clientId": {
"type": "string",
"format": "uuid",
"nullable": true
},
"objectId": {
"type": "string",
"format": "uuid",
"nullable": true
},
"msiResourceId": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"AetherIdentityType": {
"enum": [
"UserIdentity",
"Managed",
"AMLToken"
],
"type": "string"
},
"AetherImportDataTask": {
"type": "object",
"properties": {
"DataTransferSource": {
"$ref": "#/components/schemas/AetherDataTransferSource"
}
},
"additionalProperties": false
},
"AetherInputSetting": {
"type": "object",
"properties": {
"name": {
"type": "string",
"nullable": true
},
"dataStoreMode": {
"$ref": "#/components/schemas/AetherDataStoreMode"
},
"pathOnCompute": {
"type": "string",
"nullable": true
},
"options": {
"type": "object",
"additionalProperties": {
"type": "string"
},
"description": "This is a dictionary",
"nullable": true
},
"additionalTransformations": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"AetherInteractiveConfig": {
"type": "object",
"properties": {
"isSSHEnabled": {
"type": "boolean",
"nullable": true
},
"sshPublicKey": {
"type": "string",
"nullable": true
},
"isIPythonEnabled": {
"type": "boolean",
"nullable": true
},
"isTensorBoardEnabled": {
"type": "boolean",
"nullable": true
},
"interactivePort": {
"type": "integer",
"format": "int32",
"nullable": true
}
},
"additionalProperties": false
},
"AetherK8sConfiguration": {
"type": "object",
"properties": {
"maxRetryCount": {
"type": "integer",
"format": "int32",
"nullable": true
},
"resourceConfiguration": {
"$ref": "#/components/schemas/AetherResourceConfig"
},
"priorityConfiguration": {
"$ref": "#/components/schemas/AetherPriorityConfig"
},
"interactiveConfiguration": {
"$ref": "#/components/schemas/AetherInteractiveConfig"
}
},
"additionalProperties": false
},
"AetherLegacyDataPath": {
"type": "object",
"properties": {
"dataStoreName": {
"type": "string",
"nullable": true
},
"dataStoreMode": {
"$ref": "#/components/schemas/AetherDataStoreMode"
},
"relativePath": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"AetherLimitSettings": {
"type": "object",
"properties": {
"maxTrials": {
"type": "integer",
"format": "int32",
"nullable": true
},
"timeout": {
"type": "string",
"format": "date-span",
"nullable": true
},
"trialTimeout": {
"type": "string",
"format": "date-span",
"nullable": true
},
"maxConcurrentTrials": {
"type": "integer",
"format": "int32",
"nullable": true
},
"maxCoresPerTrial": {
"type": "integer",
"format": "int32",
"nullable": true
},
"exitScore": {
"type": "number",
"format": "double",
"nullable": true
},
"enableEarlyTermination": {
"type": "boolean",
"nullable": true
},
"maxNodes": {
"type": "integer",
"format": "int32",
"nullable": true
}
},
"additionalProperties": false
},
"AetherLogVerbosity": {
"enum": [
"NotSet",
"Debug",
"Info",
"Warning",
"Error",
"Critical"
],
"type": "string"
},
"AetherMlcComputeInfo": {
"type": "object",
"properties": {
"mlcComputeType": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"AetherModuleDeploymentSource": {
"enum": [
"Client",
"AutoDeployment",
"Vsts"
],
"type": "string"
},
"AetherModuleEntity": {
"type": "object",
"properties": {
"lastUpdatedBy": {
"$ref": "#/components/schemas/AetherCreatedBy"
},
"displayName": {
"type": "string",
"nullable": true
},
"moduleExecutionType": {
"type": "string",
"nullable": true
},
"moduleType": {
"$ref": "#/components/schemas/AetherModuleType"
},
"moduleTypeVersion": {
"type": "string",
"nullable": true
},
"resourceRequirements": {
"$ref": "#/components/schemas/AetherResourceModel"
},
"machineCluster": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"defaultComplianceCluster": {
"type": "string",
"nullable": true
},
"repositoryType": {
"$ref": "#/components/schemas/AetherRepositoryType"
},
"relativePathToSourceCode": {
"type": "string",
"nullable": true
},
"commitId": {
"type": "string",
"nullable": true
},
"codeReviewLink": {
"type": "string",
"nullable": true
},
"unitTestsAvailable": {
"type": "boolean"
},
"isCompressed": {
"type": "boolean"
},
"executionEnvironment": {
"$ref": "#/components/schemas/AetherExecutionEnvironment"
},
"isOutputMarkupEnabled": {
"type": "boolean"
},
"dockerImageId": {
"type": "string",
"nullable": true
},
"dockerImageReference": {
"type": "string",
"nullable": true
},
"dockerImageSecurityGroups": {
"type": "string",
"nullable": true
},
"extendedProperties": {
"$ref": "#/components/schemas/AetherModuleExtendedProperties"
},
"deploymentSource": {
"$ref": "#/components/schemas/AetherModuleDeploymentSource"
},
"deploymentSourceMetadata": {
"type": "string",
"nullable": true
},
"identifierHash": {
"type": "string",
"nullable": true
},
"identifierHashV2": {
"type": "string",
"nullable": true
},
"kvTags": {
"type": "object",
"additionalProperties": {
"type": "string"
},
"description": "This is a dictionary",
"nullable": true
},
"properties": {
"type": "object",
"additionalProperties": {
"type": "string"
},
"description": "This is a dictionary",
"nullable": true
},
"createdBy": {
"$ref": "#/components/schemas/AetherCreatedBy"
},
"runconfig": {
"type": "string",
"nullable": true
},
"cloudSettings": {
"$ref": "#/components/schemas/AetherCloudSettings"
},
"category": {
"type": "string",
"nullable": true
},
"stepType": {
"type": "string",
"nullable": true
},
"stage": {
"type": "string",
"nullable": true
},
"uploadState": {
"$ref": "#/components/schemas/AetherUploadState"
},
"sourceCodeLocation": {
"type": "string",
"nullable": true
},
"sizeInBytes": {
"type": "integer",
"format": "int64"
},
"downloadLocation": {
"type": "string",
"nullable": true
},
"dataLocation": {
"$ref": "#/components/schemas/AetherDataLocation"
},
"scriptingRuntimeId": {
"type": "string",
"nullable": true
},
"interfaceDocumentation": {
"$ref": "#/components/schemas/AetherEntityInterfaceDocumentation"
},
"isEyesOn": {
"type": "boolean"
},
"complianceCluster": {
"type": "string",
"nullable": true
},
"isDeterministic": {
"type": "boolean"
},
"informationUrl": {
"type": "string",
"nullable": true
},
"isExperimentIdInParameters": {
"type": "boolean"
},
"interfaceString": {
"type": "string",
"nullable": true
},
"defaultParameters": {
"type": "object",
"additionalProperties": {
"type": "string"
},
"description": "This is a dictionary",
"nullable": true
},
"structuredInterface": {
"$ref": "#/components/schemas/AetherStructuredInterface"
},
"familyId": {
"type": "string",
"nullable": true
},
"name": {
"type": "string",
"nullable": true
},
"hash": {
"type": "string",
"nullable": true
},
"description": {
"type": "string",
"nullable": true
},
"version": {
"type": "string",
"nullable": true
},
"sequenceNumberInFamily": {
"type": "integer",
"format": "int32"
},
"owner": {
"type": "string",
"nullable": true
},
"azureTenantId": {
"type": "string",
"nullable": true
},
"azureUserId": {
"type": "string",
"nullable": true
},
"collaborators": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"id": {
"type": "string",
"nullable": true
},
"workspaceId": {
"type": "string",
"nullable": true
},
"etag": {
"type": "string",
"nullable": true
},
"tags": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"createdDate": {
"type": "string",
"format": "date-time"
},
"lastModifiedDate": {
"type": "string",
"format": "date-time"
},
"entityStatus": {
"$ref": "#/components/schemas/AetherEntityStatus"
}
},
"additionalProperties": false
},
"AetherModuleExtendedProperties": {
"type": "object",
"properties": {
"autoDeployedArtifact": {
"$ref": "#/components/schemas/AetherBuildArtifactInfo"
},
"scriptNeedsApproval": {
"type": "boolean"
}
},
"additionalProperties": false
},
"AetherModuleHashVersion": {
"enum": [
"IdentifierHash",
"IdentifierHashV2"
],
"type": "string"
},
"AetherModuleType": {
"enum": [
"None",
"BatchInferencing"
],
"type": "string"
},
"AetherNCrossValidationMode": {
"enum": [
"Auto",
"Custom"
],
"type": "string"
},
"AetherNCrossValidations": {
"type": "object",
"properties": {
"mode": {
"$ref": "#/components/schemas/AetherNCrossValidationMode"
},
"value": {
"type": "integer",
"format": "int32"
}
},
"additionalProperties": false
},
"AetherOutputSetting": {
"type": "object",
"properties": {
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},
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},
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},
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"$ref": "#/components/schemas/ParameterAssignment"
}
},
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},
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],
"type": "string"
},
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"properties": {
"name": {
"type": "string",
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},
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"type": "boolean"
},
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"type": "string",
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},
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"additionalProperties": {
"type": "string",
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},
"nullable": true
},
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"type": "string",
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}
},
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},
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"type": "string",
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}
},
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},
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}
},
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}
},
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},
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},
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},
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},
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}
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},
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},
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},
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"type": "string"
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"description": "This is a dictionary",
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},
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"type": "string"
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"description": "This is a dictionary",
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},
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"type": "string"
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"description": "This is a dictionary",
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},
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},
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}
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"$ref": "#/components/schemas/DistributionType"
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},
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"type": "string"
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},
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},
"yamlStr": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"ModuleDtoFields": {
"enum": [
"Definition",
"YamlStr",
"RegistrationContext",
"RunSettingParameters",
"RunDefinition",
"All",
"Default",
"Basic",
"Minimal"
],
"type": "string"
},
"ModuleDtoWithErrors": {
"type": "object",
"properties": {
"versionIdToModuleDto": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/ModuleDto"
},
"description": "This is a dictionary",
"nullable": true
},
"nameAndVersionToModuleDto": {
"type": "array",
"items": {
"$ref": "#/components/schemas/KeyValuePairComponentNameMetaInfoModuleDto"
},
"nullable": true
},
"versionIdToError": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/ErrorResponse"
},
"description": "This is a dictionary",
"nullable": true
},
"nameAndVersionToError": {
"type": "array",
"items": {
"$ref": "#/components/schemas/KeyValuePairComponentNameMetaInfoErrorResponse"
},
"nullable": true
}
},
"additionalProperties": false
},
"ModuleDtoWithValidateStatus": {
"type": "object",
"properties": {
"existingModuleEntity": {
"$ref": "#/components/schemas/ModuleEntity"
},
"status": {
"$ref": "#/components/schemas/ModuleInfoFromYamlStatusEnum"
},
"statusDetails": {
"type": "string",
"nullable": true
},
"errorDetails": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"serializedModuleInfo": {
"type": "string",
"nullable": true
},
"namespace": {
"type": "string",
"nullable": true
},
"tags": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"displayName": {
"type": "string",
"nullable": true
},
"dictTags": {
"type": "object",
"additionalProperties": {
"type": "string",
"nullable": true
},
"nullable": true
},
"moduleVersionId": {
"type": "string",
"nullable": true
},
"feedName": {
"type": "string",
"nullable": true
},
"registryName": {
"type": "string",
"nullable": true
},
"moduleName": {
"type": "string",
"nullable": true
},
"moduleVersion": {
"type": "string",
"nullable": true
},
"description": {
"type": "string",
"nullable": true
},
"owner": {
"type": "string",
"nullable": true
},
"jobType": {
"type": "string",
"nullable": true
},
"defaultVersion": {
"type": "string",
"nullable": true
},
"familyId": {
"type": "string",
"nullable": true
},
"helpDocument": {
"type": "string",
"nullable": true
},
"codegenBy": {
"type": "string",
"nullable": true
},
"armId": {
"type": "string",
"nullable": true
},
"moduleScope": {
"$ref": "#/components/schemas/ModuleScope"
},
"moduleEntity": {
"$ref": "#/components/schemas/ModuleEntity"
},
"inputTypes": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"outputTypes": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"entityStatus": {
"$ref": "#/components/schemas/EntityStatus"
},
"createdDate": {
"type": "string",
"format": "date-time"
},
"lastModifiedDate": {
"type": "string",
"format": "date-time"
},
"yamlLink": {
"type": "string",
"nullable": true
},
"yamlLinkWithCommitSha": {
"type": "string",
"nullable": true
},
"moduleSourceType": {
"$ref": "#/components/schemas/ModuleSourceType"
},
"registeredBy": {
"type": "string",
"nullable": true
},
"versions": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AzureMLModuleVersionDescriptor"
},
"nullable": true
},
"isDefaultModuleVersion": {
"type": "boolean",
"nullable": true
},
"systemData": {
"$ref": "#/components/schemas/SystemData"
},
"systemMeta": {
"$ref": "#/components/schemas/SystemMeta"
},
"snapshotId": {
"type": "string",
"nullable": true
},
"entry": {
"type": "string",
"nullable": true
},
"osType": {
"type": "string",
"nullable": true
},
"requireGpu": {
"type": "boolean",
"nullable": true
},
"modulePythonInterface": {
"$ref": "#/components/schemas/ModulePythonInterface"
},
"environmentAssetId": {
"type": "string",
"nullable": true
},
"runSettingParameters": {
"type": "array",
"items": {
"$ref": "#/components/schemas/RunSettingParameter"
},
"nullable": true
},
"supportedUIInputDataDeliveryModes": {
"type": "object",
"additionalProperties": {
"type": "array",
"items": {
"$ref": "#/components/schemas/UIInputDataDeliveryMode"
},
"nullable": true
},
"nullable": true
},
"outputSettingSpecs": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/OutputSettingSpec"
},
"nullable": true
},
"yamlStr": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"ModuleEntity": {
"type": "object",
"properties": {
"displayName": {
"type": "string",
"nullable": true
},
"moduleExecutionType": {
"type": "string",
"nullable": true
},
"moduleType": {
"$ref": "#/components/schemas/ModuleType"
},
"moduleTypeVersion": {
"type": "string",
"nullable": true
},
"uploadState": {
"$ref": "#/components/schemas/UploadState"
},
"isDeterministic": {
"type": "boolean"
},
"structuredInterface": {
"$ref": "#/components/schemas/StructuredInterface"
},
"dataLocation": {
"$ref": "#/components/schemas/DataLocation"
},
"identifierHash": {
"type": "string",
"nullable": true
},
"identifierHashV2": {
"type": "string",
"nullable": true
},
"tags": {
"type": "object",
"additionalProperties": {
"type": "string"
},
"description": "This is a dictionary",
"nullable": true
},
"properties": {
"type": "object",
"additionalProperties": {
"type": "string"
},
"description": "This is a dictionary",
"nullable": true
},
"createdBy": {
"$ref": "#/components/schemas/CreatedBy"
},
"lastUpdatedBy": {
"$ref": "#/components/schemas/CreatedBy"
},
"runconfig": {
"type": "string",
"nullable": true
},
"cloudSettings": {
"$ref": "#/components/schemas/CloudSettings"
},
"category": {
"type": "string",
"nullable": true
},
"stepType": {
"type": "string",
"nullable": true
},
"stage": {
"type": "string",
"nullable": true
},
"name": {
"type": "string",
"nullable": true
},
"hash": {
"type": "string",
"nullable": true
},
"description": {
"type": "string",
"nullable": true
},
"entityStatus": {
"$ref": "#/components/schemas/EntityStatus"
},
"id": {
"type": "string",
"nullable": true
},
"etag": {
"type": "string",
"nullable": true
},
"createdDate": {
"type": "string",
"format": "date-time"
},
"lastModifiedDate": {
"type": "string",
"format": "date-time"
}
},
"additionalProperties": false
},
"ModuleInfoFromYamlStatusEnum": {
"enum": [
"NewModule",
"NewVersion",
"Conflict",
"ParseError",
"ProcessRequestError"
],
"type": "string"
},
"ModulePythonInterface": {
"type": "object",
"properties": {
"inputs": {
"type": "array",
"items": {
"$ref": "#/components/schemas/PythonInterfaceMapping"
},
"nullable": true
},
"outputs": {
"type": "array",
"items": {
"$ref": "#/components/schemas/PythonInterfaceMapping"
},
"nullable": true
},
"parameters": {
"type": "array",
"items": {
"$ref": "#/components/schemas/PythonInterfaceMapping"
},
"nullable": true
}
},
"additionalProperties": false
},
"ModuleRunSettingTypes": {
"enum": [
"All",
"Released",
"Default",
"Testing",
"Legacy",
"Preview",
"UxFull",
"Integration",
"UxIntegration",
"Full"
],
"type": "string"
},
"ModuleScope": {
"enum": [
"All",
"Global",
"Workspace",
"Anonymous",
"Step",
"Draft",
"Feed",
"Registry",
"SystemAutoCreated"
],
"type": "string"
},
"ModuleSourceType": {
"enum": [
"Unknown",
"Local",
"GithubFile",
"GithubFolder",
"DevopsArtifactsZip",
"SerializedModuleInfo"
],
"type": "string"
},
"ModuleType": {
"enum": [
"None",
"BatchInferencing"
],
"type": "string"
},
"ModuleUpdateOperationType": {
"enum": [
"SetDefaultVersion",
"EnableModule",
"DisableModule",
"UpdateDisplayName",
"UpdateDescription",
"UpdateTags"
],
"type": "string"
},
"ModuleWorkingMechanism": {
"enum": [
"Normal",
"OutputToDataset"
],
"type": "string"
},
"MpiConfiguration": {
"type": "object",
"properties": {
"processCountPerNode": {
"type": "integer",
"format": "int32"
}
},
"additionalProperties": false
},
"NCrossValidationMode": {
"enum": [
"Auto",
"Custom"
],
"type": "string"
},
"NCrossValidations": {
"type": "object",
"properties": {
"mode": {
"$ref": "#/components/schemas/NCrossValidationMode"
},
"value": {
"type": "integer",
"format": "int32"
}
},
"additionalProperties": false
},
"Node": {
"type": "object",
"properties": {
"name": {
"type": "string",
"nullable": true
},
"type": {
"$ref": "#/components/schemas/ToolType"
},
"source": {
"$ref": "#/components/schemas/NodeSource"
},
"inputs": {
"type": "object",
"additionalProperties": {
"nullable": true
},
"nullable": true
},
"tool": {
"type": "string",
"nullable": true
},
"reduce": {
"type": "boolean"
},
"activate": {
"$ref": "#/components/schemas/Activate"
},
"comment": {
"type": "string",
"nullable": true
},
"api": {
"type": "string",
"nullable": true
},
"provider": {
"type": "string",
"nullable": true
},
"connection": {
"type": "string",
"nullable": true
},
"module": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"NodeCompositionMode": {
"enum": [
"None",
"OnlySequential",
"Full"
],
"type": "string"
},
"NodeInputPort": {
"type": "object",
"properties": {
"name": {
"type": "string",
"nullable": true
},
"documentation": {
"type": "string",
"nullable": true
},
"dataTypesIds": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"isOptional": {
"type": "boolean"
}
},
"additionalProperties": false
},
"NodeLayout": {
"type": "object",
"properties": {
"x": {
"type": "number",
"format": "float"
},
"y": {
"type": "number",
"format": "float"
},
"width": {
"type": "number",
"format": "float"
},
"height": {
"type": "number",
"format": "float"
},
"extendedData": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"NodeOutputPort": {
"type": "object",
"properties": {
"name": {
"type": "string",
"nullable": true
},
"documentation": {
"type": "string",
"nullable": true
},
"dataTypeId": {
"type": "string",
"nullable": true
},
"passThroughInputName": {
"type": "string",
"nullable": true
},
"EarlyAvailable": {
"type": "boolean"
},
"dataStoreMode": {
"$ref": "#/components/schemas/AEVADataStoreMode"
}
},
"additionalProperties": false
},
"NodePortInterface": {
"type": "object",
"properties": {
"inputs": {
"type": "array",
"items": {
"$ref": "#/components/schemas/NodeInputPort"
},
"nullable": true
},
"outputs": {
"type": "array",
"items": {
"$ref": "#/components/schemas/NodeOutputPort"
},
"nullable": true
},
"controlOutputs": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ControlOutput"
},
"nullable": true
}
},
"additionalProperties": false
},
"NodeSource": {
"type": "object",
"properties": {
"type": {
"type": "string",
"nullable": true
},
"tool": {
"type": "string",
"nullable": true
},
"path": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"NodeTelemetryMetaInfo": {
"type": "object",
"properties": {
"pipelineRunId": {
"type": "string",
"nullable": true
},
"nodeId": {
"type": "string",
"nullable": true
},
"versionId": {
"type": "string",
"nullable": true
},
"nodeType": {
"type": "string",
"nullable": true
},
"nodeSource": {
"type": "string",
"nullable": true
},
"isAnonymous": {
"type": "boolean"
},
"isPipelineComponent": {
"type": "boolean"
}
},
"additionalProperties": false
},
"NodeVariant": {
"type": "object",
"properties": {
"variants": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/VariantNode"
},
"description": "This is a dictionary",
"nullable": true
},
"defaultVariantId": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"Nodes": {
"required": [
"nodes_value_type"
],
"type": "object",
"properties": {
"nodes_value_type": {
"$ref": "#/components/schemas/NodesValueType"
},
"values": {
"type": "array",
"items": {
"type": "integer",
"format": "int32"
},
"nullable": true
}
},
"additionalProperties": false
},
"NodesValueType": {
"enum": [
"All",
"Custom"
],
"type": "string"
},
"NoteBookTaskDto": {
"type": "object",
"properties": {
"notebook_path": {
"type": "string",
"nullable": true
},
"base_parameters": {
"type": "object",
"additionalProperties": {
"type": "string",
"nullable": true
},
"nullable": true
}
},
"additionalProperties": false
},
"NotificationSetting": {
"type": "object",
"properties": {
"emails": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"emailOn": {
"type": "array",
"items": {
"$ref": "#/components/schemas/EmailNotificationEnableType"
},
"nullable": true
},
"webhooks": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/Webhook"
},
"nullable": true
}
},
"additionalProperties": false
},
"ODataError": {
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "Gets or sets a language-independent, service-defined error code.\r\nThis code serves as a sub-status for the HTTP error code specified\r\nin the response.",
"nullable": true
},
"message": {
"type": "string",
"description": "Gets or sets a human-readable, language-dependent representation of the error.\r\nThe `Content-Language` header MUST contain the language code from [RFC5646]\r\ncorresponding to the language in which the value for message is written.",
"nullable": true
},
"target": {
"type": "string",
"description": "Gets or sets the target of the particular error\r\n(for example, the name of the property in error).",
"nullable": true
},
"details": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ODataErrorDetail"
},
"description": "Gets or sets additional details about the error.",
"nullable": true
},
"innererror": {
"$ref": "#/components/schemas/ODataInnerError"
}
},
"additionalProperties": false,
"description": "Represents OData v4 error object."
},
"ODataErrorDetail": {
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "Gets or sets a language-independent, service-defined error code.",
"nullable": true
},
"message": {
"type": "string",
"description": "Gets or sets a human-readable, language-dependent representation of the error.",
"nullable": true
},
"target": {
"type": "string",
"description": "Gets or sets the target of the particular error\r\n(for example, the name of the property in error).",
"nullable": true
}
},
"additionalProperties": false,
"description": "Represents additional error details."
},
"ODataErrorResponse": {
"type": "object",
"properties": {
"error": {
"$ref": "#/components/schemas/ODataError"
}
},
"additionalProperties": false,
"description": "Represents OData v4 compliant error response message."
},
"ODataInnerError": {
"type": "object",
"properties": {
"clientRequestId": {
"type": "string",
"description": "Gets or sets the client provided request ID.",
"nullable": true
},
"serviceRequestId": {
"type": "string",
"description": "Gets or sets the server generated request ID.",
"nullable": true
},
"trace": {
"type": "string",
"description": "Gets or sets the exception stack trace.\r\nDO NOT INCLUDE IT IN PRODUCTION ENVIRONMENT.",
"nullable": true
},
"context": {
"type": "string",
"description": "Gets or sets additional context for the exception.\r\nDO NOT INCLUDE IT IN PRODUCTION ENVIRONMENT.",
"nullable": true
}
},
"additionalProperties": false,
"description": "The contents of this object are service-defined.\r\nUsually this object contains information that will help debug the service\r\nand SHOULD only be used in development environments in order to guard\r\nagainst potential security concerns around information disclosure."
},
"Orientation": {
"enum": [
"Horizontal",
"Vertical"
],
"type": "string"
},
"OutputData": {
"type": "object",
"properties": {
"outputLocation": {
"$ref": "#/components/schemas/ExecutionDataLocation"
},
"mechanism": {
"$ref": "#/components/schemas/OutputMechanism"
},
"additionalOptions": {
"$ref": "#/components/schemas/OutputOptions"
},
"environmentVariableName": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"OutputDataBinding": {
"type": "object",
"properties": {
"datastoreId": {
"type": "string",
"nullable": true
},
"pathOnDatastore": {
"type": "string",
"nullable": true
},
"pathOnCompute": {
"type": "string",
"nullable": true
},
"description": {
"type": "string",
"nullable": true
},
"uri": {
"$ref": "#/components/schemas/MfeInternalUriReference"
},
"mode": {
"$ref": "#/components/schemas/DataBindingMode"
},
"assetUri": {
"type": "string",
"nullable": true
},
"isAssetJobOutput": {
"type": "boolean",
"nullable": true
},
"jobOutputType": {
"$ref": "#/components/schemas/JobOutputType"
},
"assetName": {
"type": "string",
"nullable": true
},
"assetVersion": {
"type": "string",
"nullable": true
},
"autoDeleteSetting": {
"$ref": "#/components/schemas/AutoDeleteSetting"
}
},
"additionalProperties": false
},
"OutputDatasetLineage": {
"type": "object",
"properties": {
"identifier": {
"$ref": "#/components/schemas/DatasetIdentifier"
},
"outputType": {
"$ref": "#/components/schemas/DatasetOutputType"
},
"outputDetails": {
"$ref": "#/components/schemas/DatasetOutputDetails"
}
},
"additionalProperties": false
},
"OutputDefinition": {
"type": "object",
"properties": {
"name": {
"type": "string",
"nullable": true
},
"type": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ValueType"
},
"nullable": true
},
"description": {
"type": "string",
"nullable": true
},
"isProperty": {
"type": "boolean"
}
},
"additionalProperties": false
},
"OutputMechanism": {
"enum": [
"Upload",
"Mount",
"Hdfs",
"Link",
"Direct"
],
"type": "string"
},
"OutputOptions": {
"type": "object",
"properties": {
"pathOnCompute": {
"type": "string",
"nullable": true
},
"registrationOptions": {
"$ref": "#/components/schemas/RegistrationOptions"
},
"uploadOptions": {
"$ref": "#/components/schemas/UploadOptions"
},
"mountOptions": {
"type": "object",
"additionalProperties": {
"type": "string",
"nullable": true
},
"nullable": true
}
},
"additionalProperties": false
},
"OutputSetting": {
"type": "object",
"properties": {
"name": {
"type": "string",
"nullable": true
},
"dataStoreName": {
"type": "string",
"nullable": true
},
"DataStoreNameParameterAssignment": {
"$ref": "#/components/schemas/ParameterAssignment"
},
"dataStoreMode": {
"$ref": "#/components/schemas/AEVADataStoreMode"
},
"DataStoreModeParameterAssignment": {
"$ref": "#/components/schemas/ParameterAssignment"
},
"pathOnCompute": {
"type": "string",
"nullable": true
},
"PathOnComputeParameterAssignment": {
"$ref": "#/components/schemas/ParameterAssignment"
},
"overwrite": {
"type": "boolean"
},
"dataReferenceName": {
"type": "string",
"nullable": true
},
"webServicePort": {
"type": "string",
"nullable": true
},
"datasetRegistration": {
"$ref": "#/components/schemas/DatasetRegistration"
},
"datasetOutputOptions": {
"$ref": "#/components/schemas/DatasetOutputOptions"
},
"AssetOutputSettings": {
"$ref": "#/components/schemas/AssetOutputSettings"
},
"parameterName": {
"type": "string",
"nullable": true
},
"AssetOutputSettingsParameterName": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"OutputSettingSpec": {
"type": "object",
"properties": {
"supportedDataStoreModes": {
"type": "array",
"items": {
"$ref": "#/components/schemas/AEVADataStoreMode"
},
"nullable": true
},
"defaultAssetOutputPath": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"PaginatedDataInfoList": {
"type": "object",
"properties": {
"value": {
"type": "array",
"items": {
"$ref": "#/components/schemas/DataInfo"
},
"description": "An array of objects of type DataInfo.",
"nullable": true
},
"continuationToken": {
"type": "string",
"description": "The token used in retrieving the next page. If null, there are no additional pages.",
"nullable": true
},
"nextLink": {
"type": "string",
"description": "The link to the next page constructed using the continuationToken. If null, there are no additional pages.",
"nullable": true
}
},
"additionalProperties": false,
"description": "A paginated list of DataInfos."
},
"PaginatedModelDtoList": {
"type": "object",
"properties": {
"value": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ModelDto"
},
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},
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},
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},
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"$ref": "#/components/schemas/Compute"
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"$ref": "#/components/schemas/User"
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}
},
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},
"icon": {
"nullable": true
},
"category": {
"type": "string",
"nullable": true
},
"tags": {
"type": "object",
"additionalProperties": { },
"description": "This is a dictionary",
"nullable": true
},
"is_builtin": {
"type": "boolean"
},
"package": {
"type": "string",
"nullable": true
},
"package_version": {
"type": "string",
"nullable": true
},
"default_prompt": {
"type": "string",
"nullable": true
},
"enable_kwargs": {
"type": "boolean"
},
"deprecated_tools": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"tool_state": {
"$ref": "#/components/schemas/ToolState"
}
},
"additionalProperties": false
},
"ToolFuncCallScenario": {
"enum": [
"generated_by",
"reverse_generated_by",
"dynamic_list"
],
"type": "string"
},
"ToolFuncResponse": {
"type": "object",
"properties": {
"result": {
"nullable": true
},
"logs": {
"type": "object",
"additionalProperties": {
"type": "string"
},
"description": "This is a dictionary",
"nullable": true
}
},
"additionalProperties": false
},
"ToolInputDynamicList": {
"type": "object",
"properties": {
"func_path": {
"type": "string",
"nullable": true
},
"func_kwargs": {
"type": "array",
"items": {
"type": "object",
"additionalProperties": { },
"description": "This is a dictionary"
},
"nullable": true
}
},
"additionalProperties": false
},
"ToolInputGeneratedBy": {
"type": "object",
"properties": {
"func_path": {
"type": "string",
"nullable": true
},
"func_kwargs": {
"type": "array",
"items": {
"type": "object",
"additionalProperties": { },
"description": "This is a dictionary"
},
"nullable": true
},
"reverse_func_path": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"ToolMetaDto": {
"type": "object",
"properties": {
"tools": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/Tool"
},
"description": "This is a dictionary",
"nullable": true
},
"errors": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/ErrorResponse"
},
"description": "This is a dictionary",
"nullable": true
}
},
"additionalProperties": false
},
"ToolSetting": {
"type": "object",
"properties": {
"providers": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ProviderEntity"
},
"nullable": true
}
},
"additionalProperties": false
},
"ToolSourceMeta": {
"type": "object",
"properties": {
"tool_type": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"ToolState": {
"enum": [
"Stable",
"Preview",
"Deprecated"
],
"type": "string"
},
"ToolType": {
"enum": [
"llm",
"python",
"action",
"prompt",
"custom_llm",
"csharp"
],
"type": "string"
},
"TorchDistributedConfiguration": {
"type": "object",
"properties": {
"processCountPerNode": {
"type": "integer",
"format": "int32",
"nullable": true
}
},
"additionalProperties": false
},
"TrainingDiagnosticConfiguration": {
"type": "object",
"properties": {
"jobHeartBeatTimeoutSeconds": {
"type": "integer",
"format": "int32",
"nullable": true
}
},
"additionalProperties": false
},
"TrainingOutput": {
"type": "object",
"properties": {
"trainingOutputType": {
"$ref": "#/components/schemas/TrainingOutputType"
},
"iteration": {
"type": "integer",
"format": "int32",
"nullable": true
},
"metric": {
"type": "string",
"nullable": true
},
"modelFile": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"TrainingOutputType": {
"enum": [
"Metrics",
"Model"
],
"type": "string"
},
"TrainingSettings": {
"type": "object",
"properties": {
"blockListModels": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"allowListModels": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"enableDnnTraining": {
"type": "boolean",
"nullable": true
},
"enableOnnxCompatibleModels": {
"type": "boolean",
"nullable": true
},
"stackEnsembleSettings": {
"$ref": "#/components/schemas/StackEnsembleSettings"
},
"enableStackEnsemble": {
"type": "boolean",
"nullable": true
},
"enableVoteEnsemble": {
"type": "boolean",
"nullable": true
},
"ensembleModelDownloadTimeout": {
"type": "string",
"format": "date-span",
"nullable": true
},
"enableModelExplainability": {
"type": "boolean",
"nullable": true
},
"trainingMode": {
"$ref": "#/components/schemas/TabularTrainingMode"
}
},
"additionalProperties": false
},
"TriggerAsyncOperationStatus": {
"type": "object",
"properties": {
"id": {
"type": "string",
"nullable": true
},
"operationType": {
"$ref": "#/components/schemas/TriggerOperationType"
},
"provisioningStatus": {
"$ref": "#/components/schemas/ScheduleProvisioningStatus"
},
"createdTime": {
"type": "string",
"format": "date-time",
"nullable": true
},
"endTime": {
"type": "string",
"format": "date-time",
"nullable": true
},
"error": {
"$ref": "#/components/schemas/ErrorResponse"
},
"statusCode": {
"$ref": "#/components/schemas/HttpStatusCode"
}
},
"additionalProperties": false
},
"TriggerOperationType": {
"enum": [
"Create",
"Update",
"Delete",
"CreateOrUpdate"
],
"type": "string"
},
"TriggerType": {
"enum": [
"Recurrence",
"Cron"
],
"type": "string"
},
"TuningNodeSetting": {
"type": "object",
"properties": {
"variantIds": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
}
},
"additionalProperties": false
},
"TypedAssetReference": {
"type": "object",
"properties": {
"assetId": {
"type": "string",
"nullable": true
},
"type": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"UIAzureOpenAIDeploymentNameSelector": {
"type": "object",
"properties": {
"Capabilities": {
"$ref": "#/components/schemas/UIAzureOpenAIModelCapabilities"
}
},
"additionalProperties": false
},
"UIAzureOpenAIModelCapabilities": {
"type": "object",
"properties": {
"Completion": {
"type": "boolean",
"nullable": true
},
"ChatCompletion": {
"type": "boolean",
"nullable": true
},
"Embeddings": {
"type": "boolean",
"nullable": true
}
},
"additionalProperties": false
},
"UIColumnPicker": {
"type": "object",
"properties": {
"columnPickerFor": {
"type": "string",
"nullable": true
},
"columnSelectionCategories": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"singleColumnSelection": {
"type": "boolean"
}
},
"additionalProperties": false
},
"UIComputeSelection": {
"type": "object",
"properties": {
"computeTypes": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"requireGpu": {
"type": "boolean",
"nullable": true
},
"osTypes": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"supportServerless": {
"type": "boolean"
},
"computeRunSettingsMapping": {
"type": "object",
"additionalProperties": {
"type": "array",
"items": {
"$ref": "#/components/schemas/RunSettingParameter"
},
"nullable": true
},
"nullable": true
}
},
"additionalProperties": false
},
"UIHyperparameterConfiguration": {
"type": "object",
"properties": {
"modelNameToHyperParameterAndDistributionMapping": {
"type": "object",
"additionalProperties": {
"type": "object",
"additionalProperties": {
"type": "array",
"items": {
"type": "string"
}
},
"nullable": true
},
"nullable": true
},
"distributionParametersMapping": {
"type": "object",
"additionalProperties": {
"type": "array",
"items": {
"$ref": "#/components/schemas/DistributionParameter"
},
"nullable": true
},
"nullable": true
},
"jsonSchema": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"UIInputDataDeliveryMode": {
"enum": [
"Read-only mount",
"Read-write mount",
"Download",
"Direct",
"Evaluate mount",
"Evaluate download",
"Hdfs"
],
"type": "string"
},
"UIInputSetting": {
"type": "object",
"properties": {
"name": {
"type": "string",
"nullable": true
},
"dataDeliveryMode": {
"$ref": "#/components/schemas/UIInputDataDeliveryMode"
},
"pathOnCompute": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"UIJsonEditor": {
"type": "object",
"properties": {
"jsonSchema": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"UIParameterHint": {
"type": "object",
"properties": {
"uiWidgetType": {
"$ref": "#/components/schemas/UIWidgetTypeEnum"
},
"columnPicker": {
"$ref": "#/components/schemas/UIColumnPicker"
},
"uiScriptLanguage": {
"$ref": "#/components/schemas/UIScriptLanguageEnum"
},
"jsonEditor": {
"$ref": "#/components/schemas/UIJsonEditor"
},
"PromptFlowConnectionSelector": {
"$ref": "#/components/schemas/UIPromptFlowConnectionSelector"
},
"AzureOpenAIDeploymentNameSelector": {
"$ref": "#/components/schemas/UIAzureOpenAIDeploymentNameSelector"
},
"UxIgnore": {
"type": "boolean"
},
"Anonymous": {
"type": "boolean"
}
},
"additionalProperties": false
},
"UIPromptFlowConnectionSelector": {
"type": "object",
"properties": {
"PromptFlowConnectionType": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"UIScriptLanguageEnum": {
"enum": [
"None",
"Python",
"R",
"Json",
"Sql"
],
"type": "string"
},
"UIWidgetMetaInfo": {
"type": "object",
"properties": {
"moduleNodeId": {
"type": "string",
"nullable": true
},
"metaModuleId": {
"type": "string",
"nullable": true
},
"parameterName": {
"type": "string",
"nullable": true
},
"uiWidgetType": {
"$ref": "#/components/schemas/UIWidgetTypeEnum"
}
},
"additionalProperties": false
},
"UIWidgetTypeEnum": {
"enum": [
"Default",
"Mode",
"ColumnPicker",
"Credential",
"Script",
"ComputeSelection",
"JsonEditor",
"SearchSpaceParameter",
"SectionToggle",
"YamlEditor",
"EnableRuntimeSweep",
"DataStoreSelection",
"InstanceTypeSelection",
"ConnectionSelection",
"PromptFlowConnectionSelection",
"AzureOpenAIDeploymentNameSelection"
],
"type": "string"
},
"UIYamlEditor": {
"type": "object",
"properties": {
"jsonSchema": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"UnversionedEntityRequestDto": {
"type": "object",
"properties": {
"unversionedEntityIds": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
}
},
"additionalProperties": false
},
"UnversionedEntityResponseDto": {
"type": "object",
"properties": {
"unversionedEntities": {
"type": "array",
"items": {
"$ref": "#/components/schemas/FlowIndexEntity"
},
"nullable": true
},
"unversionedEntityJsonSchema": {
"nullable": true
},
"normalizedRequestCharge": {
"type": "number",
"format": "double"
},
"normalizedRequestChargePeriod": {
"type": "string",
"format": "date-span"
}
},
"additionalProperties": false
},
"UnversionedRebuildIndexDto": {
"type": "object",
"properties": {
"continuationToken": {
"type": "string",
"nullable": true
},
"entityCount": {
"type": "integer",
"format": "int32",
"nullable": true
},
"entityContainerType": {
"type": "string",
"nullable": true
},
"entityType": {
"type": "string",
"nullable": true
},
"resourceId": {
"type": "string",
"nullable": true
},
"workspaceId": {
"type": "string",
"nullable": true
},
"immutableResourceId": {
"type": "string",
"format": "uuid"
},
"startTime": {
"type": "string",
"format": "date-time",
"nullable": true
},
"endTime": {
"type": "string",
"format": "date-time",
"nullable": true
}
},
"additionalProperties": false
},
"UnversionedRebuildResponseDto": {
"type": "object",
"properties": {
"entities": {
"$ref": "#/components/schemas/SegmentedResult`1"
},
"unversionedEntitySchema": {
"nullable": true
},
"normalizedRequestCharge": {
"type": "number",
"format": "double"
},
"normalizedRequestChargePeriod": {
"type": "string",
"format": "date-span"
}
},
"additionalProperties": false
},
"UpdateComponentRequest": {
"type": "object",
"properties": {
"displayName": {
"type": "string",
"nullable": true
},
"description": {
"type": "string",
"nullable": true
},
"tags": {
"type": "object",
"additionalProperties": {
"type": "string",
"nullable": true
},
"nullable": true
},
"moduleUpdateOperationType": {
"$ref": "#/components/schemas/ModuleUpdateOperationType"
},
"moduleVersion": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"UpdateFlowRequest": {
"type": "object",
"properties": {
"flowRunResult": {
"$ref": "#/components/schemas/FlowRunResult"
},
"flowTestMode": {
"$ref": "#/components/schemas/FlowTestMode"
},
"flowTestInfos": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/FlowTestInfo"
},
"nullable": true
},
"flowName": {
"type": "string",
"nullable": true
},
"description": {
"type": "string",
"nullable": true
},
"details": {
"type": "string",
"nullable": true
},
"tags": {
"type": "object",
"additionalProperties": {
"type": "string",
"nullable": true
},
"nullable": true
},
"flow": {
"$ref": "#/components/schemas/Flow"
},
"flowDefinitionFilePath": {
"type": "string",
"nullable": true
},
"flowType": {
"$ref": "#/components/schemas/FlowType"
},
"flowRunSettings": {
"$ref": "#/components/schemas/FlowRunSettings"
},
"isArchived": {
"type": "boolean"
},
"vmSize": {
"type": "string",
"nullable": true
},
"maxIdleTimeSeconds": {
"type": "integer",
"format": "int64",
"nullable": true
},
"identity": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"UpdateFlowRuntimeRequest": {
"type": "object",
"properties": {
"runtimeDescription": {
"type": "string",
"nullable": true
},
"environment": {
"type": "string",
"nullable": true
},
"instanceCount": {
"type": "integer",
"format": "int32"
}
},
"additionalProperties": false
},
"UpdateRegistryComponentRequest": {
"type": "object",
"properties": {
"registryName": {
"type": "string",
"nullable": true
},
"componentName": {
"type": "string",
"nullable": true
},
"componentVersion": {
"type": "string",
"nullable": true
},
"updateType": {
"$ref": "#/components/schemas/UpdateType"
}
},
"additionalProperties": false
},
"UpdateType": {
"enum": [
"SetDefaultVersion"
],
"type": "string"
},
"UploadOptions": {
"type": "object",
"properties": {
"overwrite": {
"type": "boolean"
},
"sourceGlobs": {
"$ref": "#/components/schemas/ExecutionGlobsOptions"
}
},
"additionalProperties": false
},
"UploadState": {
"enum": [
"Uploading",
"Completed",
"Canceled",
"Failed"
],
"type": "string"
},
"UriReference": {
"type": "object",
"properties": {
"path": {
"type": "string",
"nullable": true
},
"isFile": {
"type": "boolean"
}
},
"additionalProperties": false
},
"UseStl": {
"enum": [
"Season",
"SeasonTrend"
],
"type": "string"
},
"User": {
"type": "object",
"properties": {
"userObjectId": {
"type": "string",
"description": "A user or service principal's object ID.\r\nThis is EUPI and may only be logged to warm path telemetry.",
"nullable": true
},
"userPuId": {
"type": "string",
"description": "A user or service principal's PuID.\r\nThis is PII and should never be logged.",
"nullable": true
},
"userIdp": {
"type": "string",
"description": "A user identity provider. Eg live.com\r\nThis is PII and should never be logged.",
"nullable": true
},
"userAltSecId": {
"type": "string",
"description": "A user alternate sec id. This represents the user in a different identity provider system Eg.1:live.com:puid\r\nThis is PII and should never be logged.",
"nullable": true
},
"userIss": {
"type": "string",
"description": "The issuer which issed the token for this user.\r\nThis is PII and should never be logged.",
"nullable": true
},
"userTenantId": {
"type": "string",
"description": "A user or service principal's tenant ID.",
"nullable": true
},
"userName": {
"type": "string",
"description": "A user's full name or a service principal's app ID.\r\nThis is PII and should never be logged.",
"nullable": true
},
"upn": {
"type": "string",
"description": "A user's Principal name (upn)\r\nThis is PII andshould never be logged",
"nullable": true
}
},
"additionalProperties": false
},
"UserAssignedIdentity": {
"type": "object",
"properties": {
"principalId": {
"type": "string",
"format": "uuid"
},
"clientId": {
"type": "string",
"format": "uuid"
}
},
"additionalProperties": false
},
"UserType": {
"enum": [
"User",
"Application",
"ManagedIdentity",
"Key"
],
"type": "string"
},
"ValidationDataSettings": {
"type": "object",
"properties": {
"nCrossValidations": {
"$ref": "#/components/schemas/NCrossValidations"
},
"validationDataSize": {
"type": "number",
"format": "double",
"nullable": true
},
"cvSplitColumnNames": {
"type": "array",
"items": {
"type": "string"
},
"nullable": true
},
"validationType": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"ValidationStatus": {
"enum": [
"Succeeded",
"Failed"
],
"type": "string"
},
"ValueType": {
"enum": [
"int",
"double",
"bool",
"string",
"secret",
"prompt_template",
"object",
"list",
"BingConnection",
"OpenAIConnection",
"AzureOpenAIConnection",
"AzureContentModeratorConnection",
"CustomConnection",
"AzureContentSafetyConnection",
"SerpConnection",
"CognitiveSearchConnection",
"SubstrateLLMConnection",
"PineconeConnection",
"QdrantConnection",
"WeaviateConnection",
"function_list",
"function_str",
"FormRecognizerConnection",
"file_path",
"image"
],
"type": "string"
},
"VariantNode": {
"type": "object",
"properties": {
"node": {
"$ref": "#/components/schemas/Node"
},
"description": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"VmPriority": {
"enum": [
"Dedicated",
"Lowpriority"
],
"type": "string"
},
"WebServiceComputeMetaInfo": {
"type": "object",
"properties": {
"nodeCount": {
"type": "integer",
"format": "int32"
},
"isSslEnabled": {
"type": "boolean"
},
"aksNotFound": {
"type": "boolean"
},
"clusterPurpose": {
"type": "string",
"nullable": true
},
"publicIpAddress": {
"type": "string",
"nullable": true
},
"vmSize": {
"type": "string",
"nullable": true
},
"location": {
"type": "string",
"nullable": true
},
"provisioningState": {
"type": "string",
"nullable": true
},
"state": {
"type": "string",
"nullable": true
},
"osType": {
"type": "string",
"nullable": true
},
"id": {
"type": "string",
"nullable": true
},
"name": {
"type": "string",
"nullable": true
},
"createdByStudio": {
"type": "boolean"
},
"isGpuType": {
"type": "boolean"
},
"resourceId": {
"type": "string",
"nullable": true
},
"computeType": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"WebServicePort": {
"type": "object",
"properties": {
"nodeId": {
"type": "string",
"nullable": true
},
"portName": {
"type": "string",
"nullable": true
},
"name": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"WebServiceState": {
"enum": [
"Transitioning",
"Healthy",
"Unhealthy",
"Failed",
"Unschedulable"
],
"type": "string"
},
"Webhook": {
"type": "object",
"properties": {
"webhookType": {
"$ref": "#/components/schemas/WebhookType"
},
"eventType": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"WebhookType": {
"enum": [
"AzureDevOps"
],
"type": "string"
},
"WeekDays": {
"enum": [
"Monday",
"Tuesday",
"Wednesday",
"Thursday",
"Friday",
"Saturday",
"Sunday"
],
"type": "string"
},
"Weekday": {
"enum": [
"Monday",
"Tuesday",
"Wednesday",
"Thursday",
"Friday",
"Saturday",
"Sunday"
],
"type": "string"
},
"WorkspaceConnectionSpec": {
"type": "object",
"properties": {
"connectionCategory": {
"$ref": "#/components/schemas/ConnectionCategory"
},
"flowValueType": {
"$ref": "#/components/schemas/ValueType"
},
"connectionType": {
"$ref": "#/components/schemas/ConnectionType"
},
"connectionTypeDisplayName": {
"type": "string",
"nullable": true
},
"configSpecs": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ConnectionConfigSpec"
},
"nullable": true
},
"module": {
"type": "string",
"nullable": true
}
},
"additionalProperties": false
},
"YarnDeployMode": {
"enum": [
"None",
"Client",
"Cluster"
],
"type": "string"
}
},
"parameters": {
"subscriptionIdParameter": {
"name": "subscriptionId",
"in": "path",
"description": "The Azure Subscription ID.",
"required": true,
"schema": {
"type": "string",
"format": "uuid"
},
"x-ms-parameter-location": "method"
},
"resourceGroupNameParameter": {
"name": "resourceGroupName",
"in": "path",
"description": "The Name of the resource group in which the workspace is located.",
"required": true,
"schema": {
"type": "string"
},
"x-ms-parameter-location": "method"
},
"workspaceNameParameter": {
"name": "workspaceName",
"in": "path",
"description": "The name of the workspace.",
"required": true,
"schema": {
"type": "string"
},
"x-ms-parameter-location": "method"
}
},
"securitySchemes": {
"azure_auth": {
"type": "oauth2",
"flows": {
"implicit": {
"authorizationUrl": "https://login.microsoftonline.com/common/oauth2/authorize",
"scopes": {
"user_impersonation": "impersonate your user account"
}
}
}
}
}
},
"security": [
{
"azure_auth": [
"user_impersonation"
]
}
]
} | 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/README.md | # How to automatically generate the REST client code
Rest client code in this folder are not manually written, but generated by autorest.
## Setup
+ install [nodejs](https://nodejs.org/en)
+ install autorest
+ run `npm install -g autorest`
## Download swagger.json
Download swagger.json from [here](https://int.api.azureml-test.ms/flow/swagger/v1.0/swagger.json) to
[promptflow/azure/_restclient](../promptflow/azure/_restclient)
## Update code
+ cd to [promptflow/azure/_restclient](../promptflow/azure/_restclient)
+ run `autorest --v3 --python --track2 --version=3.8.0 --use=@autorest/[email protected] --input-file=swagger.json --output-folder=. --namespace=flow --modelerfour.lenient-model-deduplication`
+ don't change `--use`. latest version of `autorest/python` will generate code following different pattern, which is not compatible with our code.
## Update the generation history
- 2023.11.13 - [Update SDK restclient](https://github.com/microsoft/promptflow/pull/1101).
- 2023.12.18 - [Remove data portal url from the result of pfazure run show](https://github.com/microsoft/promptflow/pull/1497)
## Troubleshooting
### Duplicate object schemas with "xxx" name detected.
This may be caused by the duplicate generated class names.
```json
"FlowFeature": {
"type": "object",
"properties": {
"name": {
"type": "string",
"nullable": true
},
"description": {
"type": "string",
"nullable": true
},
"state": {
"type": "object",
"properties": {
"Runtime": {
"$ref": "#/components/schemas/FlowFeatureState"
},
"Executor": {
"$ref": "#/components/schemas/FlowFeatureState"
},
"PFS": {
"$ref": "#/components/schemas/FlowFeatureState"
}
},
"additionalProperties": false,
"nullable": true
}
},
"additionalProperties": false
},
"FlowFeatureState": {
"enum": [
"Ready",
"E2ETest"
],
"type": "string"
},
```
`FlowFeature` has a nested object field `state`, which will be generated to a new class named `FlowFeatureState`, and it duplicates with the enum `FlowFeatureState`.
To fix this, server side needs to change the class name in the schema, in this case, server side changed the object `state` to `states` and the problem is resolved.
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/__init__.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/py.typed | # Marker file for PEP 561. | 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/_azure_machine_learning_designer_service_client.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from copy import deepcopy
from typing import TYPE_CHECKING
from azure.core import PipelineClient
from msrest import Deserializer, Serializer
from . import models
from ._configuration import AzureMachineLearningDesignerServiceClientConfiguration
from .operations import BulkRunsOperations, ConnectionOperations, ConnectionsOperations, FlowRunsAdminOperations, FlowRuntimesOperations, FlowRuntimesWorkspaceIndependentOperations, FlowSessionsOperations, FlowsOperations, FlowsProviderOperations, ToolsOperations
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Optional
from azure.core.rest import HttpRequest, HttpResponse
class AzureMachineLearningDesignerServiceClient(object):
"""AzureMachineLearningDesignerServiceClient.
:ivar bulk_runs: BulkRunsOperations operations
:vartype bulk_runs: flow.operations.BulkRunsOperations
:ivar connection: ConnectionOperations operations
:vartype connection: flow.operations.ConnectionOperations
:ivar connections: ConnectionsOperations operations
:vartype connections: flow.operations.ConnectionsOperations
:ivar flow_runs_admin: FlowRunsAdminOperations operations
:vartype flow_runs_admin: flow.operations.FlowRunsAdminOperations
:ivar flow_runtimes: FlowRuntimesOperations operations
:vartype flow_runtimes: flow.operations.FlowRuntimesOperations
:ivar flow_runtimes_workspace_independent: FlowRuntimesWorkspaceIndependentOperations
operations
:vartype flow_runtimes_workspace_independent:
flow.operations.FlowRuntimesWorkspaceIndependentOperations
:ivar flows: FlowsOperations operations
:vartype flows: flow.operations.FlowsOperations
:ivar flow_sessions: FlowSessionsOperations operations
:vartype flow_sessions: flow.operations.FlowSessionsOperations
:ivar flows_provider: FlowsProviderOperations operations
:vartype flows_provider: flow.operations.FlowsProviderOperations
:ivar tools: ToolsOperations operations
:vartype tools: flow.operations.ToolsOperations
:param base_url: Service URL. Default value is ''.
:type base_url: str
:param api_version: Api Version. The default value is "1.0.0".
:type api_version: str
"""
def __init__(
self,
base_url="", # type: str
api_version="1.0.0", # type: Optional[str]
**kwargs # type: Any
):
# type: (...) -> None
self._config = AzureMachineLearningDesignerServiceClientConfiguration(api_version=api_version, **kwargs)
self._client = PipelineClient(base_url=base_url, config=self._config, **kwargs)
client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)}
self._serialize = Serializer(client_models)
self._deserialize = Deserializer(client_models)
self._serialize.client_side_validation = False
self.bulk_runs = BulkRunsOperations(self._client, self._config, self._serialize, self._deserialize)
self.connection = ConnectionOperations(self._client, self._config, self._serialize, self._deserialize)
self.connections = ConnectionsOperations(self._client, self._config, self._serialize, self._deserialize)
self.flow_runs_admin = FlowRunsAdminOperations(self._client, self._config, self._serialize, self._deserialize)
self.flow_runtimes = FlowRuntimesOperations(self._client, self._config, self._serialize, self._deserialize)
self.flow_runtimes_workspace_independent = FlowRuntimesWorkspaceIndependentOperations(self._client, self._config, self._serialize, self._deserialize)
self.flows = FlowsOperations(self._client, self._config, self._serialize, self._deserialize)
self.flow_sessions = FlowSessionsOperations(self._client, self._config, self._serialize, self._deserialize)
self.flows_provider = FlowsProviderOperations(self._client, self._config, self._serialize, self._deserialize)
self.tools = ToolsOperations(self._client, self._config, self._serialize, self._deserialize)
def _send_request(
self,
request, # type: HttpRequest
**kwargs # type: Any
):
# type: (...) -> HttpResponse
"""Runs the network request through the client's chained policies.
>>> from azure.core.rest import HttpRequest
>>> request = HttpRequest("GET", "https://www.example.org/")
<HttpRequest [GET], url: 'https://www.example.org/'>
>>> response = client._send_request(request)
<HttpResponse: 200 OK>
For more information on this code flow, see https://aka.ms/azsdk/python/protocol/quickstart
:param request: The network request you want to make. Required.
:type request: ~azure.core.rest.HttpRequest
:keyword bool stream: Whether the response payload will be streamed. Defaults to False.
:return: The response of your network call. Does not do error handling on your response.
:rtype: ~azure.core.rest.HttpResponse
"""
request_copy = deepcopy(request)
request_copy.url = self._client.format_url(request_copy.url)
return self._client.send_request(request_copy, **kwargs)
def close(self):
# type: () -> None
self._client.close()
def __enter__(self):
# type: () -> AzureMachineLearningDesignerServiceClient
self._client.__enter__()
return self
def __exit__(self, *exc_details):
# type: (Any) -> None
self._client.__exit__(*exc_details)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/_vendor.py | # --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from azure.core.pipeline.transport import HttpRequest
def _convert_request(request, files=None):
data = request.content if not files else None
request = HttpRequest(method=request.method, url=request.url, headers=request.headers, data=data)
if files:
request.set_formdata_body(files)
return request
def _format_url_section(template, **kwargs):
components = template.split("/")
while components:
try:
return template.format(**kwargs)
except KeyError as key:
formatted_components = template.split("/")
components = [
c for c in formatted_components if "{}".format(key.args[0]) not in c
]
template = "/".join(components)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/_configuration.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import TYPE_CHECKING
from azure.core.configuration import Configuration
from azure.core.pipeline import policies
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Optional
VERSION = "unknown"
class AzureMachineLearningDesignerServiceClientConfiguration(Configuration):
"""Configuration for AzureMachineLearningDesignerServiceClient.
Note that all parameters used to create this instance are saved as instance
attributes.
:param api_version: Api Version. The default value is "1.0.0".
:type api_version: str
"""
def __init__(
self,
api_version="1.0.0", # type: Optional[str]
**kwargs # type: Any
):
# type: (...) -> None
super(AzureMachineLearningDesignerServiceClientConfiguration, self).__init__(**kwargs)
self.api_version = api_version
kwargs.setdefault('sdk_moniker', 'azuremachinelearningdesignerserviceclient/{}'.format(VERSION))
self._configure(**kwargs)
def _configure(
self,
**kwargs # type: Any
):
# type: (...) -> None
self.user_agent_policy = kwargs.get('user_agent_policy') or policies.UserAgentPolicy(**kwargs)
self.headers_policy = kwargs.get('headers_policy') or policies.HeadersPolicy(**kwargs)
self.proxy_policy = kwargs.get('proxy_policy') or policies.ProxyPolicy(**kwargs)
self.logging_policy = kwargs.get('logging_policy') or policies.NetworkTraceLoggingPolicy(**kwargs)
self.http_logging_policy = kwargs.get('http_logging_policy') or policies.HttpLoggingPolicy(**kwargs)
self.retry_policy = kwargs.get('retry_policy') or policies.RetryPolicy(**kwargs)
self.custom_hook_policy = kwargs.get('custom_hook_policy') or policies.CustomHookPolicy(**kwargs)
self.redirect_policy = kwargs.get('redirect_policy') or policies.RedirectPolicy(**kwargs)
self.authentication_policy = kwargs.get('authentication_policy')
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/_patch.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# coding=utf-8
# --------------------------------------------------------------------------
#
# Copyright (c) Microsoft Corporation. All rights reserved.
#
# The MIT License (MIT)
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the ""Software""), to
# deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
#
# --------------------------------------------------------------------------
# This file is used for handwritten extensions to the generated code. Example:
# https://github.com/Azure/azure-sdk-for-python/blob/main/doc/dev/customize_code/how-to-patch-sdk-code.md
def patch_sdk():
pass | 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/__init__.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from ._azure_machine_learning_designer_service_client import AzureMachineLearningDesignerServiceClient
__all__ = ['AzureMachineLearningDesignerServiceClient']
# `._patch.py` is used for handwritten extensions to the generated code
# Example: https://github.com/Azure/azure-sdk-for-python/blob/main/doc/dev/customize_code/how-to-patch-sdk-code.md
from ._patch import patch_sdk
patch_sdk()
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_bulk_runs_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request, _format_url_section
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, Union
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_submit_bulk_run_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/submit')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_cancel_flow_run_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "text/plain, application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/cancel')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_clone_flow_from_flow_run_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/clone')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_flow_run_info_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_flow_child_runs_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
index = kwargs.pop('index', None) # type: Optional[int]
start_index = kwargs.pop('start_index', None) # type: Optional[int]
end_index = kwargs.pop('end_index', None) # type: Optional[int]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/childRuns')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if index is not None:
query_parameters['index'] = _SERIALIZER.query("index", index, 'int')
if start_index is not None:
query_parameters['startIndex'] = _SERIALIZER.query("start_index", start_index, 'int')
if end_index is not None:
query_parameters['endIndex'] = _SERIALIZER.query("end_index", end_index, 'int')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_flow_node_runs_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
node_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
index = kwargs.pop('index', None) # type: Optional[int]
start_index = kwargs.pop('start_index', None) # type: Optional[int]
end_index = kwargs.pop('end_index', None) # type: Optional[int]
aggregation = kwargs.pop('aggregation', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/nodeRuns/{nodeName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
"nodeName": _SERIALIZER.url("node_name", node_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if index is not None:
query_parameters['index'] = _SERIALIZER.query("index", index, 'int')
if start_index is not None:
query_parameters['startIndex'] = _SERIALIZER.query("start_index", start_index, 'int')
if end_index is not None:
query_parameters['endIndex'] = _SERIALIZER.query("end_index", end_index, 'int')
if aggregation is not None:
query_parameters['aggregation'] = _SERIALIZER.query("aggregation", aggregation, 'bool')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_flow_node_run_base_path_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
node_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/nodeRuns/{nodeName}/basePath')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
"nodeName": _SERIALIZER.url("node_name", node_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_flow_run_log_content_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/logContent')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
# fmt: on
class BulkRunsOperations(object):
"""BulkRunsOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace
def submit_bulk_run(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
body=None, # type: Optional["_models.SubmitBulkRunRequest"]
**kwargs # type: Any
):
# type: (...) -> str
"""submit_bulk_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param body:
:type body: ~flow.models.SubmitBulkRunRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'SubmitBulkRunRequest')
else:
_json = None
request = build_submit_bulk_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
template_url=self.submit_bulk_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202, 204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
if response.status_code == 200:
deserialized = self._deserialize('str', pipeline_response)
if response.status_code == 202:
deserialized = self._deserialize('str', pipeline_response)
if response.status_code == 204:
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
submit_bulk_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/submit'} # type: ignore
@distributed_trace
def cancel_flow_run(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> str
"""cancel_flow_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_cancel_flow_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
template_url=self.cancel_flow_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
cancel_flow_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/cancel'} # type: ignore
@distributed_trace
def clone_flow_from_flow_run(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
body=None, # type: Optional["_models.CreateFlowRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowDto"
"""clone_flow_from_flow_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:param body:
:type body: ~flow.models.CreateFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowDto, or the result of cls(response)
:rtype: ~flow.models.FlowDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowRequest')
else:
_json = None
request = build_clone_flow_from_flow_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
content_type=content_type,
json=_json,
template_url=self.clone_flow_from_flow_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
clone_flow_from_flow_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/clone'} # type: ignore
@distributed_trace
def get_flow_run_info(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRunInfo"
"""get_flow_run_info.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRunInfo, or the result of cls(response)
:rtype: ~flow.models.FlowRunInfo
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRunInfo"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_run_info_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
template_url=self.get_flow_run_info.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRunInfo', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_run_info.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}'} # type: ignore
@distributed_trace
def get_flow_child_runs(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
index=None, # type: Optional[int]
start_index=None, # type: Optional[int]
end_index=None, # type: Optional[int]
**kwargs # type: Any
):
# type: (...) -> List[Any]
"""get_flow_child_runs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:param index:
:type index: int
:param start_index:
:type start_index: int
:param end_index:
:type end_index: int
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of any, or the result of cls(response)
:rtype: list[any]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List[Any]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_child_runs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
index=index,
start_index=start_index,
end_index=end_index,
template_url=self.get_flow_child_runs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[object]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_child_runs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/childRuns'} # type: ignore
@distributed_trace
def get_flow_node_runs(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
node_name, # type: str
index=None, # type: Optional[int]
start_index=None, # type: Optional[int]
end_index=None, # type: Optional[int]
aggregation=False, # type: Optional[bool]
**kwargs # type: Any
):
# type: (...) -> List[Any]
"""get_flow_node_runs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:param node_name:
:type node_name: str
:param index:
:type index: int
:param start_index:
:type start_index: int
:param end_index:
:type end_index: int
:param aggregation:
:type aggregation: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of any, or the result of cls(response)
:rtype: list[any]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List[Any]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_node_runs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
node_name=node_name,
index=index,
start_index=start_index,
end_index=end_index,
aggregation=aggregation,
template_url=self.get_flow_node_runs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[object]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_node_runs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/nodeRuns/{nodeName}'} # type: ignore
@distributed_trace
def get_flow_node_run_base_path(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
node_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRunBasePath"
"""get_flow_node_run_base_path.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:param node_name:
:type node_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRunBasePath, or the result of cls(response)
:rtype: ~flow.models.FlowRunBasePath
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRunBasePath"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_node_run_base_path_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
node_name=node_name,
template_url=self.get_flow_node_run_base_path.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRunBasePath', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_node_run_base_path.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/nodeRuns/{nodeName}/basePath'} # type: ignore
@distributed_trace
def get_flow_run_log_content(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> str
"""get_flow_run_log_content.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_run_log_content_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
template_url=self.get_flow_run_log_content.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_run_log_content.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/logContent'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_flows_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request, _format_url_section
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, Union
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_create_flow_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
experiment_id = kwargs.pop('experiment_id', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if experiment_id is not None:
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_list_flows_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
experiment_id = kwargs.pop('experiment_id', None) # type: Optional[str]
owned_only = kwargs.pop('owned_only', None) # type: Optional[bool]
flow_type = kwargs.pop('flow_type', None) # type: Optional[Union[str, "_models.FlowType"]]
list_view_type = kwargs.pop('list_view_type', None) # type: Optional[Union[str, "_models.ListViewType"]]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if experiment_id is not None:
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
if owned_only is not None:
query_parameters['ownedOnly'] = _SERIALIZER.query("owned_only", owned_only, 'bool')
if flow_type is not None:
query_parameters['flowType'] = _SERIALIZER.query("flow_type", flow_type, 'str')
if list_view_type is not None:
query_parameters['listViewType'] = _SERIALIZER.query("list_view_type", list_view_type, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_clone_flow_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
experiment_id = kwargs.pop('experiment_id') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/clone')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_create_flow_from_sample_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
experiment_id = kwargs.pop('experiment_id', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/fromsample')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if experiment_id is not None:
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_update_flow_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
experiment_id = kwargs.pop('experiment_id') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_patch_flow_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
experiment_id = kwargs.pop('experiment_id') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PATCH",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_flow_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
experiment_id = kwargs.pop('experiment_id') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_submit_flow_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
experiment_id = kwargs.pop('experiment_id') # type: str
endpoint_name = kwargs.pop('endpoint_name', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/submit')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
if endpoint_name is not None:
query_parameters['endpointName'] = _SERIALIZER.query("endpoint_name", endpoint_name, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_flow_run_status_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
experiment_id = kwargs.pop('experiment_id', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/{flowRunId}/status')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if experiment_id is not None:
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_flow_run_info_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
experiment_id = kwargs.pop('experiment_id') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_flow_child_runs_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
index = kwargs.pop('index', None) # type: Optional[int]
start_index = kwargs.pop('start_index', None) # type: Optional[int]
end_index = kwargs.pop('end_index', None) # type: Optional[int]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/childRuns')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if index is not None:
query_parameters['index'] = _SERIALIZER.query("index", index, 'int')
if start_index is not None:
query_parameters['startIndex'] = _SERIALIZER.query("start_index", start_index, 'int')
if end_index is not None:
query_parameters['endIndex'] = _SERIALIZER.query("end_index", end_index, 'int')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_flow_node_runs_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
node_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
index = kwargs.pop('index', None) # type: Optional[int]
start_index = kwargs.pop('start_index', None) # type: Optional[int]
end_index = kwargs.pop('end_index', None) # type: Optional[int]
aggregation = kwargs.pop('aggregation', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/nodeRuns/{nodeName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
"nodeName": _SERIALIZER.url("node_name", node_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if index is not None:
query_parameters['index'] = _SERIALIZER.query("index", index, 'int')
if start_index is not None:
query_parameters['startIndex'] = _SERIALIZER.query("start_index", start_index, 'int')
if end_index is not None:
query_parameters['endIndex'] = _SERIALIZER.query("end_index", end_index, 'int')
if aggregation is not None:
query_parameters['aggregation'] = _SERIALIZER.query("aggregation", aggregation, 'bool')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_flow_node_run_base_path_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
node_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/nodeRuns/{nodeName}/basePath')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
"nodeName": _SERIALIZER.url("node_name", node_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_clone_flow_from_flow_run_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
experiment_id = kwargs.pop('experiment_id') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/clone')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_list_bulk_tests_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
experiment_id = kwargs.pop('experiment_id', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/bulkTests')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if experiment_id is not None:
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_bulk_test_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_test_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/bulkTests/{bulkTestId}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"bulkTestId": _SERIALIZER.url("bulk_test_id", bulk_test_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_samples_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
use_snapshot = kwargs.pop('use_snapshot', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/samples')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if use_snapshot is not None:
query_parameters['useSnapshot'] = _SERIALIZER.query("use_snapshot", use_snapshot, 'bool')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_evaluate_flow_samples_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
use_snapshot = kwargs.pop('use_snapshot', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/evaluateSamples')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if use_snapshot is not None:
query_parameters['useSnapshot'] = _SERIALIZER.query("use_snapshot", use_snapshot, 'bool')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_flow_deploy_reserved_environment_variable_names_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/DeployReservedEnvironmentVariableNames')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_deploy_flow_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
async_call = kwargs.pop('async_call', False) # type: Optional[bool]
msi_token = kwargs.pop('msi_token', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/deploy')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if async_call is not None:
query_parameters['asyncCall'] = _SERIALIZER.query("async_call", async_call, 'bool')
if msi_token is not None:
query_parameters['msiToken'] = _SERIALIZER.query("msi_token", msi_token, 'bool')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_flow_run_log_content_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/logContent')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_cancel_flow_run_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "text/plain, application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/runs/{flowRunId}/cancel')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_cancel_flow_test_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "text/plain, application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/flowTests/{flowRunId}/cancel')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_cancel_bulk_test_run_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
bulk_test_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "text/plain, application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/bulkTests/{bulkTestRunId}/cancel')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"bulkTestRunId": _SERIALIZER.url("bulk_test_run_id", bulk_test_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_flow_snapshot_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/FlowSnapshot')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_connection_override_settings_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
runtime_name = kwargs.pop('runtime_name', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/connectionOverride')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if runtime_name is not None:
query_parameters['runtimeName'] = _SERIALIZER.query("runtime_name", runtime_name, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_flow_inputs_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/flowInputs')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_load_as_component_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/LoadAsComponent')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_flow_tools_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
experiment_id = kwargs.pop('experiment_id') # type: str
flow_runtime_name = kwargs.pop('flow_runtime_name', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/flowTools')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if flow_runtime_name is not None:
query_parameters['flowRuntimeName'] = _SERIALIZER.query("flow_runtime_name", flow_runtime_name, 'str')
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_setup_flow_session_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
experiment_id = kwargs.pop('experiment_id') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/sessions')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_delete_flow_session_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
experiment_id = kwargs.pop('experiment_id') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/sessions')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="DELETE",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_flow_session_status_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
experiment_id = kwargs.pop('experiment_id') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/sessions/status')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['experimentId'] = _SERIALIZER.query("experiment_id", experiment_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
# fmt: on
class FlowsOperations(object):
"""FlowsOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace
def create_flow(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
experiment_id=None, # type: Optional[str]
body=None, # type: Optional["_models.CreateFlowRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowDto"
"""create_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.CreateFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowDto, or the result of cls(response)
:rtype: ~flow.models.FlowDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowRequest')
else:
_json = None
request = build_create_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
experiment_id=experiment_id,
template_url=self.create_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows'} # type: ignore
@distributed_trace
def list_flows(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
experiment_id=None, # type: Optional[str]
owned_only=None, # type: Optional[bool]
flow_type=None, # type: Optional[Union[str, "_models.FlowType"]]
list_view_type=None, # type: Optional[Union[str, "_models.ListViewType"]]
**kwargs # type: Any
):
# type: (...) -> List["_models.FlowBaseDto"]
"""list_flows.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param experiment_id:
:type experiment_id: str
:param owned_only:
:type owned_only: bool
:param flow_type:
:type flow_type: str or ~flow.models.FlowType
:param list_view_type:
:type list_view_type: str or ~flow.models.ListViewType
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of FlowBaseDto, or the result of cls(response)
:rtype: list[~flow.models.FlowBaseDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.FlowBaseDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_flows_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
experiment_id=experiment_id,
owned_only=owned_only,
flow_type=flow_type,
list_view_type=list_view_type,
template_url=self.list_flows.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[FlowBaseDto]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_flows.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows'} # type: ignore
@distributed_trace
def clone_flow(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
experiment_id, # type: str
body=None, # type: Optional["_models.CreateFlowRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowDto"
"""clone_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.CreateFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowDto, or the result of cls(response)
:rtype: ~flow.models.FlowDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowRequest')
else:
_json = None
request = build_clone_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
content_type=content_type,
experiment_id=experiment_id,
json=_json,
template_url=self.clone_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
clone_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/clone'} # type: ignore
@distributed_trace
def create_flow_from_sample(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
experiment_id=None, # type: Optional[str]
body=None, # type: Optional["_models.CreateFlowFromSampleRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowDto"
"""create_flow_from_sample.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.CreateFlowFromSampleRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowDto, or the result of cls(response)
:rtype: ~flow.models.FlowDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowFromSampleRequest')
else:
_json = None
request = build_create_flow_from_sample_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
experiment_id=experiment_id,
template_url=self.create_flow_from_sample.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_flow_from_sample.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/fromsample'} # type: ignore
@distributed_trace
def update_flow(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
experiment_id, # type: str
body=None, # type: Optional["_models.UpdateFlowRequest"]
**kwargs # type: Any
):
# type: (...) -> str
"""update_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.UpdateFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'UpdateFlowRequest')
else:
_json = None
request = build_update_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
content_type=content_type,
experiment_id=experiment_id,
json=_json,
template_url=self.update_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}'} # type: ignore
@distributed_trace
def patch_flow(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
experiment_id, # type: str
body=None, # type: Optional["_models.PatchFlowRequest"]
**kwargs # type: Any
):
# type: (...) -> str
"""patch_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.PatchFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json-patch+json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'PatchFlowRequest')
else:
_json = None
request = build_patch_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
content_type=content_type,
experiment_id=experiment_id,
json=_json,
template_url=self.patch_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
patch_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}'} # type: ignore
@distributed_trace
def get_flow(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
experiment_id, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.FlowDto"
"""get_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowDto, or the result of cls(response)
:rtype: ~flow.models.FlowDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
experiment_id=experiment_id,
template_url=self.get_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}'} # type: ignore
@distributed_trace
def submit_flow(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
experiment_id, # type: str
endpoint_name=None, # type: Optional[str]
body=None, # type: Optional["_models.SubmitFlowRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRunResult"
"""submit_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param experiment_id:
:type experiment_id: str
:param endpoint_name:
:type endpoint_name: str
:param body:
:type body: ~flow.models.SubmitFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRunResult, or the result of cls(response)
:rtype: ~flow.models.FlowRunResult
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRunResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'SubmitFlowRequest')
else:
_json = None
request = build_submit_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
experiment_id=experiment_id,
json=_json,
endpoint_name=endpoint_name,
template_url=self.submit_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRunResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
submit_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/submit'} # type: ignore
@distributed_trace
def get_flow_run_status(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
experiment_id=None, # type: Optional[str]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRunResult"
"""get_flow_run_status.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param experiment_id:
:type experiment_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRunResult, or the result of cls(response)
:rtype: ~flow.models.FlowRunResult
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRunResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_run_status_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
experiment_id=experiment_id,
template_url=self.get_flow_run_status.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRunResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_run_status.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/{flowRunId}/status'} # type: ignore
@distributed_trace
def get_flow_run_info(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
experiment_id, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRunInfo"
"""get_flow_run_info.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param experiment_id:
:type experiment_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRunInfo, or the result of cls(response)
:rtype: ~flow.models.FlowRunInfo
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRunInfo"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_run_info_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
experiment_id=experiment_id,
template_url=self.get_flow_run_info.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRunInfo', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_run_info.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}'} # type: ignore
@distributed_trace
def get_flow_child_runs(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
index=None, # type: Optional[int]
start_index=None, # type: Optional[int]
end_index=None, # type: Optional[int]
**kwargs # type: Any
):
# type: (...) -> List[Any]
"""get_flow_child_runs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param index:
:type index: int
:param start_index:
:type start_index: int
:param end_index:
:type end_index: int
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of any, or the result of cls(response)
:rtype: list[any]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List[Any]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_child_runs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
index=index,
start_index=start_index,
end_index=end_index,
template_url=self.get_flow_child_runs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[object]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_child_runs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/childRuns'} # type: ignore
@distributed_trace
def get_flow_node_runs(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
node_name, # type: str
index=None, # type: Optional[int]
start_index=None, # type: Optional[int]
end_index=None, # type: Optional[int]
aggregation=False, # type: Optional[bool]
**kwargs # type: Any
):
# type: (...) -> List[Any]
"""get_flow_node_runs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param node_name:
:type node_name: str
:param index:
:type index: int
:param start_index:
:type start_index: int
:param end_index:
:type end_index: int
:param aggregation:
:type aggregation: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of any, or the result of cls(response)
:rtype: list[any]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List[Any]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_node_runs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
node_name=node_name,
index=index,
start_index=start_index,
end_index=end_index,
aggregation=aggregation,
template_url=self.get_flow_node_runs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[object]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_node_runs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/nodeRuns/{nodeName}'} # type: ignore
@distributed_trace
def get_flow_node_run_base_path(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
node_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRunBasePath"
"""get_flow_node_run_base_path.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param node_name:
:type node_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRunBasePath, or the result of cls(response)
:rtype: ~flow.models.FlowRunBasePath
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRunBasePath"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_node_run_base_path_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
node_name=node_name,
template_url=self.get_flow_node_run_base_path.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRunBasePath', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_node_run_base_path.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/nodeRuns/{nodeName}/basePath'} # type: ignore
@distributed_trace
def clone_flow_from_flow_run(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
experiment_id, # type: str
body=None, # type: Optional["_models.CreateFlowRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowDto"
"""clone_flow_from_flow_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.CreateFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowDto, or the result of cls(response)
:rtype: ~flow.models.FlowDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowRequest')
else:
_json = None
request = build_clone_flow_from_flow_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
content_type=content_type,
experiment_id=experiment_id,
json=_json,
template_url=self.clone_flow_from_flow_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
clone_flow_from_flow_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/clone'} # type: ignore
@distributed_trace
def list_bulk_tests(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
experiment_id=None, # type: Optional[str]
**kwargs # type: Any
):
# type: (...) -> List["_models.BulkTestDto"]
"""list_bulk_tests.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of BulkTestDto, or the result of cls(response)
:rtype: list[~flow.models.BulkTestDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.BulkTestDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_bulk_tests_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
experiment_id=experiment_id,
template_url=self.list_bulk_tests.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[BulkTestDto]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_bulk_tests.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/bulkTests'} # type: ignore
@distributed_trace
def get_bulk_test(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_test_id, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.BulkTestDto"
"""get_bulk_test.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_test_id:
:type bulk_test_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: BulkTestDto, or the result of cls(response)
:rtype: ~flow.models.BulkTestDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.BulkTestDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_bulk_test_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_test_id=bulk_test_id,
template_url=self.get_bulk_test.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('BulkTestDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_bulk_test.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/bulkTests/{bulkTestId}'} # type: ignore
@distributed_trace
def get_samples(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
use_snapshot=False, # type: Optional[bool]
**kwargs # type: Any
):
# type: (...) -> Dict[str, "_models.FlowSampleDto"]
"""get_samples.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param use_snapshot:
:type use_snapshot: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: dict mapping str to FlowSampleDto, or the result of cls(response)
:rtype: dict[str, ~flow.models.FlowSampleDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.FlowSampleDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_samples_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
use_snapshot=use_snapshot,
template_url=self.get_samples.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('{FlowSampleDto}', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_samples.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/samples'} # type: ignore
@distributed_trace
def get_evaluate_flow_samples(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
use_snapshot=False, # type: Optional[bool]
**kwargs # type: Any
):
# type: (...) -> Dict[str, "_models.FlowSampleDto"]
"""get_evaluate_flow_samples.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param use_snapshot:
:type use_snapshot: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: dict mapping str to FlowSampleDto, or the result of cls(response)
:rtype: dict[str, ~flow.models.FlowSampleDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.FlowSampleDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_evaluate_flow_samples_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
use_snapshot=use_snapshot,
template_url=self.get_evaluate_flow_samples.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('{FlowSampleDto}', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_evaluate_flow_samples.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/evaluateSamples'} # type: ignore
@distributed_trace
def get_flow_deploy_reserved_environment_variable_names(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> List[str]
"""get_flow_deploy_reserved_environment_variable_names.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of str, or the result of cls(response)
:rtype: list[str]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List[str]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_deploy_reserved_environment_variable_names_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_flow_deploy_reserved_environment_variable_names.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[str]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_deploy_reserved_environment_variable_names.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/DeployReservedEnvironmentVariableNames'} # type: ignore
@distributed_trace
def deploy_flow(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
async_call=False, # type: Optional[bool]
msi_token=False, # type: Optional[bool]
body=None, # type: Optional["_models.DeployFlowRequest"]
**kwargs # type: Any
):
# type: (...) -> str
"""deploy_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:param body:
:type body: ~flow.models.DeployFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'DeployFlowRequest')
else:
_json = None
request = build_deploy_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
async_call=async_call,
msi_token=msi_token,
template_url=self.deploy_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
deploy_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/deploy'} # type: ignore
@distributed_trace
def get_flow_run_log_content(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> str
"""get_flow_run_log_content.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_run_log_content_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
template_url=self.get_flow_run_log_content.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_run_log_content.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/logContent'} # type: ignore
@distributed_trace
def cancel_flow_run(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> str
"""cancel_flow_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_cancel_flow_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
template_url=self.cancel_flow_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
cancel_flow_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/runs/{flowRunId}/cancel'} # type: ignore
@distributed_trace
def cancel_flow_test(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> str
"""cancel_flow_test.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_cancel_flow_test_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
template_url=self.cancel_flow_test.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
cancel_flow_test.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/flowTests/{flowRunId}/cancel'} # type: ignore
@distributed_trace
def cancel_bulk_test_run(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
bulk_test_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> str
"""cancel_bulk_test_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param bulk_test_run_id:
:type bulk_test_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_cancel_bulk_test_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
bulk_test_run_id=bulk_test_run_id,
template_url=self.cancel_bulk_test_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
cancel_bulk_test_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/bulkTests/{bulkTestRunId}/cancel'} # type: ignore
@distributed_trace
def get_flow_snapshot(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
body=None, # type: Optional["_models.CreateFlowRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowSnapshot"
"""get_flow_snapshot.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param body:
:type body: ~flow.models.CreateFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowSnapshot, or the result of cls(response)
:rtype: ~flow.models.FlowSnapshot
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowSnapshot"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowRequest')
else:
_json = None
request = build_get_flow_snapshot_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
template_url=self.get_flow_snapshot.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowSnapshot', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_snapshot.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/FlowSnapshot'} # type: ignore
@distributed_trace
def get_connection_override_settings(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name=None, # type: Optional[str]
body=None, # type: Optional["_models.FlowGraphReference"]
**kwargs # type: Any
):
# type: (...) -> List["_models.ConnectionOverrideSetting"]
"""get_connection_override_settings.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:param body:
:type body: ~flow.models.FlowGraphReference
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of ConnectionOverrideSetting, or the result of cls(response)
:rtype: list[~flow.models.ConnectionOverrideSetting]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ConnectionOverrideSetting"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'FlowGraphReference')
else:
_json = None
request = build_get_connection_override_settings_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
runtime_name=runtime_name,
template_url=self.get_connection_override_settings.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[ConnectionOverrideSetting]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_connection_override_settings.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/connectionOverride'} # type: ignore
@distributed_trace
def get_flow_inputs(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
body=None, # type: Optional["_models.FlowGraphReference"]
**kwargs # type: Any
):
# type: (...) -> Dict[str, "_models.FlowInputDefinition"]
"""get_flow_inputs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param body:
:type body: ~flow.models.FlowGraphReference
:keyword callable cls: A custom type or function that will be passed the direct response
:return: dict mapping str to FlowInputDefinition, or the result of cls(response)
:rtype: dict[str, ~flow.models.FlowInputDefinition]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.FlowInputDefinition"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'FlowGraphReference')
else:
_json = None
request = build_get_flow_inputs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
template_url=self.get_flow_inputs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('{FlowInputDefinition}', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_inputs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/flowInputs'} # type: ignore
@distributed_trace
def load_as_component(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
body=None, # type: Optional["_models.LoadFlowAsComponentRequest"]
**kwargs # type: Any
):
# type: (...) -> str
"""load_as_component.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param body:
:type body: ~flow.models.LoadFlowAsComponentRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'LoadFlowAsComponentRequest')
else:
_json = None
request = build_load_as_component_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
template_url=self.load_as_component.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
load_as_component.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/LoadAsComponent'} # type: ignore
@distributed_trace
def get_flow_tools(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
experiment_id, # type: str
flow_runtime_name=None, # type: Optional[str]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowToolsDto"
"""get_flow_tools.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:param flow_runtime_name:
:type flow_runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowToolsDto, or the result of cls(response)
:rtype: ~flow.models.FlowToolsDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowToolsDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_tools_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
experiment_id=experiment_id,
flow_runtime_name=flow_runtime_name,
template_url=self.get_flow_tools.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowToolsDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_tools.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/flowTools'} # type: ignore
@distributed_trace
def setup_flow_session(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
experiment_id, # type: str
body=None, # type: Optional["_models.SetupFlowSessionRequest"]
**kwargs # type: Any
):
# type: (...) -> Any
"""setup_flow_session.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.SetupFlowSessionRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: any, or the result of cls(response)
:rtype: any
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Any]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'SetupFlowSessionRequest')
else:
_json = None
request = build_setup_flow_session_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
content_type=content_type,
experiment_id=experiment_id,
json=_json,
template_url=self.setup_flow_session.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
if response.status_code == 200:
deserialized = self._deserialize('object', pipeline_response)
if response.status_code == 202:
deserialized = self._deserialize('object', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
setup_flow_session.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/sessions'} # type: ignore
@distributed_trace
def delete_flow_session(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
experiment_id, # type: str
**kwargs # type: Any
):
# type: (...) -> Any
"""delete_flow_session.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: any, or the result of cls(response)
:rtype: any
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Any]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_delete_flow_session_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
experiment_id=experiment_id,
template_url=self.delete_flow_session.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
if response.status_code == 200:
deserialized = self._deserialize('object', pipeline_response)
if response.status_code == 202:
deserialized = self._deserialize('object', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
delete_flow_session.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/sessions'} # type: ignore
@distributed_trace
def get_flow_session_status(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
experiment_id, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.FlowSessionDto"
"""get_flow_session_status.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowSessionDto, or the result of cls(response)
:rtype: ~flow.models.FlowSessionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowSessionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_session_status_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
experiment_id=experiment_id,
template_url=self.get_flow_session_status.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowSessionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_session_status.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/sessions/status'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_connection_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request, _format_url_section
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, Union
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_create_connection_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_update_connection_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_connection_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_delete_connection_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
connection_scope = kwargs.pop('connection_scope', None) # type: Optional[Union[str, "_models.ConnectionScope"]]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if connection_scope is not None:
query_parameters['connectionScope'] = _SERIALIZER.query("connection_scope", connection_scope, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="DELETE",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_list_connections_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_list_connection_specs_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/specs')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
# fmt: on
class ConnectionOperations(object):
"""ConnectionOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace
def create_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
body=None, # type: Optional["_models.CreateOrUpdateConnectionRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.ConnectionEntity"
"""create_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:param body:
:type body: ~flow.models.CreateOrUpdateConnectionRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionEntity, or the result of cls(response)
:rtype: ~flow.models.ConnectionEntity
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionEntity"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateOrUpdateConnectionRequest')
else:
_json = None
request = build_create_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
content_type=content_type,
json=_json,
template_url=self.create_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionEntity', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}'} # type: ignore
@distributed_trace
def update_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
body=None, # type: Optional["_models.CreateOrUpdateConnectionRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.ConnectionEntity"
"""update_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:param body:
:type body: ~flow.models.CreateOrUpdateConnectionRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionEntity, or the result of cls(response)
:rtype: ~flow.models.ConnectionEntity
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionEntity"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateOrUpdateConnectionRequest')
else:
_json = None
request = build_update_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
content_type=content_type,
json=_json,
template_url=self.update_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionEntity', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}'} # type: ignore
@distributed_trace
def get_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.ConnectionEntity"
"""get_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionEntity, or the result of cls(response)
:rtype: ~flow.models.ConnectionEntity
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionEntity"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
template_url=self.get_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionEntity', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}'} # type: ignore
@distributed_trace
def delete_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
connection_scope=None, # type: Optional[Union[str, "_models.ConnectionScope"]]
**kwargs # type: Any
):
# type: (...) -> "_models.ConnectionEntity"
"""delete_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:param connection_scope:
:type connection_scope: str or ~flow.models.ConnectionScope
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionEntity, or the result of cls(response)
:rtype: ~flow.models.ConnectionEntity
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionEntity"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_delete_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
connection_scope=connection_scope,
template_url=self.delete_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionEntity', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
delete_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}'} # type: ignore
@distributed_trace
def list_connections(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> List["_models.ConnectionEntity"]
"""list_connections.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of ConnectionEntity, or the result of cls(response)
:rtype: list[~flow.models.ConnectionEntity]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ConnectionEntity"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_connections_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_connections.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[ConnectionEntity]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_connections.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection'} # type: ignore
@distributed_trace
def list_connection_specs(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> List["_models.ConnectionSpec"]
"""list_connection_specs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of ConnectionSpec, or the result of cls(response)
:rtype: list[~flow.models.ConnectionSpec]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ConnectionSpec"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_connection_specs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_connection_specs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[ConnectionSpec]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_connection_specs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/specs'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_flow_runtimes_workspace_independent_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, Optional, TypeVar
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_get_runtime_latest_config_request(
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/runtimes/latestConfig')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
# fmt: on
class FlowRuntimesWorkspaceIndependentOperations(object):
"""FlowRuntimesWorkspaceIndependentOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace
def get_runtime_latest_config(
self,
**kwargs # type: Any
):
# type: (...) -> "_models.RuntimeConfiguration"
"""get_runtime_latest_config.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: RuntimeConfiguration, or the result of cls(response)
:rtype: ~flow.models.RuntimeConfiguration
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.RuntimeConfiguration"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_latest_config_request(
template_url=self.get_runtime_latest_config.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('RuntimeConfiguration', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime_latest_config.metadata = {'url': '/flow/api/runtimes/latestConfig'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_tools_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request, _format_url_section
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_get_tool_setting_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/setting')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_samples_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/samples')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_tool_meta_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
tool_name = kwargs.pop('tool_name') # type: str
tool_type = kwargs.pop('tool_type') # type: str
endpoint_name = kwargs.pop('endpoint_name', None) # type: Optional[str]
flow_runtime_name = kwargs.pop('flow_runtime_name', None) # type: Optional[str]
flow_id = kwargs.pop('flow_id', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/meta')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['toolName'] = _SERIALIZER.query("tool_name", tool_name, 'str')
query_parameters['toolType'] = _SERIALIZER.query("tool_type", tool_type, 'str')
if endpoint_name is not None:
query_parameters['endpointName'] = _SERIALIZER.query("endpoint_name", endpoint_name, 'str')
if flow_runtime_name is not None:
query_parameters['flowRuntimeName'] = _SERIALIZER.query("flow_runtime_name", flow_runtime_name, 'str')
if flow_id is not None:
query_parameters['flowId'] = _SERIALIZER.query("flow_id", flow_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_tool_meta_v2_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
flow_runtime_name = kwargs.pop('flow_runtime_name', None) # type: Optional[str]
flow_id = kwargs.pop('flow_id', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/meta-v2')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if flow_runtime_name is not None:
query_parameters['flowRuntimeName'] = _SERIALIZER.query("flow_runtime_name", flow_runtime_name, 'str')
if flow_id is not None:
query_parameters['flowId'] = _SERIALIZER.query("flow_id", flow_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_package_tools_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
flow_runtime_name = kwargs.pop('flow_runtime_name', None) # type: Optional[str]
flow_id = kwargs.pop('flow_id', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/packageTools')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if flow_runtime_name is not None:
query_parameters['flowRuntimeName'] = _SERIALIZER.query("flow_runtime_name", flow_runtime_name, 'str')
if flow_id is not None:
query_parameters['flowId'] = _SERIALIZER.query("flow_id", flow_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_dynamic_list_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
flow_runtime_name = kwargs.pop('flow_runtime_name', None) # type: Optional[str]
flow_id = kwargs.pop('flow_id', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/dynamicList')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if flow_runtime_name is not None:
query_parameters['flowRuntimeName'] = _SERIALIZER.query("flow_runtime_name", flow_runtime_name, 'str')
if flow_id is not None:
query_parameters['flowId'] = _SERIALIZER.query("flow_id", flow_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_retrieve_tool_func_result_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
flow_runtime_name = kwargs.pop('flow_runtime_name', None) # type: Optional[str]
flow_id = kwargs.pop('flow_id', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/RetrieveToolFuncResult')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if flow_runtime_name is not None:
query_parameters['flowRuntimeName'] = _SERIALIZER.query("flow_runtime_name", flow_runtime_name, 'str')
if flow_id is not None:
query_parameters['flowId'] = _SERIALIZER.query("flow_id", flow_id, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
# fmt: on
class ToolsOperations(object):
"""ToolsOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace
def get_tool_setting(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.ToolSetting"
"""get_tool_setting.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ToolSetting, or the result of cls(response)
:rtype: ~flow.models.ToolSetting
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ToolSetting"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_tool_setting_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_tool_setting.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ToolSetting', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_tool_setting.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/setting'} # type: ignore
@distributed_trace
def get_samples(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> Dict[str, "_models.Tool"]
"""get_samples.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: dict mapping str to Tool, or the result of cls(response)
:rtype: dict[str, ~flow.models.Tool]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.Tool"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_samples_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_samples.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('{Tool}', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_samples.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/samples'} # type: ignore
@distributed_trace
def get_tool_meta(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
tool_name, # type: str
tool_type, # type: str
endpoint_name=None, # type: Optional[str]
flow_runtime_name=None, # type: Optional[str]
flow_id=None, # type: Optional[str]
data=None, # type: Optional[str]
**kwargs # type: Any
):
# type: (...) -> str
"""get_tool_meta.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param tool_name:
:type tool_name: str
:param tool_type:
:type tool_type: str
:param endpoint_name:
:type endpoint_name: str
:param flow_runtime_name:
:type flow_runtime_name: str
:param flow_id:
:type flow_id: str
:param data:
:type data: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "text/plain") # type: Optional[str]
_content = data
request = build_get_tool_meta_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
tool_name=tool_name,
tool_type=tool_type,
content=_content,
endpoint_name=endpoint_name,
flow_runtime_name=flow_runtime_name,
flow_id=flow_id,
template_url=self.get_tool_meta.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_tool_meta.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/meta'} # type: ignore
@distributed_trace
def get_tool_meta_v2(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_runtime_name=None, # type: Optional[str]
flow_id=None, # type: Optional[str]
body=None, # type: Optional["_models.GenerateToolMetaRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.ToolMetaDto"
"""get_tool_meta_v2.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_runtime_name:
:type flow_runtime_name: str
:param flow_id:
:type flow_id: str
:param body:
:type body: ~flow.models.GenerateToolMetaRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ToolMetaDto, or the result of cls(response)
:rtype: ~flow.models.ToolMetaDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ToolMetaDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'GenerateToolMetaRequest')
else:
_json = None
request = build_get_tool_meta_v2_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
flow_runtime_name=flow_runtime_name,
flow_id=flow_id,
template_url=self.get_tool_meta_v2.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ToolMetaDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_tool_meta_v2.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/meta-v2'} # type: ignore
@distributed_trace
def get_package_tools(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_runtime_name=None, # type: Optional[str]
flow_id=None, # type: Optional[str]
**kwargs # type: Any
):
# type: (...) -> Dict[str, "_models.Tool"]
"""get_package_tools.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_runtime_name:
:type flow_runtime_name: str
:param flow_id:
:type flow_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: dict mapping str to Tool, or the result of cls(response)
:rtype: dict[str, ~flow.models.Tool]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.Tool"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_package_tools_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_runtime_name=flow_runtime_name,
flow_id=flow_id,
template_url=self.get_package_tools.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('{Tool}', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_package_tools.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/packageTools'} # type: ignore
@distributed_trace
def get_dynamic_list(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_runtime_name=None, # type: Optional[str]
flow_id=None, # type: Optional[str]
body=None, # type: Optional["_models.GetDynamicListRequest"]
**kwargs # type: Any
):
# type: (...) -> List[Any]
"""get_dynamic_list.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_runtime_name:
:type flow_runtime_name: str
:param flow_id:
:type flow_id: str
:param body:
:type body: ~flow.models.GetDynamicListRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of any, or the result of cls(response)
:rtype: list[any]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List[Any]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'GetDynamicListRequest')
else:
_json = None
request = build_get_dynamic_list_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
flow_runtime_name=flow_runtime_name,
flow_id=flow_id,
template_url=self.get_dynamic_list.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[object]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_dynamic_list.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/dynamicList'} # type: ignore
@distributed_trace
def retrieve_tool_func_result(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_runtime_name=None, # type: Optional[str]
flow_id=None, # type: Optional[str]
body=None, # type: Optional["_models.RetrieveToolFuncResultRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.ToolFuncResponse"
"""retrieve_tool_func_result.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_runtime_name:
:type flow_runtime_name: str
:param flow_id:
:type flow_id: str
:param body:
:type body: ~flow.models.RetrieveToolFuncResultRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ToolFuncResponse, or the result of cls(response)
:rtype: ~flow.models.ToolFuncResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ToolFuncResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'RetrieveToolFuncResultRequest')
else:
_json = None
request = build_retrieve_tool_func_result_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
flow_runtime_name=flow_runtime_name,
flow_id=flow_id,
template_url=self.retrieve_tool_func_result.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ToolFuncResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
retrieve_tool_func_result.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/RetrieveToolFuncResult'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_flow_sessions_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request, _format_url_section
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, Union
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_create_flow_session_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
session_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"sessionId": _SERIALIZER.url("session_id", session_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_flow_session_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
session_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"sessionId": _SERIALIZER.url("session_id", session_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_delete_flow_session_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
session_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"sessionId": _SERIALIZER.url("session_id", session_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="DELETE",
url=url,
headers=header_parameters,
**kwargs
)
def build_poll_operation_status_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
session_id, # type: str
action_type, # type: Union[str, "_models.SetupFlowSessionAction"]
location, # type: str
operation_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
api_version = kwargs.pop('api_version', "1.0.0") # type: Optional[str]
type = kwargs.pop('type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}/{actionType}/locations/{location}/operations/{operationId}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"sessionId": _SERIALIZER.url("session_id", session_id, 'str'),
"actionType": _SERIALIZER.url("action_type", action_type, 'str'),
"location": _SERIALIZER.url("location", location, 'str'),
"operationId": _SERIALIZER.url("operation_id", operation_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if api_version is not None:
query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
if type is not None:
query_parameters['type'] = _SERIALIZER.query("type", type, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_standby_pools_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/standbypools')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
# fmt: on
class FlowSessionsOperations(object):
"""FlowSessionsOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace
def create_flow_session(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
session_id, # type: str
body=None, # type: Optional["_models.CreateFlowSessionRequest"]
**kwargs # type: Any
):
# type: (...) -> Any
"""create_flow_session.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param session_id:
:type session_id: str
:param body:
:type body: ~flow.models.CreateFlowSessionRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: any, or the result of cls(response)
:rtype: any
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Any]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowSessionRequest')
else:
_json = None
request = build_create_flow_session_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
session_id=session_id,
content_type=content_type,
json=_json,
template_url=self.create_flow_session.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
if response.status_code == 200:
deserialized = self._deserialize('object', pipeline_response)
if response.status_code == 202:
deserialized = self._deserialize('object', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_flow_session.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}'} # type: ignore
@distributed_trace
def get_flow_session(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
session_id, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.GetTrainingSessionDto"
"""get_flow_session.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param session_id:
:type session_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: GetTrainingSessionDto, or the result of cls(response)
:rtype: ~flow.models.GetTrainingSessionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.GetTrainingSessionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_session_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
session_id=session_id,
template_url=self.get_flow_session.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('GetTrainingSessionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_session.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}'} # type: ignore
@distributed_trace
def delete_flow_session(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
session_id, # type: str
**kwargs # type: Any
):
# type: (...) -> Any
"""delete_flow_session.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param session_id:
:type session_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: any, or the result of cls(response)
:rtype: any
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Any]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_delete_flow_session_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
session_id=session_id,
template_url=self.delete_flow_session.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
if response.status_code == 200:
deserialized = self._deserialize('object', pipeline_response)
if response.status_code == 202:
deserialized = self._deserialize('object', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
delete_flow_session.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}'} # type: ignore
@distributed_trace
def poll_operation_status(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
session_id, # type: str
action_type, # type: Union[str, "_models.SetupFlowSessionAction"]
location, # type: str
operation_id, # type: str
api_version="1.0.0", # type: Optional[str]
type=None, # type: Optional[str]
**kwargs # type: Any
):
# type: (...) -> Any
"""poll_operation_status.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param session_id:
:type session_id: str
:param action_type:
:type action_type: str or ~flow.models.SetupFlowSessionAction
:param location:
:type location: str
:param operation_id:
:type operation_id: str
:param api_version: Api Version. The default value is "1.0.0".
:type api_version: str
:param type:
:type type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: any, or the result of cls(response)
:rtype: any
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Any]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_poll_operation_status_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
session_id=session_id,
action_type=action_type,
location=location,
operation_id=operation_id,
api_version=api_version,
type=type,
template_url=self.poll_operation_status.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('object', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
poll_operation_status.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}/{actionType}/locations/{location}/operations/{operationId}'} # type: ignore
@distributed_trace
def get_standby_pools(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> List["_models.StandbyPoolProperties"]
"""get_standby_pools.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of StandbyPoolProperties, or the result of cls(response)
:rtype: list[~flow.models.StandbyPoolProperties]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.StandbyPoolProperties"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_standby_pools_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_standby_pools.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[StandbyPoolProperties]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_standby_pools.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/standbypools'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_flow_sessions_admin_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request, _format_url_section
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, Optional, TypeVar
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_create_flow_session_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
session_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
waitfor_completion = kwargs.pop('waitfor_completion', False) # type: Optional[bool]
accept = "text/plain, application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessionsAdmin/{sessionId}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"sessionId": _SERIALIZER.url("session_id", session_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if waitfor_completion is not None:
query_parameters['waitforCompletion'] = _SERIALIZER.query("waitfor_completion", waitfor_completion, 'bool')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
# fmt: on
class FlowSessionsAdminOperations(object):
"""FlowSessionsAdminOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace
def create_flow_session(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
session_id, # type: str
waitfor_completion=False, # type: Optional[bool]
body=None, # type: Optional["_models.CreateFlowSessionRequest"]
**kwargs # type: Any
):
# type: (...) -> str
"""create_flow_session.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param session_id:
:type session_id: str
:param waitfor_completion:
:type waitfor_completion: bool
:param body:
:type body: ~flow.models.CreateFlowSessionRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowSessionRequest')
else:
_json = None
request = build_create_flow_session_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
session_id=session_id,
content_type=content_type,
json=_json,
waitfor_completion=waitfor_completion,
template_url=self.create_flow_session.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_flow_session.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessionsAdmin/{sessionId}'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_flows_provider_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request, _format_url_section
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, Optional, TypeVar
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_get_index_entity_by_id_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/v1.0/flows/getIndexEntities')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_updated_entity_ids_for_workspace_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/v1.0/flows/rebuildIndex')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
# fmt: on
class FlowsProviderOperations(object):
"""FlowsProviderOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace
def get_index_entity_by_id(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
body=None, # type: Optional["_models.UnversionedEntityRequestDto"]
**kwargs # type: Any
):
# type: (...) -> "_models.UnversionedEntityResponseDto"
"""get_index_entity_by_id.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param body:
:type body: ~flow.models.UnversionedEntityRequestDto
:keyword callable cls: A custom type or function that will be passed the direct response
:return: UnversionedEntityResponseDto, or the result of cls(response)
:rtype: ~flow.models.UnversionedEntityResponseDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.UnversionedEntityResponseDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'UnversionedEntityRequestDto')
else:
_json = None
request = build_get_index_entity_by_id_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
template_url=self.get_index_entity_by_id.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('UnversionedEntityResponseDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_index_entity_by_id.metadata = {'url': '/flow/v1.0/flows/getIndexEntities'} # type: ignore
@distributed_trace
def get_updated_entity_ids_for_workspace(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
body=None, # type: Optional["_models.UnversionedRebuildIndexDto"]
**kwargs # type: Any
):
# type: (...) -> "_models.UnversionedRebuildResponseDto"
"""get_updated_entity_ids_for_workspace.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param body:
:type body: ~flow.models.UnversionedRebuildIndexDto
:keyword callable cls: A custom type or function that will be passed the direct response
:return: UnversionedRebuildResponseDto, or the result of cls(response)
:rtype: ~flow.models.UnversionedRebuildResponseDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.UnversionedRebuildResponseDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'UnversionedRebuildIndexDto')
else:
_json = None
request = build_get_updated_entity_ids_for_workspace_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
template_url=self.get_updated_entity_ids_for_workspace.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('UnversionedRebuildResponseDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_updated_entity_ids_for_workspace.metadata = {'url': '/flow/v1.0/flows/rebuildIndex'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_flow_runs_admin_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import datetime
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request, _format_url_section
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, Union
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_submit_bulk_run_async_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
error_handling_mode = kwargs.pop('error_handling_mode', None) # type: Optional[Union[str, "_models.ErrorHandlingMode"]]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/submit')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"bulkRunId": _SERIALIZER.url("bulk_run_id", bulk_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if error_handling_mode is not None:
query_parameters['errorHandlingMode'] = _SERIALIZER.query("error_handling_mode", error_handling_mode, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_send_policy_validation_async_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/policy')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"bulkRunId": _SERIALIZER.url("bulk_run_id", bulk_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_check_policy_validation_async_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/policy')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"bulkRunId": _SERIALIZER.url("bulk_run_id", bulk_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_log_result_for_bulk_run_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/LogResult')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"bulkRunId": _SERIALIZER.url("bulk_run_id", bulk_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_storage_info_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/storageInfo')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_log_flow_run_event_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
runtime_version, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/flowRuns/{flowRunId}/runtime/{runtimeVersion}/logEvent')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
"runtimeVersion": _SERIALIZER.url("runtime_version", runtime_version, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_log_flow_run_event_v2_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
runtime_version = kwargs.pop('runtime_version', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/flowRuns/{flowRunId}/logEvent')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if runtime_version is not None:
query_parameters['runtimeVersion'] = _SERIALIZER.query("runtime_version", runtime_version, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_log_flow_run_terminated_event_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
last_checked_time = kwargs.pop('last_checked_time', None) # type: Optional[datetime.datetime]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/flowRuns/{flowRunId}/logTerminatedEvent')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"flowRunId": _SERIALIZER.url("flow_run_id", flow_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if last_checked_time is not None:
query_parameters['lastCheckedTime'] = _SERIALIZER.query("last_checked_time", last_checked_time, 'iso-8601')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_update_service_logs_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/serviceLogs')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"bulkRunId": _SERIALIZER.url("bulk_run_id", bulk_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_batch_update_service_logs_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/serviceLogs/batch')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"flowId": _SERIALIZER.url("flow_id", flow_id, 'str'),
"bulkRunId": _SERIALIZER.url("bulk_run_id", bulk_run_id, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
# fmt: on
class FlowRunsAdminOperations(object):
"""FlowRunsAdminOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace
def submit_bulk_run_async(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_run_id, # type: str
error_handling_mode=None, # type: Optional[Union[str, "_models.ErrorHandlingMode"]]
**kwargs # type: Any
):
# type: (...) -> "_models.SubmitBulkRunResponse"
"""submit_bulk_run_async.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_run_id:
:type bulk_run_id: str
:param error_handling_mode:
:type error_handling_mode: str or ~flow.models.ErrorHandlingMode
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SubmitBulkRunResponse, or the result of cls(response)
:rtype: ~flow.models.SubmitBulkRunResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.SubmitBulkRunResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_submit_bulk_run_async_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_run_id=bulk_run_id,
error_handling_mode=error_handling_mode,
template_url=self.submit_bulk_run_async.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('SubmitBulkRunResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
submit_bulk_run_async.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/submit'} # type: ignore
@distributed_trace
def send_policy_validation_async(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.PolicyValidationResponse"
"""send_policy_validation_async.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_run_id:
:type bulk_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: PolicyValidationResponse, or the result of cls(response)
:rtype: ~flow.models.PolicyValidationResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.PolicyValidationResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_send_policy_validation_async_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_run_id=bulk_run_id,
template_url=self.send_policy_validation_async.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('PolicyValidationResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
send_policy_validation_async.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/policy'} # type: ignore
@distributed_trace
def check_policy_validation_async(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.PolicyValidationResponse"
"""check_policy_validation_async.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_run_id:
:type bulk_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: PolicyValidationResponse, or the result of cls(response)
:rtype: ~flow.models.PolicyValidationResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.PolicyValidationResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_policy_validation_async_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_run_id=bulk_run_id,
template_url=self.check_policy_validation_async.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('PolicyValidationResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_policy_validation_async.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/policy'} # type: ignore
@distributed_trace
def log_result_for_bulk_run(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_run_id, # type: str
**kwargs # type: Any
):
# type: (...) -> List["_models.KeyValuePairStringObject"]
"""log_result_for_bulk_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_run_id:
:type bulk_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of KeyValuePairStringObject, or the result of cls(response)
:rtype: list[~flow.models.KeyValuePairStringObject]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.KeyValuePairStringObject"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_log_result_for_bulk_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_run_id=bulk_run_id,
template_url=self.log_result_for_bulk_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[KeyValuePairStringObject]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
log_result_for_bulk_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/LogResult'} # type: ignore
@distributed_trace
def get_storage_info(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.StorageInfo"
"""get_storage_info.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: StorageInfo, or the result of cls(response)
:rtype: ~flow.models.StorageInfo
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.StorageInfo"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_storage_info_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_storage_info.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('StorageInfo', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_storage_info.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/storageInfo'} # type: ignore
@distributed_trace
def log_flow_run_event(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
runtime_version, # type: str
**kwargs # type: Any
):
# type: (...) -> str
"""log_flow_run_event.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param runtime_version:
:type runtime_version: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_log_flow_run_event_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
runtime_version=runtime_version,
template_url=self.log_flow_run_event.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
log_flow_run_event.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/flowRuns/{flowRunId}/runtime/{runtimeVersion}/logEvent'} # type: ignore
@distributed_trace
def log_flow_run_event_v2(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
runtime_version=None, # type: Optional[str]
**kwargs # type: Any
):
# type: (...) -> str
"""log_flow_run_event_v2.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param runtime_version:
:type runtime_version: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_log_flow_run_event_v2_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
runtime_version=runtime_version,
template_url=self.log_flow_run_event_v2.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
log_flow_run_event_v2.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/flowRuns/{flowRunId}/logEvent'} # type: ignore
@distributed_trace
def log_flow_run_terminated_event(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
flow_run_id, # type: str
last_checked_time=None, # type: Optional[datetime.datetime]
**kwargs # type: Any
):
# type: (...) -> "_models.LogRunTerminatedEventDto"
"""log_flow_run_terminated_event.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param last_checked_time:
:type last_checked_time: ~datetime.datetime
:keyword callable cls: A custom type or function that will be passed the direct response
:return: LogRunTerminatedEventDto, or the result of cls(response)
:rtype: ~flow.models.LogRunTerminatedEventDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.LogRunTerminatedEventDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_log_flow_run_terminated_event_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
last_checked_time=last_checked_time,
template_url=self.log_flow_run_terminated_event.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('LogRunTerminatedEventDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
log_flow_run_terminated_event.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/flowRuns/{flowRunId}/logTerminatedEvent'} # type: ignore
@distributed_trace
def update_service_logs(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_run_id, # type: str
body=None, # type: Optional["_models.ServiceLogRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.Task"
"""update_service_logs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_run_id:
:type bulk_run_id: str
:param body:
:type body: ~flow.models.ServiceLogRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: Task, or the result of cls(response)
:rtype: ~flow.models.Task
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.Task"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'ServiceLogRequest')
else:
_json = None
request = build_update_service_logs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_run_id=bulk_run_id,
content_type=content_type,
json=_json,
template_url=self.update_service_logs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('Task', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_service_logs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/serviceLogs'} # type: ignore
@distributed_trace
def batch_update_service_logs(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
flow_id, # type: str
bulk_run_id, # type: str
body=None, # type: Optional[List["_models.ServiceLogRequest"]]
**kwargs # type: Any
):
# type: (...) -> "_models.Task"
"""batch_update_service_logs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_run_id:
:type bulk_run_id: str
:param body:
:type body: list[~flow.models.ServiceLogRequest]
:keyword callable cls: A custom type or function that will be passed the direct response
:return: Task, or the result of cls(response)
:rtype: ~flow.models.Task
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.Task"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, '[ServiceLogRequest]')
else:
_json = None
request = build_batch_update_service_logs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_run_id=bulk_run_id,
content_type=content_type,
json=_json,
template_url=self.batch_update_service_logs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('Task', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
batch_update_service_logs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/serviceLogs/batch'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_flow_runtimes_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request, _format_url_section
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_create_runtime_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
async_call = kwargs.pop('async_call', False) # type: Optional[bool]
msi_token = kwargs.pop('msi_token', False) # type: Optional[bool]
skip_port_check = kwargs.pop('skip_port_check', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if async_call is not None:
query_parameters['asyncCall'] = _SERIALIZER.query("async_call", async_call, 'bool')
if msi_token is not None:
query_parameters['msiToken'] = _SERIALIZER.query("msi_token", msi_token, 'bool')
if skip_port_check is not None:
query_parameters['skipPortCheck'] = _SERIALIZER.query("skip_port_check", skip_port_check, 'bool')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_update_runtime_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
async_call = kwargs.pop('async_call', False) # type: Optional[bool]
msi_token = kwargs.pop('msi_token', False) # type: Optional[bool]
skip_port_check = kwargs.pop('skip_port_check', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if async_call is not None:
query_parameters['asyncCall'] = _SERIALIZER.query("async_call", async_call, 'bool')
if msi_token is not None:
query_parameters['msiToken'] = _SERIALIZER.query("msi_token", msi_token, 'bool')
if skip_port_check is not None:
query_parameters['skipPortCheck'] = _SERIALIZER.query("skip_port_check", skip_port_check, 'bool')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_runtime_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_delete_runtime_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
async_call = kwargs.pop('async_call', False) # type: Optional[bool]
msi_token = kwargs.pop('msi_token', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if async_call is not None:
query_parameters['asyncCall'] = _SERIALIZER.query("async_call", async_call, 'bool')
if msi_token is not None:
query_parameters['msiToken'] = _SERIALIZER.query("msi_token", msi_token, 'bool')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="DELETE",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_check_ci_availability_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
compute_instance_name = kwargs.pop('compute_instance_name') # type: str
custom_app_name = kwargs.pop('custom_app_name') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkCiAvailability')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['computeInstanceName'] = _SERIALIZER.query("compute_instance_name", compute_instance_name, 'str')
query_parameters['customAppName'] = _SERIALIZER.query("custom_app_name", custom_app_name, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_check_mir_availability_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
endpoint_name = kwargs.pop('endpoint_name') # type: str
deployment_name = kwargs.pop('deployment_name') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkMirAvailability')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['endpointName'] = _SERIALIZER.query("endpoint_name", endpoint_name, 'str')
query_parameters['deploymentName'] = _SERIALIZER.query("deployment_name", deployment_name, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_check_runtime_upgrade_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/needUpgrade')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_runtime_capability_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/capability')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_runtime_latest_config_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/latestConfig')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_list_runtimes_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
# fmt: on
class FlowRuntimesOperations(object):
"""FlowRuntimesOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace
def create_runtime(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
async_call=False, # type: Optional[bool]
msi_token=False, # type: Optional[bool]
skip_port_check=False, # type: Optional[bool]
body=None, # type: Optional["_models.CreateFlowRuntimeRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeDto"
"""create_runtime.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:param skip_port_check:
:type skip_port_check: bool
:param body:
:type body: ~flow.models.CreateFlowRuntimeRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowRuntimeRequest')
else:
_json = None
request = build_create_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
content_type=content_type,
json=_json,
async_call=async_call,
msi_token=msi_token,
skip_port_check=skip_port_check,
template_url=self.create_runtime.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace
def update_runtime(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
async_call=False, # type: Optional[bool]
msi_token=False, # type: Optional[bool]
skip_port_check=False, # type: Optional[bool]
body=None, # type: Optional["_models.UpdateFlowRuntimeRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeDto"
"""update_runtime.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:param skip_port_check:
:type skip_port_check: bool
:param body:
:type body: ~flow.models.UpdateFlowRuntimeRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'UpdateFlowRuntimeRequest')
else:
_json = None
request = build_update_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
content_type=content_type,
json=_json,
async_call=async_call,
msi_token=msi_token,
skip_port_check=skip_port_check,
template_url=self.update_runtime.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace
def get_runtime(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeDto"
"""get_runtime.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
template_url=self.get_runtime.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace
def delete_runtime(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
async_call=False, # type: Optional[bool]
msi_token=False, # type: Optional[bool]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeDto"
"""delete_runtime.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_delete_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
async_call=async_call,
msi_token=msi_token,
template_url=self.delete_runtime.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
delete_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace
def check_ci_availability(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
compute_instance_name, # type: str
custom_app_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.AvailabilityResponse"
"""check_ci_availability.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param compute_instance_name:
:type compute_instance_name: str
:param custom_app_name:
:type custom_app_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: AvailabilityResponse, or the result of cls(response)
:rtype: ~flow.models.AvailabilityResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.AvailabilityResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_ci_availability_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
compute_instance_name=compute_instance_name,
custom_app_name=custom_app_name,
template_url=self.check_ci_availability.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('AvailabilityResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_ci_availability.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkCiAvailability'} # type: ignore
@distributed_trace
def check_mir_availability(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
endpoint_name, # type: str
deployment_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.AvailabilityResponse"
"""check_mir_availability.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param endpoint_name:
:type endpoint_name: str
:param deployment_name:
:type deployment_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: AvailabilityResponse, or the result of cls(response)
:rtype: ~flow.models.AvailabilityResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.AvailabilityResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_mir_availability_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
endpoint_name=endpoint_name,
deployment_name=deployment_name,
template_url=self.check_mir_availability.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('AvailabilityResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_mir_availability.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkMirAvailability'} # type: ignore
@distributed_trace
def check_runtime_upgrade(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> bool
"""check_runtime_upgrade.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: bool, or the result of cls(response)
:rtype: bool
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[bool]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_runtime_upgrade_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
template_url=self.check_runtime_upgrade.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('bool', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_runtime_upgrade.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/needUpgrade'} # type: ignore
@distributed_trace
def get_runtime_capability(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeCapability"
"""get_runtime_capability.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeCapability, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeCapability
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeCapability"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_capability_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
template_url=self.get_runtime_capability.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRuntimeCapability', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime_capability.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/capability'} # type: ignore
@distributed_trace
def get_runtime_latest_config(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.RuntimeConfiguration"
"""get_runtime_latest_config.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: RuntimeConfiguration, or the result of cls(response)
:rtype: ~flow.models.RuntimeConfiguration
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.RuntimeConfiguration"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_latest_config_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_runtime_latest_config.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('RuntimeConfiguration', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime_latest_config.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/latestConfig'} # type: ignore
@distributed_trace
def list_runtimes(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> List["_models.FlowRuntimeDto"]
"""list_runtimes.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of FlowRuntimeDto, or the result of cls(response)
:rtype: list[~flow.models.FlowRuntimeDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.FlowRuntimeDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_runtimes_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_runtimes.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[FlowRuntimeDto]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_runtimes.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/__init__.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from ._bulk_runs_operations import BulkRunsOperations
from ._connection_operations import ConnectionOperations
from ._connections_operations import ConnectionsOperations
from ._flow_runs_admin_operations import FlowRunsAdminOperations
from ._flow_runtimes_operations import FlowRuntimesOperations
from ._flow_runtimes_workspace_independent_operations import FlowRuntimesWorkspaceIndependentOperations
from ._flows_operations import FlowsOperations
from ._flow_sessions_operations import FlowSessionsOperations
from ._flows_provider_operations import FlowsProviderOperations
from ._tools_operations import ToolsOperations
__all__ = [
'BulkRunsOperations',
'ConnectionOperations',
'ConnectionsOperations',
'FlowRunsAdminOperations',
'FlowRuntimesOperations',
'FlowRuntimesWorkspaceIndependentOperations',
'FlowsOperations',
'FlowSessionsOperations',
'FlowsProviderOperations',
'ToolsOperations',
]
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_connections_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request, _format_url_section
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_create_connection_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_update_connection_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_connection_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_delete_connection_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="DELETE",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_connection_with_secrets_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}/listsecrets')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_list_connections_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_list_connection_specs_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/specs')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_list_azure_open_ai_deployments_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}/AzureOpenAIDeployments')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
# fmt: on
class ConnectionsOperations(object):
"""ConnectionsOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace
def create_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
body=None, # type: Optional["_models.CreateOrUpdateConnectionRequestDto"]
**kwargs # type: Any
):
# type: (...) -> "_models.ConnectionDto"
"""create_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:param body:
:type body: ~flow.models.CreateOrUpdateConnectionRequestDto
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionDto, or the result of cls(response)
:rtype: ~flow.models.ConnectionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateOrUpdateConnectionRequestDto')
else:
_json = None
request = build_create_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
content_type=content_type,
json=_json,
template_url=self.create_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}'} # type: ignore
@distributed_trace
def update_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
body=None, # type: Optional["_models.CreateOrUpdateConnectionRequestDto"]
**kwargs # type: Any
):
# type: (...) -> "_models.ConnectionDto"
"""update_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:param body:
:type body: ~flow.models.CreateOrUpdateConnectionRequestDto
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionDto, or the result of cls(response)
:rtype: ~flow.models.ConnectionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateOrUpdateConnectionRequestDto')
else:
_json = None
request = build_update_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
content_type=content_type,
json=_json,
template_url=self.update_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}'} # type: ignore
@distributed_trace
def get_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.ConnectionDto"
"""get_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionDto, or the result of cls(response)
:rtype: ~flow.models.ConnectionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
template_url=self.get_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}'} # type: ignore
@distributed_trace
def delete_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.ConnectionDto"
"""delete_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionDto, or the result of cls(response)
:rtype: ~flow.models.ConnectionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_delete_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
template_url=self.delete_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
delete_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}'} # type: ignore
@distributed_trace
def get_connection_with_secrets(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.ConnectionDto"
"""get_connection_with_secrets.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionDto, or the result of cls(response)
:rtype: ~flow.models.ConnectionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_connection_with_secrets_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
template_url=self.get_connection_with_secrets.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_connection_with_secrets.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}/listsecrets'} # type: ignore
@distributed_trace
def list_connections(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> List["_models.ConnectionDto"]
"""list_connections.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of ConnectionDto, or the result of cls(response)
:rtype: list[~flow.models.ConnectionDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ConnectionDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_connections_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_connections.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[ConnectionDto]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_connections.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections'} # type: ignore
@distributed_trace
def list_connection_specs(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> List["_models.WorkspaceConnectionSpec"]
"""list_connection_specs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of WorkspaceConnectionSpec, or the result of cls(response)
:rtype: list[~flow.models.WorkspaceConnectionSpec]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.WorkspaceConnectionSpec"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_connection_specs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_connection_specs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[WorkspaceConnectionSpec]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_connection_specs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/specs'} # type: ignore
@distributed_trace
def list_azure_open_ai_deployments(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> List["_models.AzureOpenAIDeploymentDto"]
"""list_azure_open_ai_deployments.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of AzureOpenAIDeploymentDto, or the result of cls(response)
:rtype: list[~flow.models.AzureOpenAIDeploymentDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.AzureOpenAIDeploymentDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_azure_open_ai_deployments_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
template_url=self.list_azure_open_ai_deployments.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[AzureOpenAIDeploymentDto]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_azure_open_ai_deployments.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}/AzureOpenAIDeployments'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/_azure_machine_learning_designer_service_client.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from copy import deepcopy
from typing import Any, Awaitable, Optional
from azure.core import AsyncPipelineClient
from azure.core.rest import AsyncHttpResponse, HttpRequest
from msrest import Deserializer, Serializer
from .. import models
from ._configuration import AzureMachineLearningDesignerServiceClientConfiguration
from .operations import BulkRunsOperations, ConnectionOperations, ConnectionsOperations, FlowRunsAdminOperations, FlowRuntimesOperations, FlowRuntimesWorkspaceIndependentOperations, FlowSessionsOperations, FlowsOperations, FlowsProviderOperations, ToolsOperations
class AzureMachineLearningDesignerServiceClient:
"""AzureMachineLearningDesignerServiceClient.
:ivar bulk_runs: BulkRunsOperations operations
:vartype bulk_runs: flow.aio.operations.BulkRunsOperations
:ivar connection: ConnectionOperations operations
:vartype connection: flow.aio.operations.ConnectionOperations
:ivar connections: ConnectionsOperations operations
:vartype connections: flow.aio.operations.ConnectionsOperations
:ivar flow_runs_admin: FlowRunsAdminOperations operations
:vartype flow_runs_admin: flow.aio.operations.FlowRunsAdminOperations
:ivar flow_runtimes: FlowRuntimesOperations operations
:vartype flow_runtimes: flow.aio.operations.FlowRuntimesOperations
:ivar flow_runtimes_workspace_independent: FlowRuntimesWorkspaceIndependentOperations
operations
:vartype flow_runtimes_workspace_independent:
flow.aio.operations.FlowRuntimesWorkspaceIndependentOperations
:ivar flows: FlowsOperations operations
:vartype flows: flow.aio.operations.FlowsOperations
:ivar flow_sessions: FlowSessionsOperations operations
:vartype flow_sessions: flow.aio.operations.FlowSessionsOperations
:ivar flows_provider: FlowsProviderOperations operations
:vartype flows_provider: flow.aio.operations.FlowsProviderOperations
:ivar tools: ToolsOperations operations
:vartype tools: flow.aio.operations.ToolsOperations
:param base_url: Service URL. Default value is ''.
:type base_url: str
:param api_version: Api Version. The default value is "1.0.0".
:type api_version: str
"""
def __init__(
self,
base_url: str = "",
api_version: Optional[str] = "1.0.0",
**kwargs: Any
) -> None:
self._config = AzureMachineLearningDesignerServiceClientConfiguration(api_version=api_version, **kwargs)
self._client = AsyncPipelineClient(base_url=base_url, config=self._config, **kwargs)
client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)}
self._serialize = Serializer(client_models)
self._deserialize = Deserializer(client_models)
self._serialize.client_side_validation = False
self.bulk_runs = BulkRunsOperations(self._client, self._config, self._serialize, self._deserialize)
self.connection = ConnectionOperations(self._client, self._config, self._serialize, self._deserialize)
self.connections = ConnectionsOperations(self._client, self._config, self._serialize, self._deserialize)
self.flow_runs_admin = FlowRunsAdminOperations(self._client, self._config, self._serialize, self._deserialize)
self.flow_runtimes = FlowRuntimesOperations(self._client, self._config, self._serialize, self._deserialize)
self.flow_runtimes_workspace_independent = FlowRuntimesWorkspaceIndependentOperations(self._client, self._config, self._serialize, self._deserialize)
self.flows = FlowsOperations(self._client, self._config, self._serialize, self._deserialize)
self.flow_sessions = FlowSessionsOperations(self._client, self._config, self._serialize, self._deserialize)
self.flows_provider = FlowsProviderOperations(self._client, self._config, self._serialize, self._deserialize)
self.tools = ToolsOperations(self._client, self._config, self._serialize, self._deserialize)
def _send_request(
self,
request: HttpRequest,
**kwargs: Any
) -> Awaitable[AsyncHttpResponse]:
"""Runs the network request through the client's chained policies.
>>> from azure.core.rest import HttpRequest
>>> request = HttpRequest("GET", "https://www.example.org/")
<HttpRequest [GET], url: 'https://www.example.org/'>
>>> response = await client._send_request(request)
<AsyncHttpResponse: 200 OK>
For more information on this code flow, see https://aka.ms/azsdk/python/protocol/quickstart
:param request: The network request you want to make. Required.
:type request: ~azure.core.rest.HttpRequest
:keyword bool stream: Whether the response payload will be streamed. Defaults to False.
:return: The response of your network call. Does not do error handling on your response.
:rtype: ~azure.core.rest.AsyncHttpResponse
"""
request_copy = deepcopy(request)
request_copy.url = self._client.format_url(request_copy.url)
return self._client.send_request(request_copy, **kwargs)
async def close(self) -> None:
await self._client.close()
async def __aenter__(self) -> "AzureMachineLearningDesignerServiceClient":
await self._client.__aenter__()
return self
async def __aexit__(self, *exc_details) -> None:
await self._client.__aexit__(*exc_details)
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/_configuration.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import Any, Optional
from azure.core.configuration import Configuration
from azure.core.pipeline import policies
VERSION = "unknown"
class AzureMachineLearningDesignerServiceClientConfiguration(Configuration):
"""Configuration for AzureMachineLearningDesignerServiceClient.
Note that all parameters used to create this instance are saved as instance
attributes.
:param api_version: Api Version. The default value is "1.0.0".
:type api_version: str
"""
def __init__(
self,
api_version: Optional[str] = "1.0.0",
**kwargs: Any
) -> None:
super(AzureMachineLearningDesignerServiceClientConfiguration, self).__init__(**kwargs)
self.api_version = api_version
kwargs.setdefault('sdk_moniker', 'azuremachinelearningdesignerserviceclient/{}'.format(VERSION))
self._configure(**kwargs)
def _configure(
self,
**kwargs: Any
) -> None:
self.user_agent_policy = kwargs.get('user_agent_policy') or policies.UserAgentPolicy(**kwargs)
self.headers_policy = kwargs.get('headers_policy') or policies.HeadersPolicy(**kwargs)
self.proxy_policy = kwargs.get('proxy_policy') or policies.ProxyPolicy(**kwargs)
self.logging_policy = kwargs.get('logging_policy') or policies.NetworkTraceLoggingPolicy(**kwargs)
self.http_logging_policy = kwargs.get('http_logging_policy') or policies.HttpLoggingPolicy(**kwargs)
self.retry_policy = kwargs.get('retry_policy') or policies.AsyncRetryPolicy(**kwargs)
self.custom_hook_policy = kwargs.get('custom_hook_policy') or policies.CustomHookPolicy(**kwargs)
self.redirect_policy = kwargs.get('redirect_policy') or policies.AsyncRedirectPolicy(**kwargs)
self.authentication_policy = kwargs.get('authentication_policy')
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/_patch.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# coding=utf-8
# --------------------------------------------------------------------------
#
# Copyright (c) Microsoft Corporation. All rights reserved.
#
# The MIT License (MIT)
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the ""Software""), to
# deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
#
# --------------------------------------------------------------------------
# This file is used for handwritten extensions to the generated code. Example:
# https://github.com/Azure/azure-sdk-for-python/blob/main/doc/dev/customize_code/how-to-patch-sdk-code.md
def patch_sdk():
pass | 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/__init__.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from ._azure_machine_learning_designer_service_client import AzureMachineLearningDesignerServiceClient
__all__ = ['AzureMachineLearningDesignerServiceClient']
# `._patch.py` is used for handwritten extensions to the generated code
# Example: https://github.com/Azure/azure-sdk-for-python/blob/main/doc/dev/customize_code/how-to-patch-sdk-code.md
from ._patch import patch_sdk
patch_sdk()
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_bulk_runs_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, Union
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._bulk_runs_operations import build_cancel_flow_run_request, build_clone_flow_from_flow_run_request, build_get_flow_child_runs_request, build_get_flow_node_run_base_path_request, build_get_flow_node_runs_request, build_get_flow_run_info_request, build_get_flow_run_log_content_request, build_submit_bulk_run_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class BulkRunsOperations:
"""BulkRunsOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace_async
async def submit_bulk_run(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
body: Optional["_models.SubmitBulkRunRequest"] = None,
**kwargs: Any
) -> str:
"""submit_bulk_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param body:
:type body: ~flow.models.SubmitBulkRunRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'SubmitBulkRunRequest')
else:
_json = None
request = build_submit_bulk_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
template_url=self.submit_bulk_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202, 204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
if response.status_code == 200:
deserialized = self._deserialize('str', pipeline_response)
if response.status_code == 202:
deserialized = self._deserialize('str', pipeline_response)
if response.status_code == 204:
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
submit_bulk_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/submit'} # type: ignore
@distributed_trace_async
async def cancel_flow_run(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_run_id: str,
**kwargs: Any
) -> str:
"""cancel_flow_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_cancel_flow_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
template_url=self.cancel_flow_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
cancel_flow_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/cancel'} # type: ignore
@distributed_trace_async
async def clone_flow_from_flow_run(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_run_id: str,
body: Optional["_models.CreateFlowRequest"] = None,
**kwargs: Any
) -> "_models.FlowDto":
"""clone_flow_from_flow_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:param body:
:type body: ~flow.models.CreateFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowDto, or the result of cls(response)
:rtype: ~flow.models.FlowDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowRequest')
else:
_json = None
request = build_clone_flow_from_flow_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
content_type=content_type,
json=_json,
template_url=self.clone_flow_from_flow_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
clone_flow_from_flow_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/clone'} # type: ignore
@distributed_trace_async
async def get_flow_run_info(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_run_id: str,
**kwargs: Any
) -> "_models.FlowRunInfo":
"""get_flow_run_info.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRunInfo, or the result of cls(response)
:rtype: ~flow.models.FlowRunInfo
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRunInfo"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_run_info_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
template_url=self.get_flow_run_info.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRunInfo', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_run_info.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}'} # type: ignore
@distributed_trace_async
async def get_flow_child_runs(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_run_id: str,
index: Optional[int] = None,
start_index: Optional[int] = None,
end_index: Optional[int] = None,
**kwargs: Any
) -> List[Any]:
"""get_flow_child_runs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:param index:
:type index: int
:param start_index:
:type start_index: int
:param end_index:
:type end_index: int
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of any, or the result of cls(response)
:rtype: list[any]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List[Any]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_child_runs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
index=index,
start_index=start_index,
end_index=end_index,
template_url=self.get_flow_child_runs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[object]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_child_runs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/childRuns'} # type: ignore
@distributed_trace_async
async def get_flow_node_runs(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_run_id: str,
node_name: str,
index: Optional[int] = None,
start_index: Optional[int] = None,
end_index: Optional[int] = None,
aggregation: Optional[bool] = False,
**kwargs: Any
) -> List[Any]:
"""get_flow_node_runs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:param node_name:
:type node_name: str
:param index:
:type index: int
:param start_index:
:type start_index: int
:param end_index:
:type end_index: int
:param aggregation:
:type aggregation: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of any, or the result of cls(response)
:rtype: list[any]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List[Any]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_node_runs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
node_name=node_name,
index=index,
start_index=start_index,
end_index=end_index,
aggregation=aggregation,
template_url=self.get_flow_node_runs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[object]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_node_runs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/nodeRuns/{nodeName}'} # type: ignore
@distributed_trace_async
async def get_flow_node_run_base_path(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_run_id: str,
node_name: str,
**kwargs: Any
) -> "_models.FlowRunBasePath":
"""get_flow_node_run_base_path.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:param node_name:
:type node_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRunBasePath, or the result of cls(response)
:rtype: ~flow.models.FlowRunBasePath
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRunBasePath"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_node_run_base_path_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
node_name=node_name,
template_url=self.get_flow_node_run_base_path.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRunBasePath', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_node_run_base_path.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/nodeRuns/{nodeName}/basePath'} # type: ignore
@distributed_trace_async
async def get_flow_run_log_content(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_run_id: str,
**kwargs: Any
) -> str:
"""get_flow_run_log_content.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_run_log_content_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
template_url=self.get_flow_run_log_content.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_run_log_content.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/BulkRuns/{flowRunId}/logContent'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_flows_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, Union
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._flows_operations import build_cancel_bulk_test_run_request, build_cancel_flow_run_request, build_cancel_flow_test_request, build_clone_flow_from_flow_run_request, build_clone_flow_request, build_create_flow_from_sample_request, build_create_flow_request, build_delete_flow_session_request, build_deploy_flow_request, build_get_bulk_test_request, build_get_connection_override_settings_request, build_get_evaluate_flow_samples_request, build_get_flow_child_runs_request, build_get_flow_deploy_reserved_environment_variable_names_request, build_get_flow_inputs_request, build_get_flow_node_run_base_path_request, build_get_flow_node_runs_request, build_get_flow_request, build_get_flow_run_info_request, build_get_flow_run_log_content_request, build_get_flow_run_status_request, build_get_flow_session_status_request, build_get_flow_snapshot_request, build_get_flow_tools_request, build_get_samples_request, build_list_bulk_tests_request, build_list_flows_request, build_load_as_component_request, build_patch_flow_request, build_setup_flow_session_request, build_submit_flow_request, build_update_flow_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class FlowsOperations:
"""FlowsOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace_async
async def create_flow(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
experiment_id: Optional[str] = None,
body: Optional["_models.CreateFlowRequest"] = None,
**kwargs: Any
) -> "_models.FlowDto":
"""create_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.CreateFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowDto, or the result of cls(response)
:rtype: ~flow.models.FlowDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowRequest')
else:
_json = None
request = build_create_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
experiment_id=experiment_id,
template_url=self.create_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows'} # type: ignore
@distributed_trace_async
async def list_flows(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
experiment_id: Optional[str] = None,
owned_only: Optional[bool] = None,
flow_type: Optional[Union[str, "_models.FlowType"]] = None,
list_view_type: Optional[Union[str, "_models.ListViewType"]] = None,
**kwargs: Any
) -> List["_models.FlowBaseDto"]:
"""list_flows.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param experiment_id:
:type experiment_id: str
:param owned_only:
:type owned_only: bool
:param flow_type:
:type flow_type: str or ~flow.models.FlowType
:param list_view_type:
:type list_view_type: str or ~flow.models.ListViewType
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of FlowBaseDto, or the result of cls(response)
:rtype: list[~flow.models.FlowBaseDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.FlowBaseDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_flows_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
experiment_id=experiment_id,
owned_only=owned_only,
flow_type=flow_type,
list_view_type=list_view_type,
template_url=self.list_flows.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[FlowBaseDto]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_flows.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows'} # type: ignore
@distributed_trace_async
async def clone_flow(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
experiment_id: str,
body: Optional["_models.CreateFlowRequest"] = None,
**kwargs: Any
) -> "_models.FlowDto":
"""clone_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.CreateFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowDto, or the result of cls(response)
:rtype: ~flow.models.FlowDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowRequest')
else:
_json = None
request = build_clone_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
content_type=content_type,
experiment_id=experiment_id,
json=_json,
template_url=self.clone_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
clone_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/clone'} # type: ignore
@distributed_trace_async
async def create_flow_from_sample(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
experiment_id: Optional[str] = None,
body: Optional["_models.CreateFlowFromSampleRequest"] = None,
**kwargs: Any
) -> "_models.FlowDto":
"""create_flow_from_sample.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.CreateFlowFromSampleRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowDto, or the result of cls(response)
:rtype: ~flow.models.FlowDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowFromSampleRequest')
else:
_json = None
request = build_create_flow_from_sample_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
experiment_id=experiment_id,
template_url=self.create_flow_from_sample.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_flow_from_sample.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/fromsample'} # type: ignore
@distributed_trace_async
async def update_flow(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
experiment_id: str,
body: Optional["_models.UpdateFlowRequest"] = None,
**kwargs: Any
) -> str:
"""update_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.UpdateFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'UpdateFlowRequest')
else:
_json = None
request = build_update_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
content_type=content_type,
experiment_id=experiment_id,
json=_json,
template_url=self.update_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}'} # type: ignore
@distributed_trace_async
async def patch_flow(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
experiment_id: str,
body: Optional["_models.PatchFlowRequest"] = None,
**kwargs: Any
) -> str:
"""patch_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.PatchFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json-patch+json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'PatchFlowRequest')
else:
_json = None
request = build_patch_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
content_type=content_type,
experiment_id=experiment_id,
json=_json,
template_url=self.patch_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
patch_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}'} # type: ignore
@distributed_trace_async
async def get_flow(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
experiment_id: str,
**kwargs: Any
) -> "_models.FlowDto":
"""get_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowDto, or the result of cls(response)
:rtype: ~flow.models.FlowDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
experiment_id=experiment_id,
template_url=self.get_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}'} # type: ignore
@distributed_trace_async
async def submit_flow(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
experiment_id: str,
endpoint_name: Optional[str] = None,
body: Optional["_models.SubmitFlowRequest"] = None,
**kwargs: Any
) -> "_models.FlowRunResult":
"""submit_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param experiment_id:
:type experiment_id: str
:param endpoint_name:
:type endpoint_name: str
:param body:
:type body: ~flow.models.SubmitFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRunResult, or the result of cls(response)
:rtype: ~flow.models.FlowRunResult
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRunResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'SubmitFlowRequest')
else:
_json = None
request = build_submit_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
experiment_id=experiment_id,
json=_json,
endpoint_name=endpoint_name,
template_url=self.submit_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRunResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
submit_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/submit'} # type: ignore
@distributed_trace_async
async def get_flow_run_status(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
flow_run_id: str,
experiment_id: Optional[str] = None,
**kwargs: Any
) -> "_models.FlowRunResult":
"""get_flow_run_status.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param experiment_id:
:type experiment_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRunResult, or the result of cls(response)
:rtype: ~flow.models.FlowRunResult
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRunResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_run_status_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
experiment_id=experiment_id,
template_url=self.get_flow_run_status.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRunResult', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_run_status.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/{flowRunId}/status'} # type: ignore
@distributed_trace_async
async def get_flow_run_info(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
flow_run_id: str,
experiment_id: str,
**kwargs: Any
) -> "_models.FlowRunInfo":
"""get_flow_run_info.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param experiment_id:
:type experiment_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRunInfo, or the result of cls(response)
:rtype: ~flow.models.FlowRunInfo
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRunInfo"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_run_info_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
experiment_id=experiment_id,
template_url=self.get_flow_run_info.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRunInfo', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_run_info.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}'} # type: ignore
@distributed_trace_async
async def get_flow_child_runs(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
flow_run_id: str,
index: Optional[int] = None,
start_index: Optional[int] = None,
end_index: Optional[int] = None,
**kwargs: Any
) -> List[Any]:
"""get_flow_child_runs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param index:
:type index: int
:param start_index:
:type start_index: int
:param end_index:
:type end_index: int
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of any, or the result of cls(response)
:rtype: list[any]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List[Any]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_child_runs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
index=index,
start_index=start_index,
end_index=end_index,
template_url=self.get_flow_child_runs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[object]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_child_runs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/childRuns'} # type: ignore
@distributed_trace_async
async def get_flow_node_runs(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
flow_run_id: str,
node_name: str,
index: Optional[int] = None,
start_index: Optional[int] = None,
end_index: Optional[int] = None,
aggregation: Optional[bool] = False,
**kwargs: Any
) -> List[Any]:
"""get_flow_node_runs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param node_name:
:type node_name: str
:param index:
:type index: int
:param start_index:
:type start_index: int
:param end_index:
:type end_index: int
:param aggregation:
:type aggregation: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of any, or the result of cls(response)
:rtype: list[any]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List[Any]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_node_runs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
node_name=node_name,
index=index,
start_index=start_index,
end_index=end_index,
aggregation=aggregation,
template_url=self.get_flow_node_runs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[object]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_node_runs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/nodeRuns/{nodeName}'} # type: ignore
@distributed_trace_async
async def get_flow_node_run_base_path(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
flow_run_id: str,
node_name: str,
**kwargs: Any
) -> "_models.FlowRunBasePath":
"""get_flow_node_run_base_path.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param node_name:
:type node_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRunBasePath, or the result of cls(response)
:rtype: ~flow.models.FlowRunBasePath
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRunBasePath"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_node_run_base_path_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
node_name=node_name,
template_url=self.get_flow_node_run_base_path.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRunBasePath', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_node_run_base_path.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/nodeRuns/{nodeName}/basePath'} # type: ignore
@distributed_trace_async
async def clone_flow_from_flow_run(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
flow_run_id: str,
experiment_id: str,
body: Optional["_models.CreateFlowRequest"] = None,
**kwargs: Any
) -> "_models.FlowDto":
"""clone_flow_from_flow_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.CreateFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowDto, or the result of cls(response)
:rtype: ~flow.models.FlowDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowRequest')
else:
_json = None
request = build_clone_flow_from_flow_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
content_type=content_type,
experiment_id=experiment_id,
json=_json,
template_url=self.clone_flow_from_flow_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
clone_flow_from_flow_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/clone'} # type: ignore
@distributed_trace_async
async def list_bulk_tests(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
experiment_id: Optional[str] = None,
**kwargs: Any
) -> List["_models.BulkTestDto"]:
"""list_bulk_tests.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of BulkTestDto, or the result of cls(response)
:rtype: list[~flow.models.BulkTestDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.BulkTestDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_bulk_tests_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
experiment_id=experiment_id,
template_url=self.list_bulk_tests.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[BulkTestDto]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_bulk_tests.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/bulkTests'} # type: ignore
@distributed_trace_async
async def get_bulk_test(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
bulk_test_id: str,
**kwargs: Any
) -> "_models.BulkTestDto":
"""get_bulk_test.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_test_id:
:type bulk_test_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: BulkTestDto, or the result of cls(response)
:rtype: ~flow.models.BulkTestDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.BulkTestDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_bulk_test_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_test_id=bulk_test_id,
template_url=self.get_bulk_test.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('BulkTestDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_bulk_test.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/bulkTests/{bulkTestId}'} # type: ignore
@distributed_trace_async
async def get_samples(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
use_snapshot: Optional[bool] = False,
**kwargs: Any
) -> Dict[str, "_models.FlowSampleDto"]:
"""get_samples.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param use_snapshot:
:type use_snapshot: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: dict mapping str to FlowSampleDto, or the result of cls(response)
:rtype: dict[str, ~flow.models.FlowSampleDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.FlowSampleDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_samples_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
use_snapshot=use_snapshot,
template_url=self.get_samples.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('{FlowSampleDto}', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_samples.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/samples'} # type: ignore
@distributed_trace_async
async def get_evaluate_flow_samples(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
use_snapshot: Optional[bool] = False,
**kwargs: Any
) -> Dict[str, "_models.FlowSampleDto"]:
"""get_evaluate_flow_samples.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param use_snapshot:
:type use_snapshot: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: dict mapping str to FlowSampleDto, or the result of cls(response)
:rtype: dict[str, ~flow.models.FlowSampleDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.FlowSampleDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_evaluate_flow_samples_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
use_snapshot=use_snapshot,
template_url=self.get_evaluate_flow_samples.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('{FlowSampleDto}', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_evaluate_flow_samples.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/evaluateSamples'} # type: ignore
@distributed_trace_async
async def get_flow_deploy_reserved_environment_variable_names(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
**kwargs: Any
) -> List[str]:
"""get_flow_deploy_reserved_environment_variable_names.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of str, or the result of cls(response)
:rtype: list[str]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List[str]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_deploy_reserved_environment_variable_names_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_flow_deploy_reserved_environment_variable_names.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[str]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_deploy_reserved_environment_variable_names.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/DeployReservedEnvironmentVariableNames'} # type: ignore
@distributed_trace_async
async def deploy_flow(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
async_call: Optional[bool] = False,
msi_token: Optional[bool] = False,
body: Optional["_models.DeployFlowRequest"] = None,
**kwargs: Any
) -> str:
"""deploy_flow.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:param body:
:type body: ~flow.models.DeployFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'DeployFlowRequest')
else:
_json = None
request = build_deploy_flow_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
async_call=async_call,
msi_token=msi_token,
template_url=self.deploy_flow.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
deploy_flow.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/deploy'} # type: ignore
@distributed_trace_async
async def get_flow_run_log_content(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
flow_run_id: str,
**kwargs: Any
) -> str:
"""get_flow_run_log_content.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_run_log_content_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
template_url=self.get_flow_run_log_content.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_run_log_content.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/runs/{flowRunId}/logContent'} # type: ignore
@distributed_trace_async
async def cancel_flow_run(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_run_id: str,
**kwargs: Any
) -> str:
"""cancel_flow_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_run_id:
:type flow_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_cancel_flow_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_run_id=flow_run_id,
template_url=self.cancel_flow_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
cancel_flow_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/runs/{flowRunId}/cancel'} # type: ignore
@distributed_trace_async
async def cancel_flow_test(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
flow_run_id: str,
**kwargs: Any
) -> str:
"""cancel_flow_test.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_cancel_flow_test_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
template_url=self.cancel_flow_test.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
cancel_flow_test.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/flowTests/{flowRunId}/cancel'} # type: ignore
@distributed_trace_async
async def cancel_bulk_test_run(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
bulk_test_run_id: str,
**kwargs: Any
) -> str:
"""cancel_bulk_test_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param bulk_test_run_id:
:type bulk_test_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_cancel_bulk_test_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
bulk_test_run_id=bulk_test_run_id,
template_url=self.cancel_bulk_test_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
cancel_bulk_test_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/bulkTests/{bulkTestRunId}/cancel'} # type: ignore
@distributed_trace_async
async def get_flow_snapshot(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
body: Optional["_models.CreateFlowRequest"] = None,
**kwargs: Any
) -> "_models.FlowSnapshot":
"""get_flow_snapshot.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param body:
:type body: ~flow.models.CreateFlowRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowSnapshot, or the result of cls(response)
:rtype: ~flow.models.FlowSnapshot
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowSnapshot"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowRequest')
else:
_json = None
request = build_get_flow_snapshot_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
template_url=self.get_flow_snapshot.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowSnapshot', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_snapshot.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/FlowSnapshot'} # type: ignore
@distributed_trace_async
async def get_connection_override_settings(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
runtime_name: Optional[str] = None,
body: Optional["_models.FlowGraphReference"] = None,
**kwargs: Any
) -> List["_models.ConnectionOverrideSetting"]:
"""get_connection_override_settings.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:param body:
:type body: ~flow.models.FlowGraphReference
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of ConnectionOverrideSetting, or the result of cls(response)
:rtype: list[~flow.models.ConnectionOverrideSetting]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ConnectionOverrideSetting"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'FlowGraphReference')
else:
_json = None
request = build_get_connection_override_settings_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
runtime_name=runtime_name,
template_url=self.get_connection_override_settings.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[ConnectionOverrideSetting]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_connection_override_settings.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/connectionOverride'} # type: ignore
@distributed_trace_async
async def get_flow_inputs(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
body: Optional["_models.FlowGraphReference"] = None,
**kwargs: Any
) -> Dict[str, "_models.FlowInputDefinition"]:
"""get_flow_inputs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param body:
:type body: ~flow.models.FlowGraphReference
:keyword callable cls: A custom type or function that will be passed the direct response
:return: dict mapping str to FlowInputDefinition, or the result of cls(response)
:rtype: dict[str, ~flow.models.FlowInputDefinition]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.FlowInputDefinition"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'FlowGraphReference')
else:
_json = None
request = build_get_flow_inputs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
template_url=self.get_flow_inputs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('{FlowInputDefinition}', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_inputs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/flowInputs'} # type: ignore
@distributed_trace_async
async def load_as_component(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
body: Optional["_models.LoadFlowAsComponentRequest"] = None,
**kwargs: Any
) -> str:
"""load_as_component.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param body:
:type body: ~flow.models.LoadFlowAsComponentRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'LoadFlowAsComponentRequest')
else:
_json = None
request = build_load_as_component_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
template_url=self.load_as_component.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
load_as_component.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/LoadAsComponent'} # type: ignore
@distributed_trace_async
async def get_flow_tools(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
experiment_id: str,
flow_runtime_name: Optional[str] = None,
**kwargs: Any
) -> "_models.FlowToolsDto":
"""get_flow_tools.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:param flow_runtime_name:
:type flow_runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowToolsDto, or the result of cls(response)
:rtype: ~flow.models.FlowToolsDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowToolsDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_tools_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
experiment_id=experiment_id,
flow_runtime_name=flow_runtime_name,
template_url=self.get_flow_tools.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowToolsDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_tools.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/flowTools'} # type: ignore
@distributed_trace_async
async def setup_flow_session(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
experiment_id: str,
body: Optional["_models.SetupFlowSessionRequest"] = None,
**kwargs: Any
) -> Any:
"""setup_flow_session.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:param body:
:type body: ~flow.models.SetupFlowSessionRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: any, or the result of cls(response)
:rtype: any
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Any]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'SetupFlowSessionRequest')
else:
_json = None
request = build_setup_flow_session_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
content_type=content_type,
experiment_id=experiment_id,
json=_json,
template_url=self.setup_flow_session.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
if response.status_code == 200:
deserialized = self._deserialize('object', pipeline_response)
if response.status_code == 202:
deserialized = self._deserialize('object', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
setup_flow_session.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/sessions'} # type: ignore
@distributed_trace_async
async def delete_flow_session(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
experiment_id: str,
**kwargs: Any
) -> Any:
"""delete_flow_session.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: any, or the result of cls(response)
:rtype: any
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Any]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_delete_flow_session_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
experiment_id=experiment_id,
template_url=self.delete_flow_session.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
if response.status_code == 200:
deserialized = self._deserialize('object', pipeline_response)
if response.status_code == 202:
deserialized = self._deserialize('object', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
delete_flow_session.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/sessions'} # type: ignore
@distributed_trace_async
async def get_flow_session_status(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
experiment_id: str,
**kwargs: Any
) -> "_models.FlowSessionDto":
"""get_flow_session_status.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param experiment_id:
:type experiment_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowSessionDto, or the result of cls(response)
:rtype: ~flow.models.FlowSessionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowSessionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_session_status_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
experiment_id=experiment_id,
template_url=self.get_flow_session_status.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowSessionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_session_status.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Flows/{flowId}/sessions/status'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_connection_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, Union
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._connection_operations import build_create_connection_request, build_delete_connection_request, build_get_connection_request, build_list_connection_specs_request, build_list_connections_request, build_update_connection_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class ConnectionOperations:
"""ConnectionOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace_async
async def create_connection(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
connection_name: str,
body: Optional["_models.CreateOrUpdateConnectionRequest"] = None,
**kwargs: Any
) -> "_models.ConnectionEntity":
"""create_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:param body:
:type body: ~flow.models.CreateOrUpdateConnectionRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionEntity, or the result of cls(response)
:rtype: ~flow.models.ConnectionEntity
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionEntity"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateOrUpdateConnectionRequest')
else:
_json = None
request = build_create_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
content_type=content_type,
json=_json,
template_url=self.create_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionEntity', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}'} # type: ignore
@distributed_trace_async
async def update_connection(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
connection_name: str,
body: Optional["_models.CreateOrUpdateConnectionRequest"] = None,
**kwargs: Any
) -> "_models.ConnectionEntity":
"""update_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:param body:
:type body: ~flow.models.CreateOrUpdateConnectionRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionEntity, or the result of cls(response)
:rtype: ~flow.models.ConnectionEntity
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionEntity"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateOrUpdateConnectionRequest')
else:
_json = None
request = build_update_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
content_type=content_type,
json=_json,
template_url=self.update_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionEntity', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}'} # type: ignore
@distributed_trace_async
async def get_connection(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
connection_name: str,
**kwargs: Any
) -> "_models.ConnectionEntity":
"""get_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionEntity, or the result of cls(response)
:rtype: ~flow.models.ConnectionEntity
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionEntity"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
template_url=self.get_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionEntity', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}'} # type: ignore
@distributed_trace_async
async def delete_connection(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
connection_name: str,
connection_scope: Optional[Union[str, "_models.ConnectionScope"]] = None,
**kwargs: Any
) -> "_models.ConnectionEntity":
"""delete_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:param connection_scope:
:type connection_scope: str or ~flow.models.ConnectionScope
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionEntity, or the result of cls(response)
:rtype: ~flow.models.ConnectionEntity
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionEntity"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_delete_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
connection_scope=connection_scope,
template_url=self.delete_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionEntity', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
delete_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}'} # type: ignore
@distributed_trace_async
async def list_connections(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
**kwargs: Any
) -> List["_models.ConnectionEntity"]:
"""list_connections.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of ConnectionEntity, or the result of cls(response)
:rtype: list[~flow.models.ConnectionEntity]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ConnectionEntity"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_connections_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_connections.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[ConnectionEntity]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_connections.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection'} # type: ignore
@distributed_trace_async
async def list_connection_specs(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
**kwargs: Any
) -> List["_models.ConnectionSpec"]:
"""list_connection_specs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of ConnectionSpec, or the result of cls(response)
:rtype: list[~flow.models.ConnectionSpec]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ConnectionSpec"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_connection_specs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_connection_specs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[ConnectionSpec]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_connection_specs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/specs'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_flow_runtimes_workspace_independent_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import Any, Callable, Dict, Generic, Optional, TypeVar
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._flow_runtimes_workspace_independent_operations import build_get_runtime_latest_config_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class FlowRuntimesWorkspaceIndependentOperations:
"""FlowRuntimesWorkspaceIndependentOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace_async
async def get_runtime_latest_config(
self,
**kwargs: Any
) -> "_models.RuntimeConfiguration":
"""get_runtime_latest_config.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: RuntimeConfiguration, or the result of cls(response)
:rtype: ~flow.models.RuntimeConfiguration
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.RuntimeConfiguration"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_latest_config_request(
template_url=self.get_runtime_latest_config.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('RuntimeConfiguration', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime_latest_config.metadata = {'url': '/flow/api/runtimes/latestConfig'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_tools_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._tools_operations import build_get_dynamic_list_request, build_get_package_tools_request, build_get_samples_request, build_get_tool_meta_request, build_get_tool_meta_v2_request, build_get_tool_setting_request, build_retrieve_tool_func_result_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class ToolsOperations:
"""ToolsOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace_async
async def get_tool_setting(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
**kwargs: Any
) -> "_models.ToolSetting":
"""get_tool_setting.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ToolSetting, or the result of cls(response)
:rtype: ~flow.models.ToolSetting
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ToolSetting"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_tool_setting_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_tool_setting.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ToolSetting', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_tool_setting.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/setting'} # type: ignore
@distributed_trace_async
async def get_samples(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
**kwargs: Any
) -> Dict[str, "_models.Tool"]:
"""get_samples.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: dict mapping str to Tool, or the result of cls(response)
:rtype: dict[str, ~flow.models.Tool]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.Tool"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_samples_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_samples.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('{Tool}', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_samples.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/samples'} # type: ignore
@distributed_trace_async
async def get_tool_meta(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
tool_name: str,
tool_type: str,
endpoint_name: Optional[str] = None,
flow_runtime_name: Optional[str] = None,
flow_id: Optional[str] = None,
data: Optional[str] = None,
**kwargs: Any
) -> str:
"""get_tool_meta.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param tool_name:
:type tool_name: str
:param tool_type:
:type tool_type: str
:param endpoint_name:
:type endpoint_name: str
:param flow_runtime_name:
:type flow_runtime_name: str
:param flow_id:
:type flow_id: str
:param data:
:type data: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "text/plain") # type: Optional[str]
_content = data
request = build_get_tool_meta_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
tool_name=tool_name,
tool_type=tool_type,
content=_content,
endpoint_name=endpoint_name,
flow_runtime_name=flow_runtime_name,
flow_id=flow_id,
template_url=self.get_tool_meta.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_tool_meta.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/meta'} # type: ignore
@distributed_trace_async
async def get_tool_meta_v2(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_runtime_name: Optional[str] = None,
flow_id: Optional[str] = None,
body: Optional["_models.GenerateToolMetaRequest"] = None,
**kwargs: Any
) -> "_models.ToolMetaDto":
"""get_tool_meta_v2.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_runtime_name:
:type flow_runtime_name: str
:param flow_id:
:type flow_id: str
:param body:
:type body: ~flow.models.GenerateToolMetaRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ToolMetaDto, or the result of cls(response)
:rtype: ~flow.models.ToolMetaDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ToolMetaDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'GenerateToolMetaRequest')
else:
_json = None
request = build_get_tool_meta_v2_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
flow_runtime_name=flow_runtime_name,
flow_id=flow_id,
template_url=self.get_tool_meta_v2.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ToolMetaDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_tool_meta_v2.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/meta-v2'} # type: ignore
@distributed_trace_async
async def get_package_tools(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_runtime_name: Optional[str] = None,
flow_id: Optional[str] = None,
**kwargs: Any
) -> Dict[str, "_models.Tool"]:
"""get_package_tools.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_runtime_name:
:type flow_runtime_name: str
:param flow_id:
:type flow_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: dict mapping str to Tool, or the result of cls(response)
:rtype: dict[str, ~flow.models.Tool]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.Tool"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_package_tools_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_runtime_name=flow_runtime_name,
flow_id=flow_id,
template_url=self.get_package_tools.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('{Tool}', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_package_tools.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/packageTools'} # type: ignore
@distributed_trace_async
async def get_dynamic_list(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_runtime_name: Optional[str] = None,
flow_id: Optional[str] = None,
body: Optional["_models.GetDynamicListRequest"] = None,
**kwargs: Any
) -> List[Any]:
"""get_dynamic_list.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_runtime_name:
:type flow_runtime_name: str
:param flow_id:
:type flow_id: str
:param body:
:type body: ~flow.models.GetDynamicListRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of any, or the result of cls(response)
:rtype: list[any]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List[Any]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'GetDynamicListRequest')
else:
_json = None
request = build_get_dynamic_list_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
flow_runtime_name=flow_runtime_name,
flow_id=flow_id,
template_url=self.get_dynamic_list.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[object]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_dynamic_list.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/dynamicList'} # type: ignore
@distributed_trace_async
async def retrieve_tool_func_result(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_runtime_name: Optional[str] = None,
flow_id: Optional[str] = None,
body: Optional["_models.RetrieveToolFuncResultRequest"] = None,
**kwargs: Any
) -> "_models.ToolFuncResponse":
"""retrieve_tool_func_result.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_runtime_name:
:type flow_runtime_name: str
:param flow_id:
:type flow_id: str
:param body:
:type body: ~flow.models.RetrieveToolFuncResultRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ToolFuncResponse, or the result of cls(response)
:rtype: ~flow.models.ToolFuncResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ToolFuncResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'RetrieveToolFuncResultRequest')
else:
_json = None
request = build_retrieve_tool_func_result_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
flow_runtime_name=flow_runtime_name,
flow_id=flow_id,
template_url=self.retrieve_tool_func_result.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ToolFuncResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
retrieve_tool_func_result.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/RetrieveToolFuncResult'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_flow_sessions_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, Union
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._flow_sessions_operations import build_create_flow_session_request, build_delete_flow_session_request, build_get_flow_session_request, build_get_standby_pools_request, build_poll_operation_status_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class FlowSessionsOperations:
"""FlowSessionsOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace_async
async def create_flow_session(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
session_id: str,
body: Optional["_models.CreateFlowSessionRequest"] = None,
**kwargs: Any
) -> Any:
"""create_flow_session.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param session_id:
:type session_id: str
:param body:
:type body: ~flow.models.CreateFlowSessionRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: any, or the result of cls(response)
:rtype: any
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Any]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowSessionRequest')
else:
_json = None
request = build_create_flow_session_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
session_id=session_id,
content_type=content_type,
json=_json,
template_url=self.create_flow_session.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
if response.status_code == 200:
deserialized = self._deserialize('object', pipeline_response)
if response.status_code == 202:
deserialized = self._deserialize('object', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_flow_session.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}'} # type: ignore
@distributed_trace_async
async def get_flow_session(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
session_id: str,
**kwargs: Any
) -> "_models.GetTrainingSessionDto":
"""get_flow_session.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param session_id:
:type session_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: GetTrainingSessionDto, or the result of cls(response)
:rtype: ~flow.models.GetTrainingSessionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.GetTrainingSessionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_flow_session_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
session_id=session_id,
template_url=self.get_flow_session.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('GetTrainingSessionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_flow_session.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}'} # type: ignore
@distributed_trace_async
async def delete_flow_session(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
session_id: str,
**kwargs: Any
) -> Any:
"""delete_flow_session.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param session_id:
:type session_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: any, or the result of cls(response)
:rtype: any
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Any]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_delete_flow_session_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
session_id=session_id,
template_url=self.delete_flow_session.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
if response.status_code == 200:
deserialized = self._deserialize('object', pipeline_response)
if response.status_code == 202:
deserialized = self._deserialize('object', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
delete_flow_session.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}'} # type: ignore
@distributed_trace_async
async def poll_operation_status(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
session_id: str,
action_type: Union[str, "_models.SetupFlowSessionAction"],
location: str,
operation_id: str,
api_version: Optional[str] = "1.0.0",
type: Optional[str] = None,
**kwargs: Any
) -> Any:
"""poll_operation_status.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param session_id:
:type session_id: str
:param action_type:
:type action_type: str or ~flow.models.SetupFlowSessionAction
:param location:
:type location: str
:param operation_id:
:type operation_id: str
:param api_version: Api Version. The default value is "1.0.0".
:type api_version: str
:param type:
:type type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: any, or the result of cls(response)
:rtype: any
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[Any]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_poll_operation_status_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
session_id=session_id,
action_type=action_type,
location=location,
operation_id=operation_id,
api_version=api_version,
type=type,
template_url=self.poll_operation_status.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('object', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
poll_operation_status.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/{sessionId}/{actionType}/locations/{location}/operations/{operationId}'} # type: ignore
@distributed_trace_async
async def get_standby_pools(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
**kwargs: Any
) -> List["_models.StandbyPoolProperties"]:
"""get_standby_pools.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of StandbyPoolProperties, or the result of cls(response)
:rtype: list[~flow.models.StandbyPoolProperties]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.StandbyPoolProperties"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_standby_pools_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_standby_pools.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[StandbyPoolProperties]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_standby_pools.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessions/standbypools'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_flow_sessions_admin_operations.py | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import Any, Callable, Dict, Generic, Optional, TypeVar
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._flow_sessions_admin_operations import build_create_flow_session_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class FlowSessionsAdminOperations:
"""FlowSessionsAdminOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace_async
async def create_flow_session(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
session_id: str,
waitfor_completion: Optional[bool] = False,
body: Optional["_models.CreateFlowSessionRequest"] = None,
**kwargs: Any
) -> str:
"""create_flow_session.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param session_id:
:type session_id: str
:param waitfor_completion:
:type waitfor_completion: bool
:param body:
:type body: ~flow.models.CreateFlowSessionRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowSessionRequest')
else:
_json = None
request = build_create_flow_session_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
session_id=session_id,
content_type=content_type,
json=_json,
waitfor_completion=waitfor_completion,
template_url=self.create_flow_session.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_flow_session.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowSessionsAdmin/{sessionId}'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_flows_provider_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import Any, Callable, Dict, Generic, Optional, TypeVar
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._flows_provider_operations import build_get_index_entity_by_id_request, build_get_updated_entity_ids_for_workspace_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class FlowsProviderOperations:
"""FlowsProviderOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace_async
async def get_index_entity_by_id(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
body: Optional["_models.UnversionedEntityRequestDto"] = None,
**kwargs: Any
) -> "_models.UnversionedEntityResponseDto":
"""get_index_entity_by_id.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param body:
:type body: ~flow.models.UnversionedEntityRequestDto
:keyword callable cls: A custom type or function that will be passed the direct response
:return: UnversionedEntityResponseDto, or the result of cls(response)
:rtype: ~flow.models.UnversionedEntityResponseDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.UnversionedEntityResponseDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'UnversionedEntityRequestDto')
else:
_json = None
request = build_get_index_entity_by_id_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
template_url=self.get_index_entity_by_id.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('UnversionedEntityResponseDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_index_entity_by_id.metadata = {'url': '/flow/v1.0/flows/getIndexEntities'} # type: ignore
@distributed_trace_async
async def get_updated_entity_ids_for_workspace(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
body: Optional["_models.UnversionedRebuildIndexDto"] = None,
**kwargs: Any
) -> "_models.UnversionedRebuildResponseDto":
"""get_updated_entity_ids_for_workspace.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param body:
:type body: ~flow.models.UnversionedRebuildIndexDto
:keyword callable cls: A custom type or function that will be passed the direct response
:return: UnversionedRebuildResponseDto, or the result of cls(response)
:rtype: ~flow.models.UnversionedRebuildResponseDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.UnversionedRebuildResponseDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'UnversionedRebuildIndexDto')
else:
_json = None
request = build_get_updated_entity_ids_for_workspace_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
content_type=content_type,
json=_json,
template_url=self.get_updated_entity_ids_for_workspace.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('UnversionedRebuildResponseDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_updated_entity_ids_for_workspace.metadata = {'url': '/flow/v1.0/flows/rebuildIndex'} # type: ignore
| 0 |
promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio | promptflow_repo/promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_flow_runs_admin_operations.py | # coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected])
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import datetime
import functools
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, Union
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._flow_runs_admin_operations import build_batch_update_service_logs_request, build_check_policy_validation_async_request, build_get_storage_info_request, build_log_flow_run_event_request, build_log_flow_run_event_v2_request, build_log_flow_run_terminated_event_request, build_log_result_for_bulk_run_request, build_send_policy_validation_async_request, build_submit_bulk_run_async_request, build_update_service_logs_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class FlowRunsAdminOperations:
"""FlowRunsAdminOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace_async
async def submit_bulk_run_async(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
bulk_run_id: str,
error_handling_mode: Optional[Union[str, "_models.ErrorHandlingMode"]] = None,
**kwargs: Any
) -> "_models.SubmitBulkRunResponse":
"""submit_bulk_run_async.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_run_id:
:type bulk_run_id: str
:param error_handling_mode:
:type error_handling_mode: str or ~flow.models.ErrorHandlingMode
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SubmitBulkRunResponse, or the result of cls(response)
:rtype: ~flow.models.SubmitBulkRunResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.SubmitBulkRunResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_submit_bulk_run_async_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_run_id=bulk_run_id,
error_handling_mode=error_handling_mode,
template_url=self.submit_bulk_run_async.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('SubmitBulkRunResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
submit_bulk_run_async.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/submit'} # type: ignore
@distributed_trace_async
async def send_policy_validation_async(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
bulk_run_id: str,
**kwargs: Any
) -> "_models.PolicyValidationResponse":
"""send_policy_validation_async.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_run_id:
:type bulk_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: PolicyValidationResponse, or the result of cls(response)
:rtype: ~flow.models.PolicyValidationResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.PolicyValidationResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_send_policy_validation_async_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_run_id=bulk_run_id,
template_url=self.send_policy_validation_async.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('PolicyValidationResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
send_policy_validation_async.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/policy'} # type: ignore
@distributed_trace_async
async def check_policy_validation_async(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
bulk_run_id: str,
**kwargs: Any
) -> "_models.PolicyValidationResponse":
"""check_policy_validation_async.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_run_id:
:type bulk_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: PolicyValidationResponse, or the result of cls(response)
:rtype: ~flow.models.PolicyValidationResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.PolicyValidationResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_policy_validation_async_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_run_id=bulk_run_id,
template_url=self.check_policy_validation_async.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('PolicyValidationResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_policy_validation_async.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/policy'} # type: ignore
@distributed_trace_async
async def log_result_for_bulk_run(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
bulk_run_id: str,
**kwargs: Any
) -> List["_models.KeyValuePairStringObject"]:
"""log_result_for_bulk_run.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_run_id:
:type bulk_run_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of KeyValuePairStringObject, or the result of cls(response)
:rtype: list[~flow.models.KeyValuePairStringObject]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.KeyValuePairStringObject"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_log_result_for_bulk_run_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_run_id=bulk_run_id,
template_url=self.log_result_for_bulk_run.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[KeyValuePairStringObject]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
log_result_for_bulk_run.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/LogResult'} # type: ignore
@distributed_trace_async
async def get_storage_info(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
**kwargs: Any
) -> "_models.StorageInfo":
"""get_storage_info.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: StorageInfo, or the result of cls(response)
:rtype: ~flow.models.StorageInfo
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.StorageInfo"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_storage_info_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_storage_info.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('StorageInfo', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_storage_info.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/storageInfo'} # type: ignore
@distributed_trace_async
async def log_flow_run_event(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
flow_run_id: str,
runtime_version: str,
**kwargs: Any
) -> str:
"""log_flow_run_event.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param runtime_version:
:type runtime_version: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_log_flow_run_event_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
runtime_version=runtime_version,
template_url=self.log_flow_run_event.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
log_flow_run_event.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/flowRuns/{flowRunId}/runtime/{runtimeVersion}/logEvent'} # type: ignore
@distributed_trace_async
async def log_flow_run_event_v2(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
flow_run_id: str,
runtime_version: Optional[str] = None,
**kwargs: Any
) -> str:
"""log_flow_run_event_v2.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param runtime_version:
:type runtime_version: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: str, or the result of cls(response)
:rtype: str
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[str]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_log_flow_run_event_v2_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
runtime_version=runtime_version,
template_url=self.log_flow_run_event_v2.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('str', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
log_flow_run_event_v2.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/flowRuns/{flowRunId}/logEvent'} # type: ignore
@distributed_trace_async
async def log_flow_run_terminated_event(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
flow_run_id: str,
last_checked_time: Optional[datetime.datetime] = None,
**kwargs: Any
) -> "_models.LogRunTerminatedEventDto":
"""log_flow_run_terminated_event.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param flow_run_id:
:type flow_run_id: str
:param last_checked_time:
:type last_checked_time: ~datetime.datetime
:keyword callable cls: A custom type or function that will be passed the direct response
:return: LogRunTerminatedEventDto, or the result of cls(response)
:rtype: ~flow.models.LogRunTerminatedEventDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.LogRunTerminatedEventDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_log_flow_run_terminated_event_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
flow_run_id=flow_run_id,
last_checked_time=last_checked_time,
template_url=self.log_flow_run_terminated_event.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('LogRunTerminatedEventDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
log_flow_run_terminated_event.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/flowRuns/{flowRunId}/logTerminatedEvent'} # type: ignore
@distributed_trace_async
async def update_service_logs(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
bulk_run_id: str,
body: Optional["_models.ServiceLogRequest"] = None,
**kwargs: Any
) -> "_models.Task":
"""update_service_logs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_run_id:
:type bulk_run_id: str
:param body:
:type body: ~flow.models.ServiceLogRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: Task, or the result of cls(response)
:rtype: ~flow.models.Task
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.Task"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'ServiceLogRequest')
else:
_json = None
request = build_update_service_logs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_run_id=bulk_run_id,
content_type=content_type,
json=_json,
template_url=self.update_service_logs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('Task', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_service_logs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/serviceLogs'} # type: ignore
@distributed_trace_async
async def batch_update_service_logs(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
flow_id: str,
bulk_run_id: str,
body: Optional[List["_models.ServiceLogRequest"]] = None,
**kwargs: Any
) -> "_models.Task":
"""batch_update_service_logs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param flow_id:
:type flow_id: str
:param bulk_run_id:
:type bulk_run_id: str
:param body:
:type body: list[~flow.models.ServiceLogRequest]
:keyword callable cls: A custom type or function that will be passed the direct response
:return: Task, or the result of cls(response)
:rtype: ~flow.models.Task
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.Task"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, '[ServiceLogRequest]')
else:
_json = None
request = build_batch_update_service_logs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
flow_id=flow_id,
bulk_run_id=bulk_run_id,
content_type=content_type,
json=_json,
template_url=self.batch_update_service_logs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('Task', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
batch_update_service_logs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRunsAdmin/{flowId}/bulkRuns/{bulkRunId}/serviceLogs/batch'} # type: ignore
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