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deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | custom_components/batch_pred_evaluator.py | """
This component evaluates the performance of a currently deployed model, and
the evaluation is based on the result of batch prediction on Vertex AI from the previous component.
At the end, this component will output true or false to indicate if retraining is needed.
Reference: https://bit.ly/vertex-batch
"""
from tfx.dsl.component.experimental.annotations import Parameter, OutputArtifact
from tfx.dsl.component.experimental.decorators import component
from tfx.types.experimental.simple_artifacts import Dataset
from absl import logging
import os
import json
@component
def PerformanceEvaluator(
gcs_destination: Parameter[str],
local_directory: Parameter[str],
threshold: Parameter[float],
trigger_pipeline: OutputArtifact[Dataset],
):
"""
gcs_destination: GCS location where the files containing
the result of batch prediction is
local_directory: Temporary directory to hold files copied
from the gcs_destination
threshold: threshold to decide if retraining is needed or not
it is based on the measured accuracy
trigger_pipeline: an output artifact which hold true or false
to indicate if retraining is needed or not
"""
full_gcs_results_dir = f"{gcs_destination}/{local_directory}"
# Create missing directories.
os.makedirs(local_directory, exist_ok=True)
# Get the Cloud Storage paths for each result.
os.system(f"gsutil -m cp -r {full_gcs_results_dir} {local_directory}")
# Get most recently modified directory.
latest_directory = max(
[os.path.join(local_directory, d) for d in os.listdir(local_directory)],
key=os.path.getmtime,
)
# Get downloaded results in directory.
results_files = []
for dirpath, subdirs, files in os.walk(latest_directory):
for file in files:
if file.startswith("prediction.results"):
results_files.append(os.path.join(dirpath, file))
# Consolidate all the results into a list.
results = []
for results_file in results_files:
# Download each result.
with open(results_file, "r") as file:
results.extend([json.loads(line) for line in file.readlines()])
# Calculate performance.
num_correct = 0
for result in results:
label = os.path.basename(result["instance"]).split("_")[0]
prediction = result["prediction"]["label"]
if label == prediction:
num_correct = num_correct + 1
accuracy = num_correct / len(results)
logging.info(f"Accuracy: {accuracy*100}%")
# Store the boolean result.
trigger_pipeline.set_string_custom_property("result", str(accuracy >= threshold))
|
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | custom_components/batch_prediction_vertex.py | """
This component launches a Batch Prediction job on Vertex AI.
Know more about Vertex AI Batch Predictions jobs, go here:
https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions.
"""
from google.cloud import storage
from tfx.dsl.component.experimental.annotations import Parameter, InputArtifact
from tfx.dsl.component.experimental.decorators import component
from tfx.types.standard_artifacts import String
import google.cloud.aiplatform as vertex_ai
from absl import logging
@component
def BatchPredictionGen(
gcs_source: InputArtifact[String],
project: Parameter[str],
location: Parameter[str],
model_resource_name: Parameter[str],
job_display_name: Parameter[str],
gcs_destination: Parameter[str],
instances_format: Parameter[str] = "file-list",
machine_type: Parameter[str] = "n1-standard-2",
accelerator_count: Parameter[int] = 0,
accelerator_type: Parameter[str] = None,
starting_replica_count: Parameter[int] = 1,
max_replica_count: Parameter[int] = 1,
):
"""
gcs_source: A location inside GCS to be used by the Batch Prediction job to get its inputs.
Rest of the parameters are explained here: https://git.io/JiUyU.
"""
storage_client = storage.Client()
# Read GCS Source (gcs_source contains the full path of GCS object).
# 1-1. get bucketname from gcs_source
gcs_source_uri = gcs_source.uri.split("//")[1:][0].split("/")
bucketname = gcs_source_uri[0]
bucket = storage_client.get_bucket(bucketname)
logging.info(f"bucketname: {bucketname}")
# 1-2. get object path without the bucket name.
objectpath = "/".join(gcs_source_uri[1:])
# 1-3. read the object to get value set by OutputArtifact from FileListGen.
blob = bucket.blob(objectpath)
logging.info(f"objectpath: {objectpath}")
gcs_source = f"gs://{blob.download_as_text()}"
# Get Model.
vertex_ai.init(project=project, location=location)
model = vertex_ai.Model.list(
filter=f"display_name={model_resource_name}", order_by="update_time"
)[-1]
# Launch a Batch Prediction job.
logging.info("Starting batch prediction job.")
logging.info(f"GCS path where file list is: {gcs_source}")
batch_prediction_job = model.batch_predict(
job_display_name=job_display_name,
instances_format=instances_format,
gcs_source=gcs_source,
gcs_destination_prefix=gcs_destination,
machine_type=machine_type,
accelerator_count=accelerator_count,
accelerator_type=accelerator_type,
starting_replica_count=starting_replica_count,
max_replica_count=max_replica_count,
sync=True,
)
logging.info(batch_prediction_job.display_name)
logging.info(batch_prediction_job.resource_name)
logging.info(batch_prediction_job.state)
|
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | custom_components/file_list_gen.py | """
Generate a txt file formatted required by Vertex AI's Batch Prediction
There are few options, and this component generate "file list" formatted txt.
(https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions)
"""
import tensorflow as tf
from absl import logging
from tfx.dsl.component.experimental.decorators import component
from tfx.dsl.component.experimental.annotations import Parameter, OutputArtifact
from tfx.types.standard_artifacts import String
@component
def FileListGen(
outpath: OutputArtifact[String],
gcs_src_bucket: Parameter[str],
gcs_src_prefix: Parameter[str] = "",
output_filename: Parameter[str] = "test-images.txt",
):
"""
: param outpath: OutputArtifact to hold where output_filename will be located
This will be used in the downstream component, BatchPredictionGen
: param gcs_src_bucket: GCS bucket name where the list of raw data is
: param gcs_src_prefix: prefix to be added to gcs_src_bucket
: param output_filename: output filename whose content is a list of file paths of raw data
"""
logging.info("FileListGen started")
# 1. get the list of data
gcs_src_prefix = (
f"{gcs_src_prefix}/" if len(gcs_src_prefix) != 0 else gcs_src_prefix
)
img_paths = tf.io.gfile.glob(f"gs://{gcs_src_bucket}/{gcs_src_prefix}*.jpg")
logging.info("Successfully retrieve the file(jpg) list from GCS path")
# 2. write the list of data in the expected format in Vertex AI Batch Prediction to a local file
with open(output_filename, "w", encoding="utf-8") as f:
f.writelines("%s\n" % img_path for img_path in img_paths)
logging.info(
f"Successfully created the file list file({output_filename}) in local storage"
)
# 3. upload the local file to GCS location
gcs_dst = f"{gcs_src_bucket}/{gcs_src_prefix}{output_filename}"
tf.io.gfile.copy(output_filename, f"gs://{gcs_dst}", overwrite=True)
logging.info(f"Successfully uploaded the file list ({gcs_dst})")
# 4. store the GCS location where the local file is
outpath.value = gcs_dst
|
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | custom_components/span_preparator.py | """
This component is responsible for separating provided samples into training and
validation splits. It then converts them to TFRecords and stores those inside
a GCS location. Finally, it returns the latest span id calculated from the current
samples in `gcs_source_bucket`.
"""
from tfx.dsl.component.experimental.decorators import component
from tfx.dsl.component.experimental.annotations import Parameter
from tfx.dsl.component.experimental.annotations import OutputArtifact, InputArtifact
from tfx.types.experimental.simple_artifacts import Dataset
from absl import logging
from datetime import datetime
import tensorflow as tf
import random
import os
# Label-mapping.
LABEL_DICT = {
"airplane": 0,
"automobile": 1,
"bird": 2,
"cat": 3,
"deer": 4,
"dog": 5,
"frog": 6,
"horse": 7,
"ship": 8,
"truck": 9,
}
# Images are byte-strings.
def _bytestring_feature(list_of_bytestrings):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=list_of_bytestrings))
# Classes would be integers.
def _int_feature(list_of_ints):
return tf.train.Feature(int64_list=tf.train.Int64List(value=list_of_ints))
# Function that prepares a record for the tfrecord file
# a record contains the image and its label.
def to_tfrecord(img_bytes, label):
feature = {
"image": _bytestring_feature([img_bytes]),
"label": _int_feature([label]),
}
return tf.train.Example(features=tf.train.Features(feature=feature))
def write_tfrecords(filepaths, dest_gcs, tfrecord_filename, new_span, is_train):
# For this project, we are serializing the images in one TFRecord only.
# For more realistic purposes, this should be sharded.
folder = "train" if is_train else "test"
with tf.io.TFRecordWriter(tfrecord_filename) as writer:
for path in filepaths:
image_string = tf.io.read_file(path).numpy()
class_name = path.split("/")[-1].split("_")[0]
label = LABEL_DICT[class_name]
example = to_tfrecord(image_string, label)
writer.write(example.SerializeToString())
# Copy over the zipped TFRecord file to the GCS Bucket and
# remove the temporary files.
logging.info(f"gsutil cp {tfrecord_filename} {dest_gcs}/span-{new_span}/{folder}/")
os.system(f"gsutil cp {tfrecord_filename} {dest_gcs}/span-{new_span}/{folder}/")
os.remove(tfrecord_filename)
@component
def SpanPreparator(
is_retrain: InputArtifact[Dataset],
gcs_source_bucket: Parameter[str],
gcs_destination_bucket: Parameter[str],
latest_span_id: OutputArtifact[Dataset],
gcs_source_prefix: Parameter[str] = "",
):
"""
:param is_retrain: Boolean to indicate if we are retraining.
:param gcs_source_bucket: GCS location where the entry samples are residing.
:param gcs_destination_bucket: GCS location where the converted TFRecords will be serialized.
:param latest_span_id: Data span.
:param gcs_source_prefix: Location prefix.
"""
if is_retrain.get_string_custom_property("result") == "False":
# Get the latest span and determine the new span.
last_span_str = tf.io.gfile.glob(f"{gcs_destination_bucket}/span-*")[-1]
last_span = int(last_span_str.split("-")[-1])
new_span = last_span + 1
timestamp = datetime.utcnow().strftime("%y%m%d-%H%M%S")
# Get images from the provided GCS source.
image_paths = tf.io.gfile.glob(f"gs://{gcs_source_bucket}/*.jpg")
logging.info(image_paths)
random.shuffle(image_paths)
# Create train and validation splits.
val_split = 0.2
split_index = int(len(image_paths) * (1 - val_split))
training_paths = image_paths[:split_index]
validation_paths = image_paths[split_index:]
# Write as TFRecords.
write_tfrecords(
training_paths,
gcs_destination_bucket,
tfrecord_filename=f"new_training_data_{timestamp}.tfrecord",
new_span=new_span,
is_train=True,
)
write_tfrecords(
validation_paths,
gcs_destination_bucket,
tfrecord_filename=f"new_validation_data_{timestamp}.tfrecord",
new_span=new_span,
is_train=False,
)
logging.info("Removing images from batch prediction bucket.")
os.system(
f"gsutil mv gs://{gcs_source_bucket}/{gcs_source_prefix} gs://{gcs_source_bucket}/{gcs_source_prefix}_old"
)
latest_span_id.set_string_custom_property("latest_span", str(new_span))
|
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | custom_components/training_pipeline_trigger.py | """
Component responsible for triggering a training job given a pipeline specification.
"""
import json
from google.cloud import storage
from kfp.v2.google.client import AIPlatformClient
from tfx.dsl.component.experimental.annotations import Parameter, InputArtifact
from tfx.dsl.component.experimental.decorators import component
from tfx.types.experimental.simple_artifacts import Dataset
from absl import logging
@component
def PipelineTrigger(
is_retrain: InputArtifact[Dataset],
latest_span_id: InputArtifact[Dataset],
pipeline_spec_path: Parameter[str],
project_id: Parameter[str],
region: Parameter[str],
):
"""
:param is_retrain: Boolean to indicate if we are retraining.
:param latest_span_id: Latest span id to craft training data for the model.
:param pipeline_spec_path: Training pipeline specification path.
:param project_id: GCP project id.
:param region: GCP region.
"""
if is_retrain.get_string_custom_property("result") == "False":
# Check if the pipeline spec exists.
storage_client = storage.Client()
path_parts = pipeline_spec_path.replace("gs://", "").split("/")
bucket_name = path_parts[0]
blob_name = "/".join(path_parts[1:])
bucket = storage_client.bucket(bucket_name)
blob = storage.Blob(bucket=bucket, name=blob_name)
if not blob.exists(storage_client):
raise ValueError(f"{pipeline_spec_path} does not exist.")
# Initialize Vertex AI API client and submit for pipeline execution.
api_client = AIPlatformClient(project_id=project_id, region=region)
# Fetch the latest span.
latest_span = latest_span_id.get_string_custom_property("latest_span")
# Create a training job from pipeline spec.
response = api_client.create_run_from_job_spec(
pipeline_spec_path,
enable_caching=False,
parameter_values={
"input-config": json.dumps(
{
"splits": [
{
"name": "train",
"pattern": f"span-[{int(latest_span)-1}{latest_span}]/train/*.tfrecord",
},
{
"name": "val",
"pattern": f"span-[{int(latest_span)-1}{latest_span}]/test/*.tfrecord",
},
]
}
),
"output-config": json.dumps({}),
},
)
logging.info(response)
|
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | notebooks/01_Dataset_Prep.ipynb | from google.colab import auth
auth.authenticate_user()TARGET_ROOT_DIR = "cifar10"
TARGET_TRAIN_DIR = TARGET_ROOT_DIR + "/span-1/train"
TARGET_TEST_DIR = TARGET_ROOT_DIR + "/span-1/test"
!mkdir -p {TARGET_TRAIN_DIR}
!mkdir -p {TARGET_TEST_DIR}import tensorflow_datasets as tfds
# Generate TFRecords with TFDS
builder = tfds.builder("cifar10")
builder.download_and_prepare()#@title GCS
#@markdown You should change these values as per your preferences. The copy operation can take ~5 minutes.
BUCKET_PATH = "gs://cifar10-csp-public2" #@param {type:"string"}
REGION = "us-central1" #@param {type:"string"}
!gsutil mb -l {REGION} {BUCKET_PATH}
!gsutil -m cp -r {TARGET_ROOT_DIR}/* {BUCKET_PATH}from tfx import v1 as tfx
from tfx.components.example_gen import utilsfrom tfx.proto import example_gen_pb2
_DATA_PATH = "gs://cifar10-csp-public"
splits = [
example_gen_pb2.Input.Split(name="train", pattern="span-{SPAN}/train/*"),
example_gen_pb2.Input.Split(name="val", pattern="span-{SPAN}/test/*"),
]
_, span, version = utils.calculate_splits_fingerprint_span_and_version(
_DATA_PATH, splits
)span, version |
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | notebooks/02_TFX_Training_Pipeline.ipynb | from google.colab import auth
auth.authenticate_user()import tensorflow as tf
print("TensorFlow version: {}".format(tf.__version__))
from tfx import v1 as tfx
print("TFX version: {}".format(tfx.__version__))
import kfp
print("KFP version: {}".format(kfp.__version__))
from google.cloud import aiplatform as vertex_ai
import osGOOGLE_CLOUD_PROJECT = "gcp-ml-172005" # @param {type:"string"}
GOOGLE_CLOUD_REGION = "us-central1" # @param {type:"string"}
GCS_BUCKET_NAME = "cifar10-experimental-csp2" # @param {type:"string"}
DATA_ROOT = "gs://cifar10-csp-public2" # @param {type:"string"}
if not (GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_REGION and GCS_BUCKET_NAME):
from absl import logging
logging.error("Please set all required parameters.")PIPELINE_NAME = "continuous-adaptation-for-data-changes"
# Path to various pipeline artifact.
PIPELINE_ROOT = "gs://{}/pipeline_root/{}".format(GCS_BUCKET_NAME, PIPELINE_NAME)
# Paths for users' Python module.
MODULE_ROOT = "gs://{}/pipeline_module/{}".format(GCS_BUCKET_NAME, PIPELINE_NAME)
# This is the path where your model will be pushed for serving.
SERVING_MODEL_DIR = "gs://{}/serving_model/{}".format(GCS_BUCKET_NAME, PIPELINE_NAME)
print("PIPELINE_ROOT: {}".format(PIPELINE_ROOT))_trainer_module_file = 'trainer.py'%%writefile {_trainer_module_file}
from typing import List
from absl import logging
from tensorflow import keras
from tfx import v1 as tfx
import tensorflow as tf
_IMAGE_FEATURES = {
"image": tf.io.FixedLenFeature([], tf.string),
"label": tf.io.FixedLenFeature([], tf.int64),
}
_CONCRETE_INPUT = "numpy_inputs"
_TRAIN_BATCH_SIZE = 64
_EVAL_BATCH_SIZE = 64
_INPUT_SHAPE = (32, 32, 3)
_EPOCHS = 2
def _parse_fn(example):
example = tf.io.parse_single_example(example, _IMAGE_FEATURES)
image = tf.image.decode_jpeg(example["image"], channels=3)
class_label = tf.cast(example["label"], tf.int32)
return image, class_label
def _input_fn(file_pattern: List[str], batch_size: int) -> tf.data.Dataset:
print(f"Reading data from: {file_pattern}")
tfrecord_filenames = tf.io.gfile.glob(file_pattern[0] + ".gz")
print(tfrecord_filenames)
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")
dataset = dataset.map(_parse_fn).batch(batch_size)
return dataset.repeat()
def _make_keras_model() -> tf.keras.Model:
"""Creates a ResNet50-based model for classifying flowers data.
Returns:
A Keras Model.
"""
inputs = keras.Input(shape=_INPUT_SHAPE)
base_model = keras.applications.ResNet50(
include_top=False, input_shape=_INPUT_SHAPE, pooling="avg"
)
base_model.trainable = False
x = tf.keras.applications.resnet.preprocess_input(inputs)
x = base_model(
x, training=False
) # Ensures BatchNorm runs in inference model in this model
outputs = keras.layers.Dense(10, activation="softmax")(x)
model = keras.Model(inputs, outputs)
model.compile(
optimizer=keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
model.summary(print_fn=logging.info)
return model
def _preprocess(bytes_input):
decoded = tf.io.decode_jpeg(bytes_input, channels=3)
resized = tf.image.resize(decoded, size=(32, 32))
return resized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def preprocess_fn(bytes_inputs):
decoded_images = tf.map_fn(
_preprocess, bytes_inputs, dtype=tf.float32, back_prop=False
)
return {_CONCRETE_INPUT: decoded_images}
def _model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
[tf.TensorSpec(shape=[None, 32, 32, 3], dtype=tf.float32, name=_CONCRETE_INPUT)]
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(bytes_inputs):
# This function comes from the Computer Vision book from O'Reilly.
labels = tf.constant(
[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck",
],
dtype=tf.string,
)
images = preprocess_fn(bytes_inputs)
probs = m_call(**images)
indices = tf.argmax(probs, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
def run_fn(fn_args: tfx.components.FnArgs):
print(fn_args)
train_dataset = _input_fn(fn_args.train_files, batch_size=_TRAIN_BATCH_SIZE)
eval_dataset = _input_fn(fn_args.eval_files, batch_size=_EVAL_BATCH_SIZE)
model = _make_keras_model()
model.fit(
train_dataset,
steps_per_epoch=fn_args.train_steps,
validation_data=eval_dataset,
validation_steps=fn_args.eval_steps,
epochs=_EPOCHS,
)
_, acc = model.evaluate(eval_dataset, steps=fn_args.eval_steps)
logging.info(f"Validation accuracy: {round(acc * 100, 2)}%")
# The result of the training should be saved in `fn_args.serving_model_dir`
# directory.
tf.saved_model.save(
model,
fn_args.serving_model_dir,
signatures={"serving_default": _model_exporter(model)},
)os.path.join(MODULE_ROOT, _trainer_module_file)_vertex_uploader_module_file = "vertex_uploader.py"
_vertex_deployer_module_file = "vertex_deployer.py"%%writefile {_vertex_uploader_module_file}
import os
import tensorflow as tf
from tfx.dsl.component.experimental.decorators import component
from tfx.dsl.component.experimental.annotations import Parameter
from tfx.types.standard_artifacts import String
from google.cloud import aiplatform as vertex_ai
from tfx import v1 as tfx
from absl import logging
@component
def VertexUploader(
project: Parameter[str],
region: Parameter[str],
model_display_name: Parameter[str],
pushed_model_location: Parameter[str],
serving_image_uri: Parameter[str],
uploaded_model: tfx.dsl.components.OutputArtifact[String],
):
vertex_ai.init(project=project, location=region)
pushed_model_dir = os.path.join(
pushed_model_location, tf.io.gfile.listdir(pushed_model_location)[-1]
)
logging.info(f"Model registry location: {pushed_model_dir}")
vertex_model = vertex_ai.Model.upload(
display_name=model_display_name,
artifact_uri=pushed_model_dir,
serving_container_image_uri=serving_image_uri,
parameters_schema_uri=None,
instance_schema_uri=None,
explanation_metadata=None,
explanation_parameters=None,
)
uploaded_model.set_string_custom_property(
"model_resource_name", str(vertex_model.resource_name)
)
logging.info(f"Model resource: {str(vertex_model.resource_name)}")%%writefile {_vertex_deployer_module_file}
from tfx.dsl.component.experimental.decorators import component
from tfx.dsl.component.experimental.annotations import Parameter
from tfx.types.standard_artifacts import String
from google.cloud import aiplatform as vertex_ai
from tfx import v1 as tfx
from absl import logging
@component
def VertexDeployer(
project: Parameter[str],
region: Parameter[str],
model_display_name: Parameter[str],
deployed_model_display_name: Parameter[str],
):
logging.info(f"Endpoint display: {deployed_model_display_name}")
vertex_ai.init(project=project, location=region)
endpoints = vertex_ai.Endpoint.list(
filter=f"display_name={deployed_model_display_name}", order_by="update_time"
)
if len(endpoints) > 0:
logging.info(f"Endpoint {deployed_model_display_name} already exists.")
endpoint = endpoints[-1]
else:
endpoint = vertex_ai.Endpoint.create(deployed_model_display_name)
model = vertex_ai.Model.list(
filter=f"display_name={model_display_name}", order_by="update_time"
)[-1]
endpoint = vertex_ai.Endpoint.list(
filter=f"display_name={deployed_model_display_name}", order_by="update_time"
)[-1]
deployed_model = endpoint.deploy(
model=model,
# Syntax from here: https://git.io/JBQDP
traffic_split={"0": 100},
machine_type="n1-standard-4",
min_replica_count=1,
max_replica_count=1,
)
logging.info(f"Model deployed to: {deployed_model}")DATASET_DISPLAY_NAME = "cifar10"
VERSION = "tfx-1-2-0"
TFX_IMAGE_URI = f"gcr.io/{GOOGLE_CLOUD_PROJECT}/{DATASET_DISPLAY_NAME}:{VERSION}"
print(f"URI of the custom image: {TFX_IMAGE_URI}")%%writefile Dockerfile
FROM gcr.io/tfx-oss-public/tfx:1.2.0
RUN mkdir -p custom_components
COPY custom_components/* ./custom_components/
RUN pip install --upgrade google-cloud-aiplatform# Specify training worker configurations. To minimize costs we can even specify two
# different configurations: a beefier machine for the Endpoint model and slightly less
# powerful machine for the mobile model.
TRAINING_JOB_SPEC = {
"project": GOOGLE_CLOUD_PROJECT,
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": "gcr.io/tfx-oss-public/tfx:{}".format(tfx.__version__),
},
}
],
}SERVING_JOB_SPEC = {
"endpoint_name": PIPELINE_NAME.replace("-", "_"), # '-' is not allowed.
"project_id": GOOGLE_CLOUD_PROJECT,
"min_replica_count": 1,
"max_replica_count": 1,
"machine_type": "n1-standard-2",
}from datetime import datetime
TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S")import tfxfrom tfx.orchestration import data_types
from tfx import v1 as tfx
from tfx.proto import example_gen_pb2, range_config_pb2
from tfx.components.example_gen import utils
from custom_components.vertex_uploader import VertexUploader
from custom_components.vertex_deployer import VertexDeployer
def _create_pipeline(
input_config: data_types.RuntimeParameter,
output_config: data_types.RuntimeParameter,
pipeline_name: str,
pipeline_root: str,
data_root: str,
serving_model_dir: str,
trainer_module: str,
project_id: str,
region: str,
) -> tfx.dsl.Pipeline:
"""Creates a three component flowers pipeline with TFX."""
example_gen = tfx.components.ImportExampleGen(
input_base=data_root, input_config=input_config, output_config=output_config
)
# Trainer
trainer = tfx.extensions.google_cloud_ai_platform.Trainer(
module_file=trainer_module,
examples=example_gen.outputs["examples"],
train_args=tfx.proto.TrainArgs(splits=["train"], num_steps=50000 // 64),
eval_args=tfx.proto.EvalArgs(splits=["val"], num_steps=10000 // 64),
custom_config={
tfx.extensions.google_cloud_ai_platform.ENABLE_VERTEX_KEY: True,
tfx.extensions.google_cloud_ai_platform.VERTEX_REGION_KEY: region,
tfx.extensions.google_cloud_ai_platform.TRAINING_ARGS_KEY: TRAINING_JOB_SPEC,
"use_gpu": True,
},
).with_id("trainer")
# Pushes the model to a filesystem destination.
pushed_model_location = os.path.join(serving_model_dir, "resnet50")
resnet_pusher = tfx.components.Pusher(
model=trainer.outputs["model"],
push_destination=tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=pushed_model_location
)
),
).with_id("resnet_pusher")
# Vertex AI upload.
model_display_name = "resnet_cifar_latest"
uploader = VertexUploader(
project=project_id,
region=region,
model_display_name=model_display_name,
pushed_model_location=pushed_model_location,
serving_image_uri="us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-5:latest",
).with_id("vertex_uploader")
uploader.add_upstream_node(resnet_pusher)
# Create an endpoint.
deployer = VertexDeployer(
project=project_id,
region=region,
model_display_name=model_display_name,
deployed_model_display_name=model_display_name + "_" + TIMESTAMP,
).with_id("vertex_deployer")
deployer.add_upstream_node(uploader)
components = [
example_gen,
trainer,
resnet_pusher,
uploader,
deployer,
]
return tfx.dsl.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
)import os
PIPELINE_DEFINITION_FILE = PIPELINE_NAME + "_pipeline.json"
# Important: We need to pass the custom Docker image URI to the
# `KubeflowV2DagRunnerConfig` to take effect.
runner = tfx.orchestration.experimental.KubeflowV2DagRunner(
config=tfx.orchestration.experimental.KubeflowV2DagRunnerConfig(
default_image=TFX_IMAGE_URI
),
output_filename=PIPELINE_DEFINITION_FILE,
)
_ = runner.run(
_create_pipeline(
input_config=tfx.dsl.experimental.RuntimeParameter(
name="input-config",
default='{"input_config": {"splits": [{"name":"train", "pattern":"span-1/train/tfrecord"}, {"name":"val", "pattern":"span-1/test/tfrecord"}]}}',
ptype=str,
),
output_config=tfx.dsl.experimental.RuntimeParameter(
name="output-config", default="{}", ptype=str,
),
pipeline_name=PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_root=DATA_ROOT,
serving_model_dir=SERVING_MODEL_DIR,
trainer_module=os.path.join(MODULE_ROOT, _trainer_module_file),
project_id=GOOGLE_CLOUD_PROJECT,
region=GOOGLE_CLOUD_REGION,
)
)from kfp.v2.google import client
pipelines_client = client.AIPlatformClient(
project_id=GOOGLE_CLOUD_PROJECT, region=GOOGLE_CLOUD_REGION,
)import json
from tfx.orchestration import data_types
_ = pipelines_client.create_run_from_job_spec(
PIPELINE_DEFINITION_FILE,
enable_caching=False,
parameter_values={
"input-config": json.dumps(
{
"splits": [
{"name": "train", "pattern": "span-[12]/train/*.tfrecord"},
{"name": "val", "pattern": "span-[12]/test/*.tfrecord"},
]
}
),
"output-config": json.dumps({}),
},
) |
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | notebooks/03_Batch_Prediction_Pipeline.ipynb | from google.colab import auth
auth.authenticate_user()# @title
from fastdot.core import *
tfx_components = [
"FileListGen",
"BatchPredictionGen",
"PerformanceEvaluator",
"SpanPreparator",
"PipelineTrigger",
]
block = "TFX Component Workflow"
g = graph_items(seq_cluster(tfx_components, block))
g_file_list_gen_module_file = "file_list_gen.py"%%writefile {_file_list_gen_module_file}
import tfx
from tfx.dsl.component.experimental.decorators import component
from tfx.dsl.component.experimental.annotations import Parameter
from tfx.dsl.component.experimental.annotations import OutputArtifact
from tfx.types.standard_artifacts import String
from google.cloud import storage
from absl import logging
@component
def FileListGen(
outpath: OutputArtifact[String],
project: Parameter[str],
gcs_source_bucket: Parameter[str],
gcs_source_prefix: Parameter[str] = "",
output_filename: Parameter[str] = "test-images.txt",
):
logging.info("FileListGen started")
client = storage.Client(project=project)
bucket = client.get_bucket(gcs_source_bucket)
blobs = bucket.list_blobs(prefix=gcs_source_prefix)
logging.info("Successfully retrieve the file(jpg) list from GCS path")
f = open(output_filename, "w")
for blob in blobs:
if blob.name.split(".")[-1] == "jpg":
prefix = ""
if gcs_source_prefix != "":
prefix = f"/{gcs_source_prefix}"
line = f"gs://{gcs_source_bucket}{prefix}/{blob.name}\n"
f.write(line)
f.close()
logging.info(
f"Successfully created the file list file({output_filename}) in local storage"
)
prefix = ""
if gcs_source_prefix != "":
prefix = f"{gcs_source_prefix}/"
blob = bucket.blob(f"{prefix}{output_filename}")
blob.upload_from_filename(output_filename)
logging.info(f"Successfully uploaded the file list ({prefix}{output_filename})")
outpath.value = gcs_source_bucket + "/" + prefix + output_filename_batch_pred_module_file = 'batch_prediction_vertex.py'%%writefile {_batch_pred_module_file}
from google.cloud import storage
from tfx.dsl.component.experimental.annotations import Parameter, InputArtifact
from tfx.dsl.component.experimental.decorators import component
from tfx.types import artifact_utils
from tfx.types.standard_artifacts import String
import google.cloud.aiplatform as vertex_ai
from typing import Union, Sequence
from absl import logging
@component
def BatchPredictionGen(
gcs_source: InputArtifact[String],
project: Parameter[str],
location: Parameter[str],
model_resource_name: Parameter[str],
job_display_name: Parameter[str],
gcs_destination: Parameter[str],
instances_format: Parameter[str] = "file-list",
machine_type: Parameter[str] = "n1-standard-2",
accelerator_count: Parameter[int] = 0,
accelerator_type: Parameter[str] = None,
starting_replica_count: Parameter[int] = 1,
max_replica_count: Parameter[int] = 1,
):
storage_client = storage.Client()
# Read GCS Source (gcs_source contains the full path of GCS object)
# 1-1. get bucketname from gcs_source
gcs_source_uri = gcs_source.uri.split("//")[1:][0].split("/")
bucketname = gcs_source_uri[0]
bucket = storage_client.get_bucket(bucketname)
logging.info(f"bucketname: {bucketname}")
# 1-2. get object path without the bucketname
objectpath = "/".join(gcs_source_uri[1:])
# 1-3. read the object to get value set by OutputArtifact from FileListGen
blob = bucket.blob(objectpath)
logging.info(f"objectpath: {objectpath}")
gcs_source = f"gs://{blob.download_as_text()}"
# Get Model
vertex_ai.init(project=project, location=location)
model = vertex_ai.Model.list(
filter=f"display_name={model_resource_name}", order_by="update_time"
)[-1]
# Batch Predictions
logging.info("Starting batch prediction job.")
logging.info(f"GCS path where file list is: {gcs_source}")
batch_prediction_job = model.batch_predict(
job_display_name=job_display_name,
instances_format=instances_format,
gcs_source=gcs_source,
gcs_destination_prefix=gcs_destination,
machine_type=machine_type,
accelerator_count=accelerator_count,
accelerator_type=accelerator_type,
starting_replica_count=starting_replica_count,
max_replica_count=max_replica_count,
sync=True,
)
logging.info(batch_prediction_job.display_name)
logging.info(batch_prediction_job.resource_name)
logging.info(batch_prediction_job.state)_evaluator_module_file = 'batch_pred_evaluator.py'%%writefile {_evaluator_module_file}
# Reference: https://bit.ly/vertex-batch
from tfx.dsl.component.experimental.annotations import Parameter
from tfx.dsl.component.experimental.annotations import OutputArtifact
from tfx.dsl.component.experimental.decorators import component
from tfx.types.experimental.simple_artifacts import Dataset
from absl import logging
import os
import json
@component
def PerformanceEvaluator(
gcs_destination: Parameter[str],
local_directory: Parameter[str],
threshold: Parameter[float],
trigger_pipeline: OutputArtifact[Dataset],
):
full_gcs_results_dir = f"{gcs_destination}/{local_directory}"
# Create missing directories.
os.makedirs(local_directory, exist_ok=True)
# Get the Cloud Storage paths for each result.
os.system(f"gsutil -m cp -r {full_gcs_results_dir} {local_directory}")
# Get most recently modified directory.
latest_directory = max(
[os.path.join(local_directory, d) for d in os.listdir(local_directory)],
key=os.path.getmtime,
)
# Get downloaded results in directory.
results_files = []
for dirpath, subdirs, files in os.walk(latest_directory):
for file in files:
if file.startswith("prediction.results"):
results_files.append(os.path.join(dirpath, file))
# Consolidate all the results into a list.
results = []
for results_file in results_files:
# Download each result.
with open(results_file, "r") as file:
results.extend([json.loads(line) for line in file.readlines()])
# Calculate performance.
num_correct = 0
for result in results:
label = os.path.basename(result["instance"]).split("_")[0]
prediction = result["prediction"]["label"]
if label == prediction:
num_correct = num_correct + 1
accuracy = num_correct / len(results)
logging.info(f"Accuracy: {accuracy*100}%")
trigger_pipeline.set_string_custom_property("result", str(accuracy >= threshold))_span_preparator_module_file = 'span_preparator.py'%%writefile {_span_preparator_module_file}
import tfx
from tfx.dsl.component.experimental.decorators import component
from tfx.dsl.component.experimental.annotations import Parameter
from tfx.dsl.component.experimental.annotations import OutputArtifact, InputArtifact
from tfx.types.experimental.simple_artifacts import Dataset
from google.cloud import storage
from absl import logging
from datetime import datetime
import tensorflow as tf
import random
import gzip
import os
# Label-mapping.
LABEL_DICT = {
"airplane": 0,
"automobile": 1,
"bird": 2,
"cat": 3,
"deer": 4,
"dog": 5,
"frog": 6,
"horse": 7,
"ship": 8,
"truck": 9,
}
# Images are byte-strings.
def _bytestring_feature(list_of_bytestrings):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=list_of_bytestrings))
# Classes would be integers.
def _int_feature(list_of_ints):
return tf.train.Feature(int64_list=tf.train.Int64List(value=list_of_ints))
# Function that prepares a record for the tfrecord file
# a record contains the image and its label.
def to_tfrecord(img_bytes, label):
feature = {
"image": _bytestring_feature([img_bytes]),
"label": _int_feature([label]),
}
return tf.train.Example(features=tf.train.Features(feature=feature))
def write_tfrecords(filepaths, dest_gcs, tfrecord_filename, new_span, is_train):
# For this project, we are serializing the images in one TFRecord only.
# For more realistic purposes, this should be sharded.
folder = "train" if is_train else "test"
with tf.io.TFRecordWriter(tfrecord_filename) as writer:
for path in filepaths:
image_string = tf.io.read_file(path).numpy()
class_name = path.split("/")[-1].split("_")[0]
label = LABEL_DICT[class_name]
example = to_tfrecord(image_string, label)
writer.write(example.SerializeToString())
# Copy over the zipped TFRecord file to the GCS Bucket and
# remove the temporary files.
logging.info(f"gsutil cp {tfrecord_filename} {dest_gcs}/span-{new_span}/{folder}/")
os.system(f"gsutil cp {tfrecord_filename} {dest_gcs}/span-{new_span}/{folder}/")
os.remove(tfrecord_filename)
@component
def SpanPreparator(
is_retrain: InputArtifact[Dataset],
gcs_source_bucket: Parameter[str],
gcs_destination_bucket: Parameter[str],
latest_span_id: OutputArtifact[Dataset],
gcs_source_prefix: Parameter[str] = "",
):
if is_retrain.get_string_custom_property("result") == "False":
last_span_str = tf.io.gfile.glob(f"{gcs_destination_bucket}/span-*")[-1]
last_span = int(last_span_str.split("-")[-1])
new_span = last_span + 1
timestamp = datetime.utcnow().strftime("%y%m%d-%H%M%S")
image_paths = tf.io.gfile.glob(f"gs://{gcs_source_bucket}/*.jpg")
logging.info(image_paths)
random.shuffle(image_paths)
val_split = 0.2
split_index = int(len(image_paths) * (1 - val_split))
training_paths = image_paths[:split_index]
validation_paths = image_paths[split_index:]
write_tfrecords(
training_paths,
gcs_destination_bucket,
tfrecord_filename=f"new_training_data_{timestamp}.tfrecord",
new_span=new_span,
is_train=True,
)
write_tfrecords(
validation_paths,
gcs_destination_bucket,
tfrecord_filename=f"new_validation_data_{timestamp}.tfrecord",
new_span=new_span,
is_train=False,
)
logging.info("Removing images from batch prediction bucket.")
os.system(
f"gsutil mv gs://{gcs_source_bucket}/{gcs_source_prefix} gs://{gcs_source_bucket}/{gcs_source_prefix}_old"
)
# os.system(f"gsutil rm -rf gs://{gcs_source_bucket}/*")
latest_span_id.set_string_custom_property("latest_span", str(new_span))_pipeline_trigger_module_file = 'training_pipeline_trigger.py'%%writefile {_pipeline_trigger_module_file}
import json
from google.cloud import storage
from kfp.v2.google.client import AIPlatformClient
from tfx.dsl.component.experimental.annotations import Parameter, InputArtifact
from tfx.dsl.component.experimental.decorators import component
from tfx.types.experimental.simple_artifacts import Dataset
from absl import logging
@component
def PipelineTrigger(
is_retrain: InputArtifact[Dataset],
latest_span_id: InputArtifact[Dataset],
pipeline_spec_path: Parameter[str],
project_id: Parameter[str],
region: Parameter[str],
):
if is_retrain.get_string_custom_property('result') == 'False':
# Check if the pipeline spec exists.
storage_client = storage.Client()
path_parts = pipeline_spec_path.replace("gs://", "").split("/")
bucket_name = path_parts[0]
blob_name = "/".join(path_parts[1:])
bucket = storage_client.bucket(bucket_name)
blob = storage.Blob(bucket=bucket, name=blob_name)
if not blob.exists(storage_client):
raise ValueError(f"{pipeline_spec_path} does not exist.")
# Initialize Vertex AI API client and submit for pipeline execution.
api_client = AIPlatformClient(project_id=project_id, region=region)
# Fetch the latest span.
latest_span = latest_span_id.get_string_custom_property('latest_span')
# Create a training job from pipeline spec.
response = api_client.create_run_from_job_spec(pipeline_spec_path,
enable_caching=False,
parameter_values={
'input-config': json.dumps({
'splits': [
{'name': 'train', 'pattern': f'span-[{int(latest_span)-1}{latest_span}]/train/*.tfrecord'},
{'name': 'val', 'pattern': f'span-[{int(latest_span)-1}{latest_span}]/test/*.tfrecord'}
]
}),
'output-config': json.dumps({})
})
logging.info(response)# This bucket will be responsible for storing the pipeline related artifacts.
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005" # @param {type:"string"}
GOOGLE_CLOUD_REGION = "us-central1"
GCS_BUCKET_NAME = "cifar10-experimental-csp2" # @param {type:"string"}
MODEL_RESOURCE_NAME = "resnet_cifar_latest" # @param {type: "string"}
TEST_FILENAME = "test-images.txt" # @param {type:"string"}
TEST_GCS_BUCKET = "batch-prediction-collection-3" # @param {type:"string"}
TEST_GCS_PREFIX = "" # @param {type: "string"}
TRAINING_PIPELINE_SPEC = "gs://cifar10-experimental-csp2/pipeline_root/continuous-adaptation-for-data-changes/continuous-adaptation-for-data-changes_pipeline.json" # @param {type: "string"}
TRAINING_DATA_PATH = "gs://cifar10-csp-public2" # @param {type: "string"}
if not (GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_REGION and GCS_BUCKET_NAME):
from absl import logging
logging.error("Please set all required parameters.")PIPELINE_NAME = 'continuous-adaptation-for-data-changes-batch'
# Path to various pipeline artifact.
PIPELINE_ROOT = 'gs://{}/pipeline_root/{}'.format(
GCS_BUCKET_NAME, PIPELINE_NAME)
print('PIPELINE_ROOT: {}'.format(PIPELINE_ROOT))DISPLAY_NAME = "batch-predictions-pipeline"
VERSION = "tfx-1-2-0-34"
TFX_IMAGE_URI = f"gcr.io/{GOOGLE_CLOUD_PROJECT}/{DISPLAY_NAME}:{VERSION}"
print(f"URI of the custom image: {TFX_IMAGE_URI}")%%writefile Dockerfile
FROM gcr.io/tfx-oss-public/tfx:1.2.0
RUN mkdir -p custom_components
COPY custom_components/* ./custom_components/
RUN pip install --upgrade google-cloud-aiplatform google-cloud-storage kfp==1.6.1from datetime import datetime
TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S")from tfx.orchestration import data_types
from tfx import v1 as tfx
from tfx.orchestration.pipeline import Pipeline
from custom_components.file_list_gen import FileListGen
from custom_components.batch_prediction_vertex import BatchPredictionGen
from custom_components.batch_pred_evaluator import PerformanceEvaluator
from custom_components.span_preparator import SpanPreparator
from custom_components.training_pipeline_trigger import PipelineTrigger
def _create_pipeline(
pipeline_name: str,
pipeline_root: str,
data_gcs_bucket: str,
data_gcs_prefix: data_types.RuntimeParameter,
batch_job_gcs: str,
job_display_name: str,
model_resource_name: str,
project_id: str,
region: str,
threshold: float,
data_gcs_destination: str,
training_pipeline_spec: str,
) -> Pipeline:
# Generate a file list for batch preditions.
# More details on the structure of this file here:
# https://bit.ly/3BzfHVu.
filelist_gen = FileListGen(
project=project_id,
gcs_source_bucket=data_gcs_bucket,
gcs_source_prefix=data_gcs_prefix,
).with_id("filelist_gen")
# Submit a batch prediction job.
batch_pred_component = BatchPredictionGen(
project=project_id,
location=region,
job_display_name=job_display_name,
model_resource_name=model_resource_name,
gcs_source=filelist_gen.outputs["outpath"],
gcs_destination=f"gs://{batch_job_gcs}/results/",
accelerator_count=0,
accelerator_type=None,
).with_id("bulk_inferer_vertex")
batch_pred_component.add_upstream_node(filelist_gen)
# Evaluate the performance of the predictions.
# In a real-world project, this evaluation takes place
# separately, typically with the help of domain experts.
final_gcs_destination = f"gs://{batch_job_gcs}/results/"
evaluator = PerformanceEvaluator(
gcs_destination=f'gs://{final_gcs_destination.split("/")[2]}',
local_directory=final_gcs_destination.split("/")[-2],
threshold=threshold,
).with_id("batch_prediction_evaluator")
evaluator.add_upstream_node(batch_pred_component)
span_preparator = SpanPreparator(
is_retrain=evaluator.outputs["trigger_pipeline"],
gcs_source_bucket=data_gcs_bucket,
gcs_source_prefix=data_gcs_prefix,
gcs_destination_bucket=data_gcs_destination,
).with_id("span_preparator")
span_preparator.add_upstream_node(evaluator)
trigger = PipelineTrigger(
is_retrain=evaluator.outputs["trigger_pipeline"],
latest_span_id=span_preparator.outputs["latest_span_id"],
pipeline_spec_path=training_pipeline_spec,
project_id=project_id,
region=region,
).with_id("training_pipeline_trigger")
trigger.add_upstream_node(span_preparator)
components = [
filelist_gen,
batch_pred_component,
evaluator,
span_preparator,
trigger,
]
return Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
)import os
import tfx
from tfx.orchestration import data_types
from tfx.orchestration.kubeflow.v2.kubeflow_v2_dag_runner import KubeflowV2DagRunner
from tfx.orchestration.kubeflow.v2.kubeflow_v2_dag_runner import (
KubeflowV2DagRunnerConfig,
)
PIPELINE_DEFINITION_FILE = PIPELINE_NAME + "_pipeline.json"
THRESHOLD = 0.9
# Important: We need to pass the custom Docker image URI to the
# `KubeflowV2DagRunnerConfig` to take effect.
runner = KubeflowV2DagRunner(
config=KubeflowV2DagRunnerConfig(default_image=TFX_IMAGE_URI),
output_filename=PIPELINE_DEFINITION_FILE,
)
_ = runner.run(
_create_pipeline(
pipeline_name=PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_gcs_bucket=TEST_GCS_BUCKET,
data_gcs_prefix=data_types.RuntimeParameter(
name="data_gcs_prefix", default="", ptype=str
),
batch_job_gcs=GCS_BUCKET_NAME,
job_display_name=f"{MODEL_RESOURCE_NAME}_{TIMESTAMP}",
project_id=GOOGLE_CLOUD_PROJECT,
region=GOOGLE_CLOUD_REGION,
model_resource_name=MODEL_RESOURCE_NAME,
threshold=THRESHOLD,
data_gcs_destination=TRAINING_DATA_PATH,
training_pipeline_spec=TRAINING_PIPELINE_SPEC,
)
)from kfp.v2.google import client
pipelines_client = client.AIPlatformClient(
project_id=GOOGLE_CLOUD_PROJECT, region=GOOGLE_CLOUD_REGION,
)
_ = pipelines_client.create_run_from_job_spec(
PIPELINE_DEFINITION_FILE,
enable_caching=False,
parameter_values={"data_gcs_prefix": "2021-10"},
) |
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | notebooks/04_Cloud_Scheduler_Trigger.ipynb | from google.colab import auth
auth.authenticate_user()GOOGLE_CLOUD_PROJECT = "gcp-ml-172005" # @param {type:"string"}
GOOGLE_CLOUD_REGION = "us-central1"
GCS_BUCKET_NAME = "cifar10-experimental-csp2" # @param {type:"string"}
PIPELINE_NAME = "continuous-adaptation-for-data-changes-batch" # @param {type:"string"}
PIPELINE_ROOT = "gs://{}/pipeline_root/{}".format(GCS_BUCKET_NAME, PIPELINE_NAME)
PIPELINE_LOCATION = f"{PIPELINE_ROOT}/{PIPELINE_NAME}_pipeline.json"
PUBSUB_TOPIC = f"trigger-{PIPELINE_NAME}"
SCHEDULER_JOB_NAME = f"scheduler-job-{PUBSUB_TOPIC}"
IMAGE_LOCATION_BUCKET = "batch-prediction-collection-3" # @param {type:"string"}IMAGE_LOCATION_BUCKET_cloud_function_dep = "cloud_function/requirements.txt"%%writefile {_cloud_function_dep}
kfp==1.6.2
google-cloud-aiplatform
google-cloud-storage_cloud_function_file = "cloud_function/main.py"%%writefile {_cloud_function_file}
import os
import re
import json
import logging
import base64
from datetime import datetime
from kfp.v2.google.client import AIPlatformClient
from google.cloud import storage
def get_number_of_images(storage_client, bucket, latest_directory):
blobs = storage_client.list_blobs(bucket, prefix=latest_directory)
count = 0
for blob in blobs:
if blob.name.split(".")[-1] == "jpg":
count = count + 1
return count
def is_there_enough_images(storage_client, bucket, latest_directory, threshold):
number_of_images = get_number_of_images(storage_client, bucket, latest_directory)
print(f"number of images = {number_of_images}")
return number_of_images >= threshold
def get_latest_directory(storage_client, bucket):
blobs = storage_client.list_blobs(bucket)
folders = list(
set(
[
os.path.dirname(blob.name)
for blob in blobs
if bool(
re.match(
"[1-9][0-9][0-9][0-9]-[0-1][0-9]", os.path.dirname(blob.name)
)
)
is True
]
)
)
folders.sort(key=lambda date: datetime.strptime(date, "%Y-%m"))
print(folders[0])
return folders[0]
def trigger_pipeline(event, context):
# Parse the environment variables.
project = os.getenv("PROJECT")
region = os.getenv("REGION")
gcs_pipeline_file_location = os.getenv("GCS_PIPELINE_FILE_LOCATION")
gcs_image_bucket = os.getenv("GCS_IMAGE_BUCKET")
print(project)
print(region)
print(gcs_pipeline_file_location)
print(gcs_image_bucket)
threshold = 100
# Check if the pipeline file exists in the provided GCS Bucket.
storage_client = storage.Client()
latest_directory = get_latest_directory(storage_client, gcs_image_bucket)
if is_there_enough_images(
storage_client, gcs_image_bucket, latest_directory, threshold
):
path_parts = gcs_pipeline_file_location.replace("gs://", "").split("/")
pipeline_bucket = path_parts[0]
pipeline_blob = "/".join(path_parts[1:])
pipeline_bucket = storage_client.bucket(pipeline_bucket)
blob = storage.Blob(bucket=pipeline_bucket, name=pipeline_blob)
if not blob.exists(storage_client):
raise ValueError(f"{gcs_pipeline_file_location} does not exist.")
# Initialize Vertex AI API client and submit for pipeline execution.
api_client = AIPlatformClient(project_id=project, region=region)
response = api_client.create_run_from_job_spec(
job_spec_path=gcs_pipeline_file_location,
parameter_values={"data_gcs_prefix": latest_directory},
enable_caching=True,
)
logging.info(response)ENV_VARS=f"""\
PROJECT={GOOGLE_CLOUD_PROJECT},\
REGION={GOOGLE_CLOUD_REGION},\
GCS_PIPELINE_FILE_LOCATION={PIPELINE_LOCATION},\
GCS_IMAGE_BUCKET={IMAGE_LOCATION_BUCKET}
"""
!echo {ENV_VARS}BUCKET = f'gs://{GCS_BUCKET_NAME}'
CLOUD_FUNCTION_NAME = f'trigger-{PIPELINE_NAME}-fn'
!gcloud functions deploy {CLOUD_FUNCTION_NAME} \
--region={GOOGLE_CLOUD_REGION} \
--trigger-topic={PUBSUB_TOPIC} \
--runtime=python37 \
--source=cloud_function\
--entry-point=trigger_pipeline\
--stage-bucket={BUCKET}\
--update-env-vars={ENV_VARS}import IPython
cloud_fn_url = f"https://console.cloud.google.com/functions/details/{GOOGLE_CLOUD_REGION}/{CLOUD_FUNCTION_NAME}"
html = (
f'See the Cloud Function details <a href="{cloud_fn_url}" target="_blank">here</a>.'
)
IPython.display.display(IPython.display.HTML(html)) |
deep-diver/Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes | notebooks/98_Batch_Prediction_Test.ipynb | from google.colab import auth
auth.authenticate_user()GOOGLE_CLOUD_PROJECT = "central-hangar-321813" # @param {type:"string"}
GOOGLE_CLOUD_REGION = "us-central1" # @param {type:"string"}
MODEL_NAME = "resnet_cifar_latest" # @param {type:"string"}
TEST_FILENAME = "test-images.txt" # @param {type:"string"}
TEST_GCS_BUCKET = "gs://batch-prediction-collection" # @param {type:"string"}
TEST_LOCAL_PATH = "Continuous-Adaptation-for-Machine-Learning-System-to-Data-Changes/notebooks/test-images" # @param {type:"string"}from os import listdir
test_files = listdir(TEST_LOCAL_PATH)
test_filesf = open(TEST_FILENAME, "w")
for filename in test_files:
f.write(f"{TEST_GCS_BUCKET}/{filename}\n")
f.close()import google.cloud.aiplatform as aiplatform
from typing import Union, Sequence
def create_batch_prediction_job_dedicated_resources_sample(
project: str,
location: str,
model_resource_name: str,
job_display_name: str,
gcs_source: Union[str, Sequence[str]],
gcs_destination: str,
instances_format: str = "file-list",
machine_type: str = "n1-standard-2",
accelerator_count: int = 1,
accelerator_type: str = "NVIDIA_TESLA_K80",
starting_replica_count: int = 1,
max_replica_count: int = 1,
sync: bool = True,
):
aiplatform.init(project=project, location=location)
my_model = aiplatform.Model(model_resource_name)
batch_prediction_job = my_model.batch_predict(
job_display_name=job_display_name,
instances_format=instances_format,
gcs_source=gcs_source,
gcs_destination_prefix=gcs_destination,
machine_type=machine_type,
accelerator_count=accelerator_count,
accelerator_type=accelerator_type,
starting_replica_count=starting_replica_count,
max_replica_count=max_replica_count,
sync=sync,
)
batch_prediction_job.wait()
print(batch_prediction_job.display_name)
print(batch_prediction_job.resource_name)
print(batch_prediction_job.state)
return batch_prediction_jobfrom datetime import datetime
TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S")create_batch_prediction_job_dedicated_resources_sample(
project=GOOGLE_CLOUD_PROJECT,
location=GOOGLE_CLOUD_REGION,
model_resource_name="2008244793993330688",
job_display_name=f"{MODEL_NAME}-{TIMESTAMP}",
gcs_source=[f"{TEST_GCS_BUCKET}/{TEST_FILENAME}"],
gcs_destination=f"{TEST_GCS_BUCKET}/results/",
accelerator_type=None,
accelerator_count=None,
)import os
import json
RESULTS_DIRECTORY = "results"
RESULTS_DIRECTORY_FULL = f'{TEST_GCS_BUCKET}/{RESULTS_DIRECTORY}'
# Create missing directories
os.makedirs(RESULTS_DIRECTORY, exist_ok=True)
# Get the Cloud Storage paths for each result
!gsutil -m cp -r $RESULTS_DIRECTORY_FULL $RESULTS_DIRECTORY
# Get most recently modified directory
latest_directory = max(
[
os.path.join(RESULTS_DIRECTORY, d)
for d in os.listdir(RESULTS_DIRECTORY)
],
key=os.path.getmtime,
)
# Get downloaded results in directory
results_files = []
for dirpath, subdirs, files in os.walk(latest_directory):
for file in files:
if file.startswith("prediction.results"):
results_files.append(os.path.join(dirpath, file))
# Consolidate all the results into a list
results = []
for results_file in results_files:
# Download each result
with open(results_file, "r") as file:
results.extend([json.loads(line) for line in file.readlines()])resultsnum_correct = 0
for result in results:
label = os.path.basename(result["instance"]).split("_")[0]
prediction = result["prediction"]["label"]
print(f"label({label})/prediction({prediction})")
if label == prediction:
num_correct = num_correct + 1
print()
print(f"number of results: {len(results)}")
print(f"number of correct: {num_correct}")
print(f"Accuracy: {num_correct/len(results)}") |
deep-diver/mlops-hf-tf-vision-models | advanced_part1/kubeflow_runner.py | from absl import logging
from tfx import v1 as tfx
from tfx.orchestration.kubeflow.v2 import kubeflow_v2_dag_runner as runner
from pipeline import configs
from pipeline import kubeflow_pipeline
def run():
runner_config = runner.KubeflowV2DagRunnerConfig(
default_image=configs.PIPELINE_IMAGE
)
runner.KubeflowV2DagRunner(
config=runner_config,
output_filename=configs.PIPELINE_NAME + "_pipeline.json",
).run(
kubeflow_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=configs.PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
},
eval_configs=configs.EVAL_CONFIGS,
ai_platform_training_args=configs.GCP_AI_PLATFORM_TRAINING_ARGS,
ai_platform_serving_args=configs.GCP_AI_PLATFORM_SERVING_ARGS,
example_gen_beam_args=configs.EXAMPLE_GEN_BEAM_ARGS,
transform_beam_args=configs.TRANSFORM_BEAM_ARGS,
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/local_runner.py | import os
from absl import logging
from tfx import v1 as tfx
from pipeline import configs
from pipeline import local_pipeline
OUTPUT_DIR = "."
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", configs.PIPELINE_NAME)
METADATA_PATH = os.path.join(
OUTPUT_DIR, "tfx_metadata", configs.PIPELINE_NAME, "metadata.db"
)
SERVING_MODEL_DIR = os.path.join(PIPELINE_ROOT, "serving_model")
def run():
tfx.orchestration.LocalDagRunner().run(
local_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
},
eval_configs=configs.EVAL_CONFIGS,
serving_model_dir=SERVING_MODEL_DIR,
metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(
METADATA_PATH
),
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/kubeflow_runner.py | from absl import logging
from tfx import v1 as tfx
from tfx.orchestration.kubeflow.v2 import kubeflow_v2_dag_runner as runner
from tfx.proto import tuner_pb2
from pipeline import configs
from pipeline import kubeflow_pipeline
def run():
runner_config = runner.KubeflowV2DagRunnerConfig(
default_image=configs.PIPELINE_IMAGE
)
runner.KubeflowV2DagRunner(
config=runner_config,
output_filename=configs.PIPELINE_NAME + "_pipeline.json",
).run(
kubeflow_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=configs.PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
"tuner_fn": configs.TUNER_FN,
},
eval_configs=configs.EVAL_CONFIGS,
ai_platform_training_args=configs.GCP_AI_PLATFORM_TRAINING_ARGS,
ai_platform_tuner_args=configs.GCP_AI_PLATFORM_TUNER_ARGS,
tuner_args=tuner_pb2.TuneArgs(
num_parallel_trials=configs.NUM_PARALLEL_TRIALS
),
ai_platform_serving_args=configs.GCP_AI_PLATFORM_SERVING_ARGS,
example_gen_beam_args=configs.EXAMPLE_GEN_BEAM_ARGS,
transform_beam_args=configs.TRANSFORM_BEAM_ARGS,
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/local_runner.py | import os
from absl import logging
from tfx import v1 as tfx
from pipeline import configs
from pipeline import local_pipeline
OUTPUT_DIR = "."
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", configs.PIPELINE_NAME)
METADATA_PATH = os.path.join(
OUTPUT_DIR, "tfx_metadata", configs.PIPELINE_NAME, "metadata.db"
)
SERVING_MODEL_DIR = os.path.join(PIPELINE_ROOT, "serving_model")
def run():
tfx.orchestration.LocalDagRunner().run(
local_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
"tuner_fn": configs.TUNER_FN,
},
hyperparameters=configs.HYPER_PARAMETERS,
eval_configs=configs.EVAL_CONFIGS,
serving_model_dir=SERVING_MODEL_DIR,
metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(
METADATA_PATH
),
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | basic/kubeflow_runner.py | from absl import logging
from tfx import v1 as tfx
from tfx.orchestration.kubeflow.v2 import kubeflow_v2_dag_runner as runner
from pipeline import configs
from pipeline import kubeflow_pipeline
def run():
runner_config = runner.KubeflowV2DagRunnerConfig(
default_image=configs.PIPELINE_IMAGE
)
runner.KubeflowV2DagRunner(
config=runner_config,
output_filename=configs.PIPELINE_NAME + "_pipeline.json",
).run(
kubeflow_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=configs.PIPELINE_ROOT,
data_path=configs.DATA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
},
ai_platform_training_args=configs.GCP_AI_PLATFORM_TRAINING_ARGS,
ai_platform_serving_args=configs.GCP_AI_PLATFORM_SERVING_ARGS,
example_gen_beam_args=configs.EXAMPLE_GEN_BEAM_ARGS,
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | basic/local_runner.py | import os
from absl import logging
from tfx import v1 as tfx
from pipeline import configs
from pipeline import local_pipeline
OUTPUT_DIR = "."
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", configs.PIPELINE_NAME)
METADATA_PATH = os.path.join(
OUTPUT_DIR, "tfx_metadata", configs.PIPELINE_NAME, "metadata.db"
)
SERVING_MODEL_DIR = os.path.join(PIPELINE_ROOT, "serving_model")
def run():
tfx.orchestration.LocalDagRunner().run(
local_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_path=configs.DATA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
},
serving_model_dir=SERVING_MODEL_DIR,
metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(
METADATA_PATH
),
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | dataset/create_tfrecords.py | """
Script to generate TFRecord shards from the Sidewalks dataset as shown in this
blog post: https://huggingface.co./blog/fine-tune-segformer.
The recommended way to obtain TFRecord shards is via an Apache Beam
Pipeline with an execution runner like Dataflow. Example:
https://github.com/GoogleCloudPlatform/practical-ml-vision-book/blob/master/05_create_dataset/jpeg_to_tfrecord.py.
Usage:
python create_tfrecords --batch_size 16
python create_tfrecords --resize 256 # without --resize flag, no resizing is applied
References:
* https://github.com/GoogleCloudPlatform/practical-ml-vision-book/blob/master/05_create_dataset/05_split_tfrecord.ipynb
* https://www.tensorflow.org/tutorials/images/segmentation
"""
import argparse
import math
import os
from typing import Tuple
import datasets
import numpy as np
import tensorflow as tf
import tqdm
from PIL import Image
RESOLUTION = 256
def load_beans_dataset(args):
hf_dataset_identifier = "beans"
ds = datasets.load_dataset(hf_dataset_identifier)
ds = ds.shuffle(seed=1)
ds = ds["train"].train_test_split(test_size=args.split, seed=args.seed)
train_ds = ds["train"]
val_ds = ds["test"]
return train_ds, val_ds
def resize_img(
image: tf.Tensor, label: tf.Tensor, resize: int
) -> Tuple[tf.Tensor, tf.Tensor]:
image = tf.image.resize(image, (resize, resize))
return image, label
def process_image(
image: Image, label: Image, resize: int
) -> Tuple[tf.Tensor, tf.Tensor]:
image = np.array(image)
label = np.array(label)
image = tf.convert_to_tensor(image)
label = tf.convert_to_tensor(label)
if resize:
image, label = resize_img(image, label, resize)
return image, label
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def _float_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def create_tfrecord(image: Image, label: Image, resize: int):
image, label = process_image(image, label, resize)
image_dims = image.shape
image = tf.reshape(image, [-1]) # flatten to 1D array
label = tf.reshape(label, [-1]) # flatten to 1D array
return tf.train.Example(
features=tf.train.Features(
feature={
"image": _float_feature(image.numpy()),
"image_shape": _int64_feature(
[image_dims[0], image_dims[1], image_dims[2]]
),
"label": _int64_feature(label.numpy()),
}
)
).SerializeToString()
def write_tfrecords(root_dir, dataset, split, batch_size, resize):
print(f"Preparing TFRecords for split: {split}.")
for step in tqdm.tnrange(int(math.ceil(len(dataset) / batch_size))):
temp_ds = dataset[step * batch_size : (step + 1) * batch_size]
shard_size = len(temp_ds["image"])
filename = os.path.join(
root_dir, "{}-{:02d}-{}.tfrec".format(split, step, shard_size)
)
with tf.io.TFRecordWriter(filename) as out_file:
for i in range(shard_size):
image = temp_ds["image"][i]
label = temp_ds["labels"][i]
example = create_tfrecord(image, label, resize)
out_file.write(example)
print("Wrote file {} containing {} records".format(filename, shard_size))
def main(args):
train_ds, val_ds = load_beans_dataset(args)
print("Dataset loaded from HF.")
if not os.path.exists(args.root_tfrecord_dir):
os.makedirs(args.root_tfrecord_dir, exist_ok=True)
print(args.resize)
write_tfrecords(
args.root_tfrecord_dir, train_ds, "train", args.batch_size, args.resize
)
write_tfrecords(args.root_tfrecord_dir, val_ds, "val", args.batch_size, args.resize)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--split", help="Train and test split.", default=0.2, type=float
)
parser.add_argument(
"--seed",
help="Seed to be used while performing train-test splits.",
default=2022,
type=int,
)
parser.add_argument(
"--root_tfrecord_dir",
help="Root directory where the TFRecord shards will be serialized.",
default="beans-tfrecords",
type=str,
)
parser.add_argument(
"--batch_size",
help="Number of samples to process in a batch before serializing a single TFRecord shard.",
default=32,
type=int,
)
parser.add_argument(
"--resize",
help="Width and height size the image will be resized to. No resizing will be applied when this isn't set.",
type=int,
)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
main(args)
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/kubeflow_runner.py | from absl import logging
from tfx import v1 as tfx
from tfx.orchestration.kubeflow.v2 import kubeflow_v2_dag_runner as runner
from tfx.proto import tuner_pb2
from pipeline import configs
from pipeline import kubeflow_pipeline
def run():
runner_config = runner.KubeflowV2DagRunnerConfig(
default_image=configs.PIPELINE_IMAGE
)
runner.KubeflowV2DagRunner(
config=runner_config,
output_filename=configs.PIPELINE_NAME + "_pipeline.json",
).run(
kubeflow_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=configs.PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
"tuner_fn": configs.TUNER_FN,
},
eval_configs=configs.EVAL_CONFIGS,
ai_platform_training_args=configs.GCP_AI_PLATFORM_TRAINING_ARGS,
ai_platform_tuner_args=configs.GCP_AI_PLATFORM_TUNER_ARGS,
tuner_args=tuner_pb2.TuneArgs(
num_parallel_trials=configs.NUM_PARALLEL_TRIALS
),
ai_platform_serving_args=configs.GCP_AI_PLATFORM_SERVING_ARGS,
example_gen_beam_args=configs.EXAMPLE_GEN_BEAM_ARGS,
transform_beam_args=configs.TRANSFORM_BEAM_ARGS,
hf_pusher_args=configs.HF_PUSHER_ARGS,
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/local_runner.py | import os
from absl import logging
from tfx import v1 as tfx
from pipeline import configs
from pipeline import local_pipeline
OUTPUT_DIR = "."
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", configs.PIPELINE_NAME)
METADATA_PATH = os.path.join(
OUTPUT_DIR, "tfx_metadata", configs.PIPELINE_NAME, "metadata.db"
)
SERVING_MODEL_DIR = os.path.join(PIPELINE_ROOT, "serving_model")
def run():
tfx.orchestration.LocalDagRunner().run(
local_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
"tuner_fn": configs.TUNER_FN,
},
hyperparameters=configs.HYPER_PARAMETERS,
eval_configs=configs.EVAL_CONFIGS,
serving_model_dir=SERVING_MODEL_DIR,
metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(
METADATA_PATH
),
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | intermediate/kubeflow_runner.py | from absl import logging
from tfx import v1 as tfx
from tfx.orchestration.kubeflow.v2 import kubeflow_v2_dag_runner as runner
from pipeline import configs
from pipeline import kubeflow_pipeline
def run():
runner_config = runner.KubeflowV2DagRunnerConfig(
default_image=configs.PIPELINE_IMAGE
)
runner.KubeflowV2DagRunner(
config=runner_config,
output_filename=configs.PIPELINE_NAME + "_pipeline.json",
).run(
kubeflow_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=configs.PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
},
ai_platform_training_args=configs.GCP_AI_PLATFORM_TRAINING_ARGS,
ai_platform_serving_args=configs.GCP_AI_PLATFORM_SERVING_ARGS,
example_gen_beam_args=configs.EXAMPLE_GEN_BEAM_ARGS,
transform_beam_args=configs.TRANSFORM_BEAM_ARGS,
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | intermediate/local_runner.py | import os
from absl import logging
from tfx import v1 as tfx
from pipeline import configs
from pipeline import local_pipeline
OUTPUT_DIR = "."
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", configs.PIPELINE_NAME)
METADATA_PATH = os.path.join(
OUTPUT_DIR, "tfx_metadata", configs.PIPELINE_NAME, "metadata.db"
)
SERVING_MODEL_DIR = os.path.join(PIPELINE_ROOT, "serving_model")
def run():
tfx.orchestration.LocalDagRunner().run(
local_pipeline.create_pipeline(
pipeline_name=configs.PIPELINE_NAME,
pipeline_root=PIPELINE_ROOT,
data_path=configs.DATA_PATH,
schema_path=configs.SCHEMA_PATH,
modules={
"training_fn": configs.TRAINING_FN,
"preprocessing_fn": configs.PREPROCESSING_FN,
},
serving_model_dir=SERVING_MODEL_DIR,
metadata_connection_config=tfx.orchestration.metadata.sqlite_metadata_connection_config(
METADATA_PATH
),
)
)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
run()
|
deep-diver/mlops-hf-tf-vision-models | notebooks/advanced_part1.ipynb | data_path = "gs://beans-lowres/tfrecords"
local_data_path = "data"
model_file = "modules/model.py"
model_fn = "modules.model.run_fn"
proprocessing_file = "modules/preprocessing.py"
preprocessing_fn = "modules.preprocessing.preprocessing_fn"
schema_file = "schema.pbtxt"import tfx
tfx.__version__from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import SchemaGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Trainer
from tfx.components import Evaluator
from tfx.components import Pusher
from tfx.proto import example_gen_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import LatestBlessedModelResolver
import tensorflow_model_analysis as tfmacontext = InteractiveContext()input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(
input_base=local_data_path,
input_config=input_config
)
context.run(example_gen)statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
context.run(statistics_gen)context.show(statistics_gen.outputs['statistics'])%%writefile {schema_file}
feature {
name: "image"
type: FLOAT
presence {
min_fraction: 1.0
}
float_domain {
min: 0
max: 255
}
shape {
dim { size: 256 }
dim { size: 256 }
dim { size: 3 }
}
}
feature {
name: "image_shape"
type: INT
presence {
min_fraction: 1.0
}
shape {
dim { size: 3 }
}
}
feature {
name: "label"
type: INT
presence {
min_fraction: 1.0
}
int_domain {
min: 0
max: 2
}
shape {
dim { size: 1 }
}
}schema_gen = tfx.components.ImportSchemaGen(
schema_file=schema_file)
context.run(schema_gen)example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema']
)
context.run(example_validator)context.show(example_validator.outputs['anomalies'])%%writefile {proprocessing_file}
import tensorflow as tf
IMAGE_KEY = "image"
LABEL_KEY = "label"
MODEL_INPUT_IMAGE_KEY = "pixel_values"
MODEL_INPUT_LABEL_KEY = "labels"
INPUT_IMG_SIZE = 224
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
inputs[IMAGE_KEY] = tf.image.resize(
inputs[IMAGE_KEY], [INPUT_IMG_SIZE, INPUT_IMG_SIZE]
)
inputs[IMAGE_KEY] = inputs[IMAGE_KEY] / 255.0
inputs[IMAGE_KEY] = tf.transpose(inputs[IMAGE_KEY], [0, 3, 1, 2])
outputs[MODEL_INPUT_IMAGE_KEY] = inputs[IMAGE_KEY]
outputs[MODEL_INPUT_LABEL_KEY] = inputs[LABEL_KEY]
return outputstransform = Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
preprocessing_fn=preprocessing_fn)context.run(transform)%%writefile {model_file}
from typing import List, Dict, Tuple
import absl
import tensorflow as tf
import tensorflow_transform as tft
from transformers import ViTFeatureExtractor, TFViTForImageClassification
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import DataAccessor
feature_extractor = ViTFeatureExtractor()
_TRAIN_LENGTH = 128
_EVAL_LENGTH = 128
_TRAIN_BATCH_SIZE = 8
_EVAL_BATCH_SIZE = 8
_EPOCHS = 1
_LABELS = ['angular_leaf_spot', 'bean_rust', 'healthy']
_CONCRETE_INPUT = "pixel_values"
_MODEL_INPUT_LABEL_KEY = "labels"
def INFO(text: str):
absl.logging.info(text)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {_CONCRETE_INPUT: decoded_images}
def _model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(
shape=[None, 3, 224, 224], dtype=tf.float32, name=_CONCRETE_INPUT
)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
###
def _transform_features_signature(
model: tf.keras.Model, tf_transform_output: tft.TFTransformOutput
):
"""
transform_features_signature simply returns a function that transforms
any data of the type of tf.Example which is denoted as the type of sta
ndard_artifacts.Examples in TFX. The purpose of this function is to ap
ply Transform Graph obtained from Transform component to the data prod
uced by ImportExampleGen. This function will be used in the Evaluator
component, so the raw evaluation inputs from ImportExampleGen can be a
pporiately transformed that the model could understand.
"""
# basically, what Transform component emits is a SavedModel that knows
# how to transform data. transform_features_layer() simply returns the
# layer from the Transform.
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(serialized_tf_examples):
"""
raw_feature_spec returns a set of feature maps(dict) for the input
TFRecords based on the knowledge that Transform component has lear
ned(learn doesn't mean training here). By using this information,
the raw data from ImportExampleGen could be parsed with tf.io.parse
_example utility function.
Then, it is passed to the model.tft_layer, so the final output we
get is the transformed data of the raw input.
"""
feature_spec = tf_transform_output.raw_feature_spec()
parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
transformed_features = model.tft_layer(parsed_features)
return transformed_features
return serve_tf_examples_fn
def _tf_examples_serving_signature(model, tf_transform_output):
"""
tf_examples_serving_signature simply returns a function that performs
data transformation(preprocessing) and model prediction in a sequential
manner. How data transformation is done is idential to the process of
transform_features_signature function.
"""
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(
serialized_tf_example: tf.Tensor,
) -> Dict[str, tf.Tensor]:
raw_feature_spec = tf_transform_output.raw_feature_spec()
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer(raw_features)
logits = model(transformed_features).logits
return {_MODEL_INPUT_LABEL_KEY: logits}
return serve_tf_examples_fn
###
def _input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = 32,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_MODEL_INPUT_LABEL_KEY
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
def _build_model():
id2label={str(i): c for i, c in enumerate(_LABELS)}
label2id={c: str(i) for i, c in enumerate(_LABELS)}
model = TFViTForImageClassification.from_pretrained(
"google/vit-base-patch16-224-in21k",
num_labels=len(_LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable=False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='adam', loss=loss, metrics=["accuracy"])
return model
def run_fn(fn_args: FnArgs):
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=_TRAIN_BATCH_SIZE,
)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=_EVAL_BATCH_SIZE,
)
model = _build_model()
model.fit(
train_dataset,
steps_per_epoch=_TRAIN_LENGTH // _TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=_EVAL_LENGTH // _EVAL_BATCH_SIZE,
epochs=_EPOCHS,
)
model.save(
fn_args.serving_model_dir,
save_format="tf",
signatures={
"serving_default": _model_exporter(model),
"transform_features": _transform_features_signature(
model, tf_transform_output
),
"from_examples": _tf_examples_serving_signature(model, tf_transform_output),
},
)trainer = Trainer(
run_fn=model_fn,
transformed_examples=transform.outputs["transformed_examples"],
transform_graph=transform.outputs["transform_graph"],
schema=schema_gen.outputs["schema"],
)context.run(trainer)model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")context.run(model_resolver)eval_configs = tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(
signature_name="from_examples",
preprocessing_function_names=["transform_features"],
label_key="labels",
prediction_key="labels",
)
],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(
metrics=[
tfma.MetricConfig(
class_name="SparseCategoricalAccuracy",
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={"value": 0.55}
),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={"value": -1e-3},
),
),
)
]
)
],
)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)context.run(evaluator) |
deep-diver/mlops-hf-tf-vision-models | notebooks/advanced_part2.ipynb | data_path = "gs://beans-lowres/tfrecords"
local_data_path = "data"
model_file = "modules/model.py"
model_fn = "modules.model.run_fn"
tuner_fn = "modules.model.tuner_fn"
proprocessing_file = "modules/preprocessing.py"
preprocessing_fn = "modules.preprocessing.preprocessing_fn"
schema_file = "schema.pbtxt"import tfx
tfx.__version__from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import SchemaGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Trainer
from tfx.components import Tuner
from tfx.components import Evaluator
from tfx.components import Pusher
from tfx.proto import example_gen_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import LatestBlessedModelResolver
import tensorflow_model_analysis as tfmacontext = InteractiveContext()input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(
input_base=local_data_path,
input_config=input_config
)
context.run(example_gen)statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
context.run(statistics_gen)context.show(statistics_gen.outputs['statistics'])%%writefile {schema_file}
feature {
name: "image"
type: FLOAT
presence {
min_fraction: 1.0
}
float_domain {
min: 0
max: 255
}
shape {
dim { size: 256 }
dim { size: 256 }
dim { size: 3 }
}
}
feature {
name: "image_shape"
type: INT
presence {
min_fraction: 1.0
}
shape {
dim { size: 3 }
}
}
feature {
name: "label"
type: INT
presence {
min_fraction: 1.0
}
int_domain {
min: 0
max: 2
}
shape {
dim { size: 1 }
}
}schema_gen = tfx.components.ImportSchemaGen(
schema_file=schema_file)
context.run(schema_gen)example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema']
)
context.run(example_validator)context.show(example_validator.outputs['anomalies'])%%writefile {proprocessing_file}
import tensorflow as tf
IMAGE_KEY = "image"
LABEL_KEY = "label"
MODEL_INPUT_IMAGE_KEY = "pixel_values"
MODEL_INPUT_LABEL_KEY = "labels"
INPUT_IMG_SIZE = 224
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
inputs[IMAGE_KEY] = tf.image.resize(
inputs[IMAGE_KEY], [INPUT_IMG_SIZE, INPUT_IMG_SIZE]
)
inputs[IMAGE_KEY] = inputs[IMAGE_KEY] / 255.0
inputs[IMAGE_KEY] = tf.transpose(inputs[IMAGE_KEY], [0, 3, 1, 2])
outputs[MODEL_INPUT_IMAGE_KEY] = inputs[IMAGE_KEY]
outputs[MODEL_INPUT_LABEL_KEY] = inputs[LABEL_KEY]
return outputstransform = Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
preprocessing_fn=preprocessing_fn)context.run(transform)%%writefile {model_file}
from typing import List, Dict, Tuple
import absl
import tensorflow as tf
import keras_tuner
import tensorflow_transform as tft
from transformers import ViTFeatureExtractor, TFViTForImageClassification
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx.v1.components import TunerFnResult
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import DataAccessor
feature_extractor = ViTFeatureExtractor()
_TRAIN_LENGTH = 128
_EVAL_LENGTH = 128
_TRAIN_BATCH_SIZE = 8
_EVAL_BATCH_SIZE = 8
_EPOCHS = 1
_LABELS = ['angular_leaf_spot', 'bean_rust', 'healthy']
_CONCRETE_INPUT = "pixel_values"
_MODEL_INPUT_LABEL_KEY = "labels"
def INFO(text: str):
absl.logging.info(text)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {_CONCRETE_INPUT: decoded_images}
def _model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(
shape=[None, 3, 224, 224], dtype=tf.float32, name=_CONCRETE_INPUT
)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
###
def _transform_features_signature(
model: tf.keras.Model, tf_transform_output: tft.TFTransformOutput
):
"""
transform_features_signature simply returns a function that transforms
any data of the type of tf.Example which is denoted as the type of sta
ndard_artifacts.Examples in TFX. The purpose of this function is to ap
ply Transform Graph obtained from Transform component to the data prod
uced by ImportExampleGen. This function will be used in the Evaluator
component, so the raw evaluation inputs from ImportExampleGen can be a
pporiately transformed that the model could understand.
"""
# basically, what Transform component emits is a SavedModel that knows
# how to transform data. transform_features_layer() simply returns the
# layer from the Transform.
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(serialized_tf_examples):
"""
raw_feature_spec returns a set of feature maps(dict) for the input
TFRecords based on the knowledge that Transform component has lear
ned(learn doesn't mean training here). By using this information,
the raw data from ImportExampleGen could be parsed with tf.io.parse
_example utility function.
Then, it is passed to the model.tft_layer, so the final output we
get is the transformed data of the raw input.
"""
feature_spec = tf_transform_output.raw_feature_spec()
parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
transformed_features = model.tft_layer(parsed_features)
return transformed_features
return serve_tf_examples_fn
def _tf_examples_serving_signature(model, tf_transform_output):
"""
tf_examples_serving_signature simply returns a function that performs
data transformation(preprocessing) and model prediction in a sequential
manner. How data transformation is done is idential to the process of
transform_features_signature function.
"""
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(
serialized_tf_example: tf.Tensor,
) -> Dict[str, tf.Tensor]:
raw_feature_spec = tf_transform_output.raw_feature_spec()
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer(raw_features)
logits = model(transformed_features).logits
return {_MODEL_INPUT_LABEL_KEY: logits}
return serve_tf_examples_fn
###
def _input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = 32,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_MODEL_INPUT_LABEL_KEY
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
def _get_hyperparameters() -> keras_tuner.HyperParameters:
hp = keras_tuner.HyperParameters()
hp.Choice("learning_rate", [1e-3, 1e-2], default=1e-3)
return hp
def _build_model(hparams: keras_tuner.HyperParameters):
id2label={str(i): c for i, c in enumerate(_LABELS)}
label2id={c: str(i) for i, c in enumerate(_LABELS)}
model = TFViTForImageClassification.from_pretrained(
"google/vit-base-patch16-224-in21k",
num_labels=len(_LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable=False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=hparams.get("learning_rate"))
model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])
return model
def run_fn(fn_args: FnArgs):
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=_TRAIN_BATCH_SIZE,
)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=_EVAL_BATCH_SIZE,
)
hparams = keras_tuner.HyperParameters.from_config(fn_args.hyperparameters)
INFO(f"HyperParameters for training: {hparams.get_config()}")
model = _build_model(hparams)
model.fit(
train_dataset,
steps_per_epoch=_TRAIN_LENGTH // _TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=_EVAL_LENGTH // _EVAL_BATCH_SIZE,
epochs=_EPOCHS,
)
model.save(
fn_args.serving_model_dir,
save_format="tf",
signatures={
"serving_default": _model_exporter(model),
"transform_features": _transform_features_signature(
model, tf_transform_output
),
"from_examples": _tf_examples_serving_signature(model, tf_transform_output),
},
)
def tuner_fn(fn_args: FnArgs) -> TunerFnResult:
tuner = keras_tuner.RandomSearch(
_build_model,
max_trials=6,
hyperparameters=_get_hyperparameters(),
allow_new_entries=False,
objective=keras_tuner.Objective("val_accuracy", "max"),
directory=fn_args.working_dir,
project_name="img_classification_tuning",
)
tf_transform_output = tft.TFTransformOutput(fn_args.transform_graph_path)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=_TRAIN_BATCH_SIZE,
)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=_EVAL_BATCH_SIZE,
)
return TunerFnResult(
tuner=tuner,
fit_kwargs={
"x": train_dataset,
"validation_data": eval_dataset,
"steps_per_epoch": _TRAIN_LENGTH // _TRAIN_BATCH_SIZE,
"validation_steps": _EVAL_LENGTH // _EVAL_BATCH_SIZE,
},
)
tuner = Tuner(
tuner_fn=tuner_fn,
examples=transform.outputs["transformed_examples"],
schema=schema_gen.outputs["schema"],
transform_graph=transform.outputs["transform_graph"],
)context.run(tuner)trainer = Trainer(
run_fn=model_fn,
transformed_examples=transform.outputs["transformed_examples"],
transform_graph=transform.outputs["transform_graph"],
schema=schema_gen.outputs["schema"],
hyperparameters=tuner.outputs["best_hyperparameters"],
)context.run(trainer)model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")context.run(model_resolver)eval_configs = tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(
signature_name="from_examples",
preprocessing_function_names=["transform_features"],
label_key="labels",
prediction_key="labels",
)
],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(
metrics=[
tfma.MetricConfig(
class_name="SparseCategoricalAccuracy",
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={"value": 0.55}
),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={"value": -1e-3},
),
),
)
]
)
],
)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)context.run(evaluator) |
deep-diver/mlops-hf-tf-vision-models | notebooks/basic.ipynb | data_path = "gs://beans-lowres/tfrecords"
local_data_path = "data"
model_file = "modules/model.py"
model_fn = "modules.model.run_fn"import tfx
tfx.__version__from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.components import ImportExampleGen
from tfx.components import Trainer
from tfx.components import Pusher
from tfx.components import StatisticsGen
from tfx.proto import example_gen_pb2context = InteractiveContext()input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(
input_base=local_data_path,
input_config=input_config
)context.run(example_gen)%%writefile {model_file}
from typing import List, Dict, Tuple
import absl
import tensorflow as tf
from transformers import ViTFeatureExtractor, TFViTForImageClassification
from tfx.components.trainer.fn_args_utils import FnArgs
feature_extractor = ViTFeatureExtractor()
_TRAIN_LENGTH = 128
_EVAL_LENGTH = 128
_TRAIN_BATCH_SIZE = 8
_EVAL_BATCH_SIZE = 8
_EPOCHS = 1
_LABELS = ['angular_leaf_spot', 'bean_rust', 'healthy']
_CONCRETE_INPUT = "pixel_values"
def INFO(text: str):
absl.logging.info(text)
###
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {_CONCRETE_INPUT: decoded_images}
def _model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(
shape=[None, 3, 224, 224], dtype=tf.float32, name=_CONCRETE_INPUT
)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
###
def _parse_tfr(proto):
feature_description = {
"image": tf.io.VarLenFeature(tf.float32),
"image_shape": tf.io.VarLenFeature(tf.int64),
"label": tf.io.VarLenFeature(tf.int64),
}
rec = tf.io.parse_single_example(proto, feature_description)
image_shape = tf.sparse.to_dense(rec["image_shape"])
image = tf.reshape(tf.sparse.to_dense(rec["image"]), image_shape)
label = tf.sparse.to_dense(rec["label"])
return {"pixel_values": image, "labels": label}
def _preprocess(example_batch):
images = example_batch["pixel_values"]
images = tf.transpose(images, perm=[0, 1, 2, 3]) # (batch_size, height, width, num_channels)
images = tf.image.resize(images, (224, 224))
images = tf.transpose(images, perm=[0, 3, 1, 2])
labels = example_batch["labels"]
labels = tf.transpose(labels, perm=[0, 1]) # So, that TF can evaluation the shapes.
return {"pixel_values": images, "labels": labels}
def _input_fn(
file_pattern: List[str],
batch_size: int = 32,
is_train: bool = False,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = tf.data.TFRecordDataset(
tf.io.gfile.glob(file_pattern[0] + ".gz"),
num_parallel_reads=tf.data.AUTOTUNE,
compression_type="GZIP",
).map(_parse_tfr, num_parallel_calls=tf.data.AUTOTUNE)
if is_train:
dataset = dataset.shuffle(batch_size * 2)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(tf.data.AUTOTUNE)
dataset = dataset.map(_preprocess)
return dataset
def _build_model():
id2label={str(i): c for i, c in enumerate(_LABELS)}
label2id={c: str(i) for i, c in enumerate(_LABELS)}
model = TFViTForImageClassification.from_pretrained(
"google/vit-base-patch16-224-in21k",
num_labels=len(_LABELS),
label2id=label2id,
id2label=id2label,
)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='adam', loss=loss, metrics=["accuracy"])
return model
def run_fn(fn_args: FnArgs):
train_dataset = _input_fn(
fn_args.train_files,
is_train=True,
batch_size=_TRAIN_BATCH_SIZE,
)
eval_dataset = _input_fn(
fn_args.eval_files,
is_train=False,
batch_size=_EVAL_BATCH_SIZE,
)
model = _build_model()
model.fit(
train_dataset,
steps_per_epoch=_TRAIN_LENGTH // _TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=_EVAL_LENGTH // _TRAIN_BATCH_SIZE,
epochs=_EPOCHS,
)
model.save(
fn_args.serving_model_dir, save_format="tf", signatures=_model_exporter(model)
)trainer = Trainer(
run_fn=model_fn,
examples=example_gen.outputs["examples"],
)context.run(trainer) |
deep-diver/mlops-hf-tf-vision-models | notebooks/intermediate.ipynb | data_path = "gs://beans-lowres/tfrecords"
local_data_path = "data"
model_file = "modules/model.py"
model_fn = "modules.model.run_fn"
proprocessing_file = "modules/preprocessing.py"
preprocessing_fn = "modules.preprocessing.preprocessing_fn"
schema_file = "schema.pbtxt"import tfx
tfx.__version__from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import SchemaGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Trainer
from tfx.components import Pusher
from tfx.proto import example_gen_pb2context = InteractiveContext()input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(
input_base=local_data_path,
input_config=input_config
)
context.run(example_gen)statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
context.run(statistics_gen)context.show(statistics_gen.outputs['statistics'])%%writefile {schema_file}
feature {
name: "image"
type: FLOAT
presence {
min_fraction: 1.0
}
float_domain {
min: 0
max: 255
}
shape {
dim { size: 256 }
dim { size: 256 }
dim { size: 3 }
}
}
feature {
name: "image_shape"
type: INT
presence {
min_fraction: 1.0
}
shape {
dim { size: 3 }
}
}
feature {
name: "label"
type: INT
presence {
min_fraction: 1.0
}
int_domain {
min: 0
max: 2
}
shape {
dim { size: 1 }
}
}schema_gen = tfx.components.ImportSchemaGen(
schema_file=schema_file)
context.run(schema_gen)example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema']
)
context.run(example_validator)context.show(example_validator.outputs['anomalies'])%%writefile {proprocessing_file}
import tensorflow as tf
IMAGE_KEY = "image"
LABEL_KEY = "label"
MODEL_INPUT_IMAGE_KEY = "pixel_values"
MODEL_INPUT_LABEL_KEY = "labels"
INPUT_IMG_SIZE = 224
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
inputs[IMAGE_KEY] = tf.image.resize(
inputs[IMAGE_KEY], [INPUT_IMG_SIZE, INPUT_IMG_SIZE]
)
inputs[IMAGE_KEY] = inputs[IMAGE_KEY] / 255.0
inputs[IMAGE_KEY] = tf.transpose(inputs[IMAGE_KEY], [0, 3, 1, 2])
outputs[MODEL_INPUT_IMAGE_KEY] = inputs[IMAGE_KEY]
outputs[MODEL_INPUT_LABEL_KEY] = inputs[LABEL_KEY]
return outputstransform = Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
preprocessing_fn=preprocessing_fn)context.run(transform)%%writefile {model_file}
from typing import List, Dict, Tuple
import absl
import tensorflow as tf
import tensorflow_transform as tft
from transformers import ViTFeatureExtractor, TFViTForImageClassification
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import DataAccessor
feature_extractor = ViTFeatureExtractor()
_TRAIN_LENGTH = 128
_EVAL_LENGTH = 128
_TRAIN_BATCH_SIZE = 8
_EVAL_BATCH_SIZE = 8
_EPOCHS = 1
_LABELS = ['angular_leaf_spot', 'bean_rust', 'healthy']
_CONCRETE_INPUT = "pixel_values"
_MODEL_INPUT_LABEL_KEY = "labels"
def INFO(text: str):
absl.logging.info(text)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {_CONCRETE_INPUT: decoded_images}
def _model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(
shape=[None, 3, 224, 224], dtype=tf.float32, name=_CONCRETE_INPUT
)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
def _input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = 32,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_MODEL_INPUT_LABEL_KEY
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
def _build_model():
id2label={str(i): c for i, c in enumerate(_LABELS)}
label2id={c: str(i) for i, c in enumerate(_LABELS)}
model = TFViTForImageClassification.from_pretrained(
"google/vit-base-patch16-224-in21k",
num_labels=len(_LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable=False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='adam', loss=loss, metrics=["accuracy"])
return model
def run_fn(fn_args: FnArgs):
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=_TRAIN_BATCH_SIZE,
)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=_EVAL_BATCH_SIZE,
)
model = _build_model()
model.fit(
train_dataset,
steps_per_epoch=_TRAIN_LENGTH // _TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=_EVAL_LENGTH // _EVAL_BATCH_SIZE,
epochs=_EPOCHS,
)
model.save(
fn_args.serving_model_dir,
save_format="tf",
signatures=_model_exporter(model)
)trainer = Trainer(
run_fn=model_fn,
transformed_examples=transform.outputs["transformed_examples"],
transform_graph=transform.outputs["transform_graph"],
schema=schema_gen.outputs["schema"],
)context.run(trainer) |
deep-diver/mlops-hf-tf-vision-models | notebooks/parse_tfrecord.ipynb | import tensorflow as tfGCS_PATH_FULL_RESOUTION = "gs://beans-fullres/tfrecords"
GCS_PATH_LOW_RESOLUTION = "gs://beans-lowres/tfrecords"BATCH_SIZE = 4
AUTO = tf.data.AUTOTUNEdef parse_tfr(proto):
feature_description = {
"image": tf.io.VarLenFeature(tf.float32),
"image_shape": tf.io.VarLenFeature(tf.int64),
"label": tf.io.VarLenFeature(tf.int64),
}
rec = tf.io.parse_single_example(proto, feature_description)
image_shape = tf.sparse.to_dense(rec["image_shape"])
image = tf.reshape(tf.sparse.to_dense(rec["image"]), image_shape)
label = tf.sparse.to_dense(rec["label"])
return {"pixel_values": image, "label": label}
def prepare_dataset(GCS_PATH=GCS_PATH_FULL_RESOUTION,
split="train", batch_size=BATCH_SIZE):
if split not in ["train", "val"]:
raise ValueError(
"Invalid split provided. Supports splits are: `train` and `val`."
)
dataset = tf.data.TFRecordDataset(
[filename for filename in tf.io.gfile.glob(f"{GCS_PATH}/{split}-*")],
num_parallel_reads=AUTO,
).map(parse_tfr, num_parallel_calls=AUTO)
if split == "train":
dataset = dataset.shuffle(batch_size * 2)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(AUTO)
return datasettrain_dataset = prepare_dataset()
val_dataset = prepare_dataset(split="val")for batch in train_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape)for batch in val_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape)train_dataset = prepare_dataset(GCS_PATH_LOW_RESOLUTION)
val_dataset = prepare_dataset(GCS_PATH_LOW_RESOLUTION, split="val")for batch in train_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape)for batch in val_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape) |
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/ViT.py | import tensorflow as tf
from transformers import TFViTForImageClassification
from .common import LABELS
from .common import PRETRAIN_CHECKPOINT
from .utils import INFO
def build_model():
id2label = {str(i): c for i, c in enumerate(LABELS)}
label2id = {c: str(i) for i, c in enumerate(LABELS)}
model = TFViTForImageClassification.from_pretrained(
PRETRAIN_CHECKPOINT,
num_labels=len(LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable = False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer="adam", loss=loss, metrics=["accuracy"])
INFO(model.summary())
return model
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/common.py | IMAGE_TFREC_KEY = "image"
IMAGE_SHAPE_TFREC_KEY = "image_shape"
LABEL_TFREC_KEY = "label"
MODEL_INPUT_IMAGE_KEY = "pixel_values"
MODEL_INPUT_LABEL_KEY = "labels"
IMAGE_MODEL_KEY = "pixel_values"
LABEL_MODEL_KEY = "labels"
CONCRETE_INPUT = "pixel_values"
PRETRAIN_CHECKPOINT = "google/vit-base-patch16-224-in21k"
LABELS = ["angular_leaf_spot", "bean_rust", "healthy"]
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/hyperparams.py | EPOCHS = 1
BATCH_SIZE = 32
TRAIN_BATCH_SIZE = 32
EVAL_BATCH_SIZE = 32
TRAIN_LENGTH = 1034
EVAL_LENGTH = 128
INPUT_IMG_SIZE = 224
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/preprocessing.py | import tensorflow as tf
from .common import IMAGE_TFREC_KEY, LABEL_TFREC_KEY
from .common import IMAGE_MODEL_KEY, LABEL_MODEL_KEY
from .hyperparams import INPUT_IMG_SIZE
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
inputs[IMAGE_TFREC_KEY] = tf.image.resize(
inputs[IMAGE_TFREC_KEY], [INPUT_IMG_SIZE, INPUT_IMG_SIZE]
)
inputs[IMAGE_TFREC_KEY] = inputs[IMAGE_TFREC_KEY] / 255.0
inputs[IMAGE_TFREC_KEY] = tf.transpose(inputs[IMAGE_TFREC_KEY], [0, 3, 1, 2])
outputs[IMAGE_MODEL_KEY] = inputs[IMAGE_TFREC_KEY]
outputs[LABEL_MODEL_KEY] = inputs[LABEL_TFREC_KEY]
return outputs
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/signatures.py | from typing import Dict
import tensorflow as tf
import tensorflow_transform as tft
from transformers import ViTFeatureExtractor
from .common import PRETRAIN_CHECKPOINT
from .common import CONCRETE_INPUT
from .common import LABEL_MODEL_KEY
feature_extractor = ViTFeatureExtractor.from_pretrained(PRETRAIN_CHECKPOINT)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {CONCRETE_INPUT: decoded_images}
def model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(shape=[None, 3, 224, 224], dtype=tf.float32, name=CONCRETE_INPUT)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
def transform_features_signature(
model: tf.keras.Model, tf_transform_output: tft.TFTransformOutput
):
"""
transform_features_signature simply returns a function that transforms
any data of the type of tf.Example which is denoted as the type of sta
ndard_artifacts.Examples in TFX. The purpose of this function is to ap
ply Transform Graph obtained from Transform component to the data prod
uced by ImportExampleGen. This function will be used in the Evaluator
component, so the raw evaluation inputs from ImportExampleGen can be a
pporiately transformed that the model could understand.
"""
# basically, what Transform component emits is a SavedModel that knows
# how to transform data. transform_features_layer() simply returns the
# layer from the Transform.
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(serialized_tf_examples):
"""
raw_feature_spec returns a set of feature maps(dict) for the input
TFRecords based on the knowledge that Transform component has lear
ned(learn doesn't mean training here). By using this information,
the raw data from ImportExampleGen could be parsed with tf.io.parse
_example utility function.
Then, it is passed to the model.tft_layer, so the final output we
get is the transformed data of the raw input.
"""
feature_spec = tf_transform_output.raw_feature_spec()
parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
transformed_features = model.tft_layer(parsed_features)
return transformed_features
return serve_tf_examples_fn
def tf_examples_serving_signature(model, tf_transform_output):
"""
tf_examples_serving_signature simply returns a function that performs
data transformation(preprocessing) and model prediction in a sequential
manner. How data transformation is done is idential to the process of
transform_features_signature function.
"""
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(
serialized_tf_example: tf.Tensor,
) -> Dict[str, tf.Tensor]:
raw_feature_spec = tf_transform_output.raw_feature_spec()
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer(raw_features)
logits = model(transformed_features).logits
return {LABEL_MODEL_KEY: logits}
return serve_tf_examples_fn
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/train.py | import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import FnArgs
from .train_data import input_fn
from .ViT import build_model
from .signatures import (
model_exporter,
transform_features_signature,
tf_examples_serving_signature,
)
from .hyperparams import TRAIN_BATCH_SIZE, EVAL_BATCH_SIZE
from .hyperparams import TRAIN_LENGTH, EVAL_LENGTH
from .hyperparams import EPOCHS
def run_fn(fn_args: FnArgs):
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=TRAIN_BATCH_SIZE,
)
eval_dataset = input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=EVAL_BATCH_SIZE,
)
model = build_model()
model.fit(
train_dataset,
steps_per_epoch=TRAIN_LENGTH // TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=EVAL_LENGTH // TRAIN_BATCH_SIZE,
epochs=EPOCHS,
)
model.save(
fn_args.serving_model_dir,
save_format="tf",
signatures={
"serving_default": model_exporter(model),
"transform_features": transform_features_signature(
model, tf_transform_output
),
"from_examples": tf_examples_serving_signature(model, tf_transform_output),
},
)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/train_data.py | from typing import List
import tensorflow as tf
import tensorflow_transform as tft
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import DataAccessor
from .utils import INFO
from .common import LABEL_MODEL_KEY
from .hyperparams import BATCH_SIZE
def input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = BATCH_SIZE,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=LABEL_MODEL_KEY
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/modules/utils.py | import absl
def INFO(text: str):
absl.logging.info(text)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/pipeline/configs.py | import os
import tensorflow_model_analysis as tfma
import tfx.extensions.google_cloud_ai_platform.constants as vertex_const
import tfx.extensions.google_cloud_ai_platform.trainer.executor as vertex_training_const
PIPELINE_NAME = "vit-e2e-pipeline-advanced-part1"
try:
import google.auth # pylint: disable=g-import-not-at-top # pytype: disable=import-error
try:
_, GOOGLE_CLOUD_PROJECT = google.auth.default()
except google.auth.exceptions.DefaultCredentialsError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
except ImportError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
GOOGLE_CLOUD_REGION = "us-central1"
GCS_BUCKET_NAME = GOOGLE_CLOUD_PROJECT + "-complete-mlops"
PIPELINE_IMAGE = f"gcr.io/{GOOGLE_CLOUD_PROJECT}/{PIPELINE_NAME}"
OUTPUT_DIR = os.path.join("gs://", GCS_BUCKET_NAME)
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", PIPELINE_NAME)
DATA_PATH = "gs://beans-lowres/tfrecords/"
SCHEMA_PATH = "pipeline/schema.pbtxt"
TRAINING_FN = "modules.train.run_fn"
PREPROCESSING_FN = "modules.preprocessing.preprocessing_fn"
EXAMPLE_GEN_BEAM_ARGS = None
TRANSFORM_BEAM_ARGS = None
EVAL_CONFIGS = tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(
signature_name="from_examples",
preprocessing_function_names=["transform_features"],
label_key="labels",
prediction_key="labels",
)
],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(
metrics=[
tfma.MetricConfig(
class_name="SparseCategoricalAccuracy",
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={"value": 0.55}
),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={"value": -1e-3},
),
),
)
]
)
],
)
GCP_AI_PLATFORM_TRAINING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_training_const.TRAINING_ARGS_KEY: {
"project": GOOGLE_CLOUD_PROJECT,
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
],
},
"use_gpu": True,
}
fullres_data = os.environ.get("FULL_RES_DATA", "false")
if fullres_data.lower() == "true":
DATA_PATH = "gs://beans-fullres/tfrecords/"
DATAFLOW_SERVICE_ACCOUNT = "[email protected]"
DATAFLOW_MACHINE_TYPE = "n1-standard-4"
DATAFLOW_MAX_WORKERS = 4
DATAFLOW_DISK_SIZE_GB = 100
EXAMPLE_GEN_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
]
TRANSFORM_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
"--worker_harness_container_image=" + PIPELINE_IMAGE,
]
GCP_AI_PLATFORM_TRAINING_ARGS[vertex_training_const.TRAINING_ARGS_KEY][
"worker_pool_specs"
] = [
{
"machine_spec": {
"machine_type": "n1-standard-8",
"accelerator_type": "NVIDIA_TESLA_V100",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
]
GCP_AI_PLATFORM_SERVING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_const.VERTEX_CONTAINER_IMAGE_URI_KEY: "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest",
vertex_const.SERVING_ARGS_KEY: {
"project_id": GOOGLE_CLOUD_PROJECT,
"deployed_model_display_name": PIPELINE_NAME.replace("-", "_"),
"endpoint_name": "prediction-" + PIPELINE_NAME.replace("-", "_"),
"traffic_split": {"0": 100},
"machine_type": "n1-standard-4",
"min_replica_count": 1,
"max_replica_count": 1,
},
}
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/pipeline/kubeflow_pipeline.py | from typing import Any, Dict, List, Optional, Text
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Evaluator
from tfx.extensions.google_cloud_ai_platform.trainer.component import (
Trainer as VertexTrainer,
)
from tfx.extensions.google_cloud_ai_platform.pusher.component import (
Pusher as VertexPusher,
)
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import (
LatestBlessedModelResolver,
)
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
schema_path: Text,
modules: Dict[Text, Text],
eval_configs: tfma.EvalConfig,
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
ai_platform_training_args: Optional[Dict[Text, Text]] = None,
ai_platform_serving_args: Optional[Dict[Text, Any]] = None,
example_gen_beam_args: Optional[List] = None,
transform_beam_args: Optional[List] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
if example_gen_beam_args is not None:
example_gen.with_beam_pipeline_args(example_gen_beam_args)
components.append(example_gen)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
components.append(statistics_gen)
schema_gen = tfx.components.ImportSchemaGen(schema_file=schema_path)
components.append(schema_gen)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"],
)
components.append(example_validator)
transform_args = {
"examples": example_gen.outputs["examples"],
"schema": schema_gen.outputs["schema"],
"preprocessing_fn": modules["preprocessing_fn"],
}
transform = Transform(**transform_args)
if transform_beam_args is not None:
transform.with_beam_pipeline_args(transform_beam_args)
components.append(transform)
trainer_args = {
"run_fn": modules["training_fn"],
"transformed_examples": transform.outputs["transformed_examples"],
"transform_graph": transform.outputs["transform_graph"],
"schema": schema_gen.outputs["schema"],
"custom_config": ai_platform_training_args,
}
trainer = VertexTrainer(**trainer_args)
components.append(trainer)
model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")
components.append(model_resolver)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)
components.append(evaluator)
pusher_args = {
"model": trainer.outputs["model"],
"model_blessing": evaluator.outputs["blessing"],
"custom_config": ai_platform_serving_args,
}
pusher = VertexPusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part1/pipeline/local_pipeline.py | from typing import Dict, Optional, Text
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Trainer
from tfx.components import Evaluator
from tfx.components import Pusher
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import (
LatestBlessedModelResolver,
)
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
schema_path: Text,
modules: Dict[Text, Text],
eval_configs: tfma.EvalConfig,
serving_model_dir: Text,
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-00-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-00-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
components.append(example_gen)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
components.append(statistics_gen)
schema_gen = tfx.components.ImportSchemaGen(schema_file=schema_path)
components.append(schema_gen)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"],
)
components.append(example_validator)
transform_args = {
"examples": example_gen.outputs["examples"],
"schema": schema_gen.outputs["schema"],
"preprocessing_fn": modules["preprocessing_fn"],
}
transform = Transform(**transform_args)
components.append(transform)
trainer_args = {
"run_fn": modules["training_fn"],
"transformed_examples": transform.outputs["transformed_examples"],
"transform_graph": transform.outputs["transform_graph"],
"schema": schema_gen.outputs["schema"],
}
trainer = Trainer(**trainer_args)
components.append(trainer)
model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")
components.append(model_resolver)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)
components.append(evaluator)
pusher_args = {
"model": trainer.outputs["model"],
"model_blessing": evaluator.outputs["blessing"],
"push_destination": tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=serving_model_dir
)
),
}
pusher = Pusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=False,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/ViT.py | import tensorflow as tf
import keras_tuner
from transformers import TFViTForImageClassification
from .common import LABELS
from .common import PRETRAIN_CHECKPOINT
from .utils import INFO
def build_model(hparams: keras_tuner.HyperParameters):
id2label = {str(i): c for i, c in enumerate(LABELS)}
label2id = {c: str(i) for i, c in enumerate(LABELS)}
model = TFViTForImageClassification.from_pretrained(
PRETRAIN_CHECKPOINT,
num_labels=len(LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable = False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=hparams.get("learning_rate"))
model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])
INFO(model.summary())
return model
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/common.py | IMAGE_TFREC_KEY = "image"
IMAGE_SHAPE_TFREC_KEY = "image_shape"
LABEL_TFREC_KEY = "label"
MODEL_INPUT_IMAGE_KEY = "pixel_values"
MODEL_INPUT_LABEL_KEY = "labels"
IMAGE_MODEL_KEY = "pixel_values"
LABEL_MODEL_KEY = "labels"
CONCRETE_INPUT = "pixel_values"
PRETRAIN_CHECKPOINT = "google/vit-base-patch16-224-in21k"
LABELS = ["angular_leaf_spot", "bean_rust", "healthy"]
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/hyperparams.py | import keras_tuner
EPOCHS = 10
BATCH_SIZE = 32
TRAIN_BATCH_SIZE = 32
EVAL_BATCH_SIZE = 32
TRAIN_LENGTH = 1034
EVAL_LENGTH = 128
INPUT_IMG_SIZE = 224
def get_hyperparameters(hyperparameters) -> keras_tuner.HyperParameters:
hp_set = keras_tuner.HyperParameters()
for hp in hyperparameters:
hp_set.Choice(
hp, hyperparameters[hp]["values"], default=hyperparameters[hp]["default"]
)
return hp_set
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/preprocessing.py | import tensorflow as tf
from .common import IMAGE_TFREC_KEY, LABEL_TFREC_KEY
from .common import IMAGE_MODEL_KEY, LABEL_MODEL_KEY
from .hyperparams import INPUT_IMG_SIZE
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
inputs[IMAGE_TFREC_KEY] = tf.image.resize(
inputs[IMAGE_TFREC_KEY], [INPUT_IMG_SIZE, INPUT_IMG_SIZE]
)
inputs[IMAGE_TFREC_KEY] = inputs[IMAGE_TFREC_KEY] / 255.0
inputs[IMAGE_TFREC_KEY] = tf.transpose(inputs[IMAGE_TFREC_KEY], [0, 3, 1, 2])
outputs[IMAGE_MODEL_KEY] = inputs[IMAGE_TFREC_KEY]
outputs[LABEL_MODEL_KEY] = inputs[LABEL_TFREC_KEY]
return outputs
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/signatures.py | from typing import Dict
import tensorflow as tf
import tensorflow_transform as tft
from transformers import ViTFeatureExtractor
from .common import PRETRAIN_CHECKPOINT
from .common import CONCRETE_INPUT
from .common import LABEL_MODEL_KEY
feature_extractor = ViTFeatureExtractor.from_pretrained(PRETRAIN_CHECKPOINT)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {CONCRETE_INPUT: decoded_images}
def model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(shape=[None, 3, 224, 224], dtype=tf.float32, name=CONCRETE_INPUT)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
def transform_features_signature(
model: tf.keras.Model, tf_transform_output: tft.TFTransformOutput
):
"""
transform_features_signature simply returns a function that transforms
any data of the type of tf.Example which is denoted as the type of sta
ndard_artifacts.Examples in TFX. The purpose of this function is to ap
ply Transform Graph obtained from Transform component to the data prod
uced by ImportExampleGen. This function will be used in the Evaluator
component, so the raw evaluation inputs from ImportExampleGen can be a
pporiately transformed that the model could understand.
"""
# basically, what Transform component emits is a SavedModel that knows
# how to transform data. transform_features_layer() simply returns the
# layer from the Transform.
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(serialized_tf_examples):
"""
raw_feature_spec returns a set of feature maps(dict) for the input
TFRecords based on the knowledge that Transform component has lear
ned(learn doesn't mean training here). By using this information,
the raw data from ImportExampleGen could be parsed with tf.io.parse
_example utility function.
Then, it is passed to the model.tft_layer, so the final output we
get is the transformed data of the raw input.
"""
feature_spec = tf_transform_output.raw_feature_spec()
parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
transformed_features = model.tft_layer(parsed_features)
return transformed_features
return serve_tf_examples_fn
def tf_examples_serving_signature(model, tf_transform_output):
"""
tf_examples_serving_signature simply returns a function that performs
data transformation(preprocessing) and model prediction in a sequential
manner. How data transformation is done is idential to the process of
transform_features_signature function.
"""
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(
serialized_tf_example: tf.Tensor,
) -> Dict[str, tf.Tensor]:
raw_feature_spec = tf_transform_output.raw_feature_spec()
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer(raw_features)
logits = model(transformed_features).logits
return {LABEL_MODEL_KEY: logits}
return serve_tf_examples_fn
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/train.py | import keras_tuner
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import FnArgs
from .train_data import input_fn
from .ViT import build_model
from .signatures import (
model_exporter,
transform_features_signature,
tf_examples_serving_signature,
)
from .hyperparams import TRAIN_BATCH_SIZE, EVAL_BATCH_SIZE
from .hyperparams import TRAIN_LENGTH, EVAL_LENGTH
from .hyperparams import EPOCHS
from .utils import INFO
def run_fn(fn_args: FnArgs):
custom_config = fn_args.custom_config
epochs = EPOCHS
if custom_config is not None:
if "is_local" in custom_config:
epochs = 1
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=TRAIN_BATCH_SIZE,
)
eval_dataset = input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=EVAL_BATCH_SIZE,
)
hparams = keras_tuner.HyperParameters.from_config(fn_args.hyperparameters)
INFO(f"HyperParameters for training: {hparams.get_config()}")
model = build_model(hparams)
model.fit(
train_dataset,
steps_per_epoch=TRAIN_LENGTH // TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=EVAL_LENGTH // TRAIN_BATCH_SIZE,
epochs=epochs,
)
model.save(
fn_args.serving_model_dir,
save_format="tf",
signatures={
"serving_default": model_exporter(model),
"transform_features": transform_features_signature(
model, tf_transform_output
),
"from_examples": tf_examples_serving_signature(model, tf_transform_output),
},
)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/train_data.py | from typing import List
import tensorflow as tf
import tensorflow_transform as tft
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import DataAccessor
from .utils import INFO
from .common import LABEL_MODEL_KEY
from .hyperparams import BATCH_SIZE
def input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = BATCH_SIZE,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=LABEL_MODEL_KEY
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/tuning.py | import keras_tuner
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx.v1.components import TunerFnResult
from .train_data import input_fn
from .ViT import build_model
from .hyperparams import TRAIN_BATCH_SIZE, EVAL_BATCH_SIZE
from .hyperparams import TRAIN_LENGTH, EVAL_LENGTH
from .hyperparams import get_hyperparameters
def tuner_fn(fn_args: FnArgs) -> TunerFnResult:
hyperparameters = fn_args.custom_config["hyperparameters"]
tuner = keras_tuner.RandomSearch(
build_model,
max_trials=6,
hyperparameters=get_hyperparameters(hyperparameters),
allow_new_entries=False,
objective=keras_tuner.Objective("val_accuracy", "max"),
directory=fn_args.working_dir,
project_name="ViT MLOps Advanced Part2",
)
tf_transform_output = tft.TFTransformOutput(fn_args.transform_graph_path)
train_dataset = input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=TRAIN_BATCH_SIZE,
)
eval_dataset = input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=EVAL_BATCH_SIZE,
)
return TunerFnResult(
tuner=tuner,
fit_kwargs={
"x": train_dataset,
"validation_data": eval_dataset,
"steps_per_epoch": TRAIN_LENGTH // TRAIN_BATCH_SIZE,
"validation_steps": EVAL_LENGTH // EVAL_BATCH_SIZE,
},
)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/modules/utils.py | import absl
def INFO(text: str):
absl.logging.info(text)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/pipeline/configs.py | import os
import tensorflow_model_analysis as tfma
import tfx.extensions.google_cloud_ai_platform.constants as vertex_const
import tfx.extensions.google_cloud_ai_platform.trainer.executor as vertex_training_const
import tfx.extensions.google_cloud_ai_platform.tuner.executor as vertex_tuner_const
PIPELINE_NAME = "vit-e2e-pipeline-advanced-part2"
try:
import google.auth # pylint: disable=g-import-not-at-top # pytype: disable=import-error
try:
_, GOOGLE_CLOUD_PROJECT = google.auth.default()
except google.auth.exceptions.DefaultCredentialsError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
except ImportError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
GOOGLE_CLOUD_REGION = "us-central1"
GCS_BUCKET_NAME = GOOGLE_CLOUD_PROJECT + "-complete-mlops"
PIPELINE_IMAGE = f"gcr.io/{GOOGLE_CLOUD_PROJECT}/{PIPELINE_NAME}"
OUTPUT_DIR = os.path.join("gs://", GCS_BUCKET_NAME)
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", PIPELINE_NAME)
DATA_PATH = "gs://beans-lowres/tfrecords/"
SCHEMA_PATH = "pipeline/schema.pbtxt"
TRAINING_FN = "modules.train.run_fn"
TUNER_FN = "modules.tuning.tuner_fn"
PREPROCESSING_FN = "modules.preprocessing.preprocessing_fn"
EXAMPLE_GEN_BEAM_ARGS = None
TRANSFORM_BEAM_ARGS = None
TRAIN_BATCH_SIZE = 32
EVAL_BATCH_SIZE = 32
TRAIN_LENGTH = 1034
EVAL_LENGTH = 128
HYPER_PARAMETERS = {
"learning_rate": {"values": [1e-3, 1e-2, 1e-1], "default": 1e-3},
}
EVAL_CONFIGS = tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(
signature_name="from_examples",
preprocessing_function_names=["transform_features"],
label_key="labels",
prediction_key="labels",
)
],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(
metrics=[
tfma.MetricConfig(
class_name="SparseCategoricalAccuracy",
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={"value": 0.55}
),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={"value": -1e-3},
),
),
)
]
)
],
)
GCP_AI_PLATFORM_TRAINING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_training_const.TRAINING_ARGS_KEY: {
"project": GOOGLE_CLOUD_PROJECT,
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
],
},
"use_gpu": True,
}
fullres_data = os.environ.get("FULL_RES_DATA", "false")
if fullres_data.lower() == "true":
DATA_PATH = "gs://beans-fullres/tfrecords/"
DATAFLOW_SERVICE_ACCOUNT = "[email protected]"
DATAFLOW_MACHINE_TYPE = "n1-standard-4"
DATAFLOW_MAX_WORKERS = 4
DATAFLOW_DISK_SIZE_GB = 100
EXAMPLE_GEN_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
]
TRANSFORM_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
"--worker_harness_container_image=" + PIPELINE_IMAGE,
]
GCP_AI_PLATFORM_TRAINING_ARGS[vertex_training_const.TRAINING_ARGS_KEY][
"worker_pool_specs"
] = [
{
"machine_spec": {
"machine_type": "n1-standard-8",
"accelerator_type": "NVIDIA_TESLA_V100",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
]
NUM_PARALLEL_TRIALS = 3
GCP_AI_PLATFORM_TUNER_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_tuner_const.TUNING_ARGS_KEY: {
"project": GOOGLE_CLOUD_PROJECT,
"job_spec": {
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
],
},
},
vertex_tuner_const.REMOTE_TRIALS_WORKING_DIR_KEY: os.path.join(
PIPELINE_ROOT, "trials"
),
"use_gpu": True,
"hyperparameters": HYPER_PARAMETERS,
}
GCP_AI_PLATFORM_SERVING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_const.VERTEX_CONTAINER_IMAGE_URI_KEY: "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest",
vertex_const.SERVING_ARGS_KEY: {
"project_id": GOOGLE_CLOUD_PROJECT,
"deployed_model_display_name": PIPELINE_NAME.replace("-", "_"),
"endpoint_name": "prediction-" + PIPELINE_NAME.replace("-", "_"),
"traffic_split": {"0": 100},
"machine_type": "n1-standard-4",
"min_replica_count": 1,
"max_replica_count": 1,
},
}
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/pipeline/kubeflow_pipeline.py | from typing import Any, Dict, List, Optional, Text
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Evaluator
from tfx.extensions.google_cloud_ai_platform.trainer.component import (
Trainer as VertexTrainer,
)
from tfx.extensions.google_cloud_ai_platform.pusher.component import (
Pusher as VertexPusher,
)
from tfx.extensions.google_cloud_ai_platform.tuner.component import Tuner as VertexTuner
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
from tfx.proto import tuner_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import (
LatestBlessedModelResolver,
)
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
schema_path: Text,
modules: Dict[Text, Text],
eval_configs: tfma.EvalConfig,
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
ai_platform_training_args: Optional[Dict[Text, Text]] = None,
ai_platform_tuner_args: Optional[Dict[Text, Text]] = None,
tuner_args: tuner_pb2.TuneArgs = None,
ai_platform_serving_args: Optional[Dict[Text, Any]] = None,
example_gen_beam_args: Optional[List] = None,
transform_beam_args: Optional[List] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
if example_gen_beam_args is not None:
example_gen.with_beam_pipeline_args(example_gen_beam_args)
components.append(example_gen)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
components.append(statistics_gen)
schema_gen = tfx.components.ImportSchemaGen(schema_file=schema_path)
components.append(schema_gen)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"],
)
components.append(example_validator)
transform_args = {
"examples": example_gen.outputs["examples"],
"schema": schema_gen.outputs["schema"],
"preprocessing_fn": modules["preprocessing_fn"],
}
transform = Transform(**transform_args)
if transform_beam_args is not None:
transform.with_beam_pipeline_args(transform_beam_args)
components.append(transform)
tuner = VertexTuner(
tuner_fn=modules["tuner_fn"],
examples=transform.outputs["transformed_examples"],
transform_graph=transform.outputs["transform_graph"],
tune_args=tuner_args,
custom_config=ai_platform_tuner_args,
)
components.append(tuner)
trainer_args = {
"run_fn": modules["training_fn"],
"transformed_examples": transform.outputs["transformed_examples"],
"transform_graph": transform.outputs["transform_graph"],
"schema": schema_gen.outputs["schema"],
"hyperparameters": tuner.outputs["best_hyperparameters"],
"custom_config": ai_platform_training_args,
}
trainer = VertexTrainer(**trainer_args)
components.append(trainer)
model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")
components.append(model_resolver)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)
components.append(evaluator)
pusher_args = {
"model": trainer.outputs["model"],
"model_blessing": evaluator.outputs["blessing"],
"custom_config": ai_platform_serving_args,
}
pusher = VertexPusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | advanced_part2/pipeline/local_pipeline.py | from typing import Dict, Optional, Text
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Tuner
from tfx.components import Trainer
from tfx.components import Evaluator
from tfx.components import Pusher
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import (
LatestBlessedModelResolver,
)
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
schema_path: Text,
modules: Dict[Text, Text],
hyperparameters: Dict[Text, Text],
eval_configs: tfma.EvalConfig,
serving_model_dir: Text,
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-00-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-00-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
components.append(example_gen)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
components.append(statistics_gen)
schema_gen = tfx.components.ImportSchemaGen(schema_file=schema_path)
components.append(schema_gen)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"],
)
components.append(example_validator)
transform_args = {
"examples": example_gen.outputs["examples"],
"schema": schema_gen.outputs["schema"],
"preprocessing_fn": modules["preprocessing_fn"],
}
transform = Transform(**transform_args)
components.append(transform)
tuner = Tuner(
tuner_fn=modules["tuner_fn"],
examples=transform.outputs["transformed_examples"],
schema=schema_gen.outputs["schema"],
transform_graph=transform.outputs["transform_graph"],
custom_config={"hyperparameters": hyperparameters},
)
components.append(tuner)
trainer_args = {
"run_fn": modules["training_fn"],
"transformed_examples": transform.outputs["transformed_examples"],
"transform_graph": transform.outputs["transform_graph"],
"schema": schema_gen.outputs["schema"],
"hyperparameters": tuner.outputs["best_hyperparameters"],
"custom_config": {"is_local": True},
}
trainer = Trainer(**trainer_args)
components.append(trainer)
model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")
components.append(model_resolver)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)
components.append(evaluator)
pusher_args = {
"model": trainer.outputs["model"],
"model_blessing": evaluator.outputs["blessing"],
"push_destination": tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=serving_model_dir
)
),
}
pusher = Pusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=False,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/ViT.py | import tensorflow as tf
from transformers import TFViTForImageClassification
from .common import LABELS
from .common import PRETRAIN_CHECKPOINT
from .utils import INFO
def build_model():
id2label = {str(i): c for i, c in enumerate(LABELS)}
label2id = {c: str(i) for i, c in enumerate(LABELS)}
model = TFViTForImageClassification.from_pretrained(
PRETRAIN_CHECKPOINT,
num_labels=len(LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable = False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer="adam", loss=loss, metrics=["accuracy"])
INFO(model.summary())
return model
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/common.py | IMAGE_TFREC_KEY = "image"
IMAGE_SHAPE_TFREC_KEY = "image_shape"
LABEL_TFREC_KEY = "label"
IMAGE_MODEL_KEY = "pixel_values"
LABEL_MODEL_KEY = "labels"
CONCRETE_INPUT = "pixel_values"
PRETRAIN_CHECKPOINT = "google/vit-base-patch16-224-in21k"
LABELS = ["angular_leaf_spot", "bean_rust", "healthy"]
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/hyperparams.py | EPOCHS = 1
BATCH_SIZE = 32
TRAIN_BATCH_SIZE = 32
EVAL_BATCH_SIZE = 32
TRAIN_LENGTH = 1034
EVAL_LENGTH = 128
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/signatures.py | import tensorflow as tf
from transformers import ViTFeatureExtractor
from .common import PRETRAIN_CHECKPOINT
from .common import CONCRETE_INPUT
feature_extractor = ViTFeatureExtractor.from_pretrained(PRETRAIN_CHECKPOINT)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {CONCRETE_INPUT: decoded_images}
def model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(shape=[None, 3, 224, 224], dtype=tf.float32, name=CONCRETE_INPUT)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/train.py | from tfx.components.trainer.fn_args_utils import FnArgs
from .train_data import input_fn
from .ViT import build_model
from .signatures import model_exporter
from .hyperparams import TRAIN_BATCH_SIZE, EVAL_BATCH_SIZE
from .hyperparams import TRAIN_LENGTH, EVAL_LENGTH
from .hyperparams import EPOCHS
def run_fn(fn_args: FnArgs):
train_dataset = input_fn(
fn_args.train_files,
is_train=True,
batch_size=TRAIN_BATCH_SIZE,
)
eval_dataset = input_fn(
fn_args.eval_files,
is_train=False,
batch_size=EVAL_BATCH_SIZE,
)
model = build_model()
model.fit(
train_dataset,
steps_per_epoch=TRAIN_LENGTH // TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=EVAL_LENGTH // TRAIN_BATCH_SIZE,
epochs=EPOCHS,
)
model.save(
fn_args.serving_model_dir, save_format="tf", signatures=model_exporter(model)
)
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/train_data.py | from typing import List
import tensorflow as tf
from .utils import INFO
from .common import IMAGE_TFREC_KEY, IMAGE_SHAPE_TFREC_KEY, LABEL_TFREC_KEY
from .common import IMAGE_MODEL_KEY, LABEL_MODEL_KEY
from .hyperparams import BATCH_SIZE
def _parse_tfr(proto):
feature_description = {
IMAGE_TFREC_KEY: tf.io.VarLenFeature(tf.float32),
IMAGE_SHAPE_TFREC_KEY: tf.io.VarLenFeature(tf.int64),
LABEL_TFREC_KEY: tf.io.VarLenFeature(tf.int64),
}
rec = tf.io.parse_single_example(proto, feature_description)
image_shape = tf.sparse.to_dense(rec[IMAGE_SHAPE_TFREC_KEY])
image = tf.reshape(tf.sparse.to_dense(rec[IMAGE_TFREC_KEY]), image_shape)
label = tf.sparse.to_dense(rec[LABEL_TFREC_KEY])
return {IMAGE_MODEL_KEY: image, LABEL_MODEL_KEY: label}
def _preprocess(example_batch):
images = example_batch[IMAGE_MODEL_KEY]
images = tf.transpose(
images, perm=[0, 1, 2, 3]
) # (batch_size, height, width, num_channels)
images = tf.image.resize(images, (224, 224))
images = tf.transpose(images, perm=[0, 3, 1, 2])
labels = example_batch[LABEL_MODEL_KEY]
labels = tf.transpose(labels, perm=[0, 1]) # So, that TF can evaluation the shapes.
return {IMAGE_MODEL_KEY: images, LABEL_MODEL_KEY: labels}
def input_fn(
file_pattern: List[str],
batch_size: int = BATCH_SIZE,
is_train: bool = False,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = tf.data.TFRecordDataset(
tf.io.gfile.glob(file_pattern[0] + ".gz"),
num_parallel_reads=tf.data.AUTOTUNE,
compression_type="GZIP",
).map(_parse_tfr, num_parallel_calls=tf.data.AUTOTUNE)
if is_train:
dataset = dataset.shuffle(batch_size * 2)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(tf.data.AUTOTUNE)
dataset = dataset.map(_preprocess)
return dataset
|
deep-diver/mlops-hf-tf-vision-models | basic/modules/utils.py | import absl
def INFO(text: str):
absl.logging.info(text)
|
deep-diver/mlops-hf-tf-vision-models | basic/pipeline/configs.py | import os # pylint: disable=unused-import
import tfx.extensions.google_cloud_ai_platform.constants as vertex_const
import tfx.extensions.google_cloud_ai_platform.trainer.executor as vertex_training_const
PIPELINE_NAME = "vit-e2e-pipeline-basic"
try:
import google.auth # pylint: disable=g-import-not-at-top # pytype: disable=import-error
try:
_, GOOGLE_CLOUD_PROJECT = google.auth.default()
except google.auth.exceptions.DefaultCredentialsError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
except ImportError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
GOOGLE_CLOUD_REGION = "us-central1"
GCS_BUCKET_NAME = GOOGLE_CLOUD_PROJECT + "-complete-mlops"
PIPELINE_IMAGE = f"gcr.io/{GOOGLE_CLOUD_PROJECT}/{PIPELINE_NAME}"
OUTPUT_DIR = os.path.join("gs://", GCS_BUCKET_NAME)
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", PIPELINE_NAME)
DATA_PATH = "gs://beans-lowres/tfrecords/"
TRAINING_FN = "modules.train.run_fn"
EXAMPLE_GEN_BEAM_ARGS = None
GCP_AI_PLATFORM_TRAINING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_training_const.TRAINING_ARGS_KEY: {
"project": GOOGLE_CLOUD_PROJECT,
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
],
},
"use_gpu": True,
}
fullres_data = os.environ.get("FULL_RES_DATA", "false")
if fullres_data.lower() == "true":
DATA_PATH = "gs://beans-fullres/tfrecords/"
DATAFLOW_SERVICE_ACCOUNT = "[email protected]"
DATAFLOW_MACHINE_TYPE = "n1-standard-4"
DATAFLOW_MAX_WORKERS = 4
DATAFLOW_DISK_SIZE_GB = 100
EXAMPLE_GEN_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
]
GCP_AI_PLATFORM_TRAINING_ARGS[vertex_training_const.TRAINING_ARGS_KEY][
"worker_pool_specs"
] = [
{
"machine_spec": {
"machine_type": "n1-standard-8",
"accelerator_type": "NVIDIA_TESLA_V100",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
]
GCP_AI_PLATFORM_SERVING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_const.VERTEX_CONTAINER_IMAGE_URI_KEY: "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest",
vertex_const.SERVING_ARGS_KEY: {
"project_id": GOOGLE_CLOUD_PROJECT,
"deployed_model_display_name": PIPELINE_NAME.replace("-", "_"),
"endpoint_name": "prediction-" + PIPELINE_NAME.replace("-", "_"),
"traffic_split": {"0": 100},
"machine_type": "n1-standard-4",
"min_replica_count": 1,
"max_replica_count": 1,
},
}
|
deep-diver/mlops-hf-tf-vision-models | basic/pipeline/kubeflow_pipeline.py | from typing import Any, Dict, List, Optional, Text
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.extensions.google_cloud_ai_platform.trainer.component import (
Trainer as VertexTrainer,
)
from tfx.extensions.google_cloud_ai_platform.pusher.component import (
Pusher as VertexPusher,
)
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
modules: Dict[Text, Text],
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
ai_platform_training_args: Optional[Dict[Text, Text]] = None,
ai_platform_serving_args: Optional[Dict[Text, Any]] = None,
example_gen_beam_args: Optional[List] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
if example_gen_beam_args is not None:
example_gen.with_beam_pipeline_args(example_gen_beam_args)
components.append(example_gen)
trainer_args = {
"run_fn": modules["training_fn"],
"examples": example_gen.outputs["examples"],
"custom_config": ai_platform_training_args,
}
trainer = VertexTrainer(**trainer_args)
components.append(trainer)
pusher_args = {
"model": trainer.outputs["model"],
"custom_config": ai_platform_serving_args,
}
pusher = VertexPusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | basic/pipeline/local_pipeline.py | from typing import Dict, Optional, Text
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import Trainer
from tfx.components import Pusher
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
modules: Dict[Text, Text],
serving_model_dir: Text,
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-00-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-00-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
components.append(example_gen)
trainer = Trainer(
run_fn=modules["training_fn"],
examples=example_gen.outputs["examples"],
)
components.append(trainer)
pusher_args = {
"model": trainer.outputs["model"],
"push_destination": tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=serving_model_dir
)
),
}
pusher = Pusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=False,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/modules/ViT.py | import tensorflow as tf
import keras_tuner
from transformers import TFViTForImageClassification
from .common import LABELS
from .common import PRETRAIN_CHECKPOINT
from .utils import INFO
def build_model(hparams: keras_tuner.HyperParameters):
id2label = {str(i): c for i, c in enumerate(LABELS)}
label2id = {c: str(i) for i, c in enumerate(LABELS)}
model = TFViTForImageClassification.from_pretrained(
PRETRAIN_CHECKPOINT,
num_labels=len(LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable = False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(learning_rate=hparams.get("learning_rate"))
model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])
INFO(model.summary())
return model
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/modules/common.py | IMAGE_TFREC_KEY = "image"
IMAGE_SHAPE_TFREC_KEY = "image_shape"
LABEL_TFREC_KEY = "label"
MODEL_INPUT_IMAGE_KEY = "pixel_values"
MODEL_INPUT_LABEL_KEY = "labels"
IMAGE_MODEL_KEY = "pixel_values"
LABEL_MODEL_KEY = "labels"
CONCRETE_INPUT = "pixel_values"
PRETRAIN_CHECKPOINT = "google/vit-base-patch16-224-in21k"
LABELS = ["angular_leaf_spot", "bean_rust", "healthy"]
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/modules/hyperparams.py | import keras_tuner
EPOCHS = 10
BATCH_SIZE = 32
TRAIN_BATCH_SIZE = 32
EVAL_BATCH_SIZE = 32
TRAIN_LENGTH = 1034
EVAL_LENGTH = 128
INPUT_IMG_SIZE = 224
def get_hyperparameters(hyperparameters) -> keras_tuner.HyperParameters:
hp_set = keras_tuner.HyperParameters()
for hp in hyperparameters:
hp_set.Choice(
hp, hyperparameters[hp]["values"], default=hyperparameters[hp]["default"]
)
return hp_set
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/modules/preprocessing.py | import tensorflow as tf
from .common import IMAGE_TFREC_KEY, LABEL_TFREC_KEY
from .common import IMAGE_MODEL_KEY, LABEL_MODEL_KEY
from .hyperparams import INPUT_IMG_SIZE
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
inputs[IMAGE_TFREC_KEY] = tf.image.resize(
inputs[IMAGE_TFREC_KEY], [INPUT_IMG_SIZE, INPUT_IMG_SIZE]
)
inputs[IMAGE_TFREC_KEY] = inputs[IMAGE_TFREC_KEY] / 255.0
inputs[IMAGE_TFREC_KEY] = tf.transpose(inputs[IMAGE_TFREC_KEY], [0, 3, 1, 2])
outputs[IMAGE_MODEL_KEY] = inputs[IMAGE_TFREC_KEY]
outputs[LABEL_MODEL_KEY] = inputs[LABEL_TFREC_KEY]
return outputs
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/modules/signatures.py | from typing import Dict
import tensorflow as tf
import tensorflow_transform as tft
from transformers import ViTFeatureExtractor
from .common import PRETRAIN_CHECKPOINT
from .common import CONCRETE_INPUT
from .common import LABEL_MODEL_KEY
feature_extractor = ViTFeatureExtractor.from_pretrained(PRETRAIN_CHECKPOINT)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {CONCRETE_INPUT: decoded_images}
def model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(shape=[None, 3, 224, 224], dtype=tf.float32, name=CONCRETE_INPUT)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
def transform_features_signature(
model: tf.keras.Model, tf_transform_output: tft.TFTransformOutput
):
"""
transform_features_signature simply returns a function that transforms
any data of the type of tf.Example which is denoted as the type of sta
ndard_artifacts.Examples in TFX. The purpose of this function is to ap
ply Transform Graph obtained from Transform component to the data prod
uced by ImportExampleGen. This function will be used in the Evaluator
component, so the raw evaluation inputs from ImportExampleGen can be a
pporiately transformed that the model could understand.
"""
# basically, what Transform component emits is a SavedModel that knows
# how to transform data. transform_features_layer() simply returns the
# layer from the Transform.
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(serialized_tf_examples):
"""
raw_feature_spec returns a set of feature maps(dict) for the input
TFRecords based on the knowledge that Transform component has lear
ned(learn doesn't mean training here). By using this information,
the raw data from ImportExampleGen could be parsed with tf.io.parse
_example utility function.
Then, it is passed to the model.tft_layer, so the final output we
get is the transformed data of the raw input.
"""
feature_spec = tf_transform_output.raw_feature_spec()
parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
transformed_features = model.tft_layer(parsed_features)
return transformed_features
return serve_tf_examples_fn
def tf_examples_serving_signature(model, tf_transform_output):
"""
tf_examples_serving_signature simply returns a function that performs
data transformation(preprocessing) and model prediction in a sequential
manner. How data transformation is done is idential to the process of
transform_features_signature function.
"""
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(
serialized_tf_example: tf.Tensor,
) -> Dict[str, tf.Tensor]:
raw_feature_spec = tf_transform_output.raw_feature_spec()
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
transformed_features = model.tft_layer(raw_features)
logits = model(transformed_features).logits
return {LABEL_MODEL_KEY: logits}
return serve_tf_examples_fn
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/modules/train.py | import keras_tuner
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import FnArgs
from .train_data import input_fn
from .ViT import build_model
from .signatures import (
model_exporter,
transform_features_signature,
tf_examples_serving_signature,
)
from .hyperparams import TRAIN_BATCH_SIZE, EVAL_BATCH_SIZE
from .hyperparams import TRAIN_LENGTH, EVAL_LENGTH
from .hyperparams import EPOCHS
from .utils import INFO
def run_fn(fn_args: FnArgs):
custom_config = fn_args.custom_config
epochs = EPOCHS
if custom_config is not None:
if "is_local" in custom_config:
epochs = 1
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=TRAIN_BATCH_SIZE,
)
eval_dataset = input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=EVAL_BATCH_SIZE,
)
hparams = keras_tuner.HyperParameters.from_config(fn_args.hyperparameters)
INFO(f"HyperParameters for training: {hparams.get_config()}")
model = build_model(hparams)
model.fit(
train_dataset,
steps_per_epoch=TRAIN_LENGTH // TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=EVAL_LENGTH // TRAIN_BATCH_SIZE,
epochs=epochs,
)
model.save(
fn_args.serving_model_dir,
save_format="tf",
signatures={
"serving_default": model_exporter(model),
"transform_features": transform_features_signature(
model, tf_transform_output
),
"from_examples": tf_examples_serving_signature(model, tf_transform_output),
},
)
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/modules/train_data.py | from typing import List
import tensorflow as tf
import tensorflow_transform as tft
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import DataAccessor
from .utils import INFO
from .common import LABEL_MODEL_KEY
from .hyperparams import BATCH_SIZE
def input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = BATCH_SIZE,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=LABEL_MODEL_KEY
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/modules/tuning.py | import keras_tuner
import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx.v1.components import TunerFnResult
from .train_data import input_fn
from .ViT import build_model
from .hyperparams import TRAIN_BATCH_SIZE, EVAL_BATCH_SIZE
from .hyperparams import TRAIN_LENGTH, EVAL_LENGTH
from .hyperparams import get_hyperparameters
def tuner_fn(fn_args: FnArgs) -> TunerFnResult:
hyperparameters = fn_args.custom_config["hyperparameters"]
tuner = keras_tuner.RandomSearch(
build_model,
max_trials=6,
hyperparameters=get_hyperparameters(hyperparameters),
allow_new_entries=False,
objective=keras_tuner.Objective("val_accuracy", "max"),
directory=fn_args.working_dir,
project_name="ViT MLOps Advanced Part2",
)
tf_transform_output = tft.TFTransformOutput(fn_args.transform_graph_path)
train_dataset = input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=TRAIN_BATCH_SIZE,
)
eval_dataset = input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=EVAL_BATCH_SIZE,
)
return TunerFnResult(
tuner=tuner,
fit_kwargs={
"x": train_dataset,
"validation_data": eval_dataset,
"steps_per_epoch": TRAIN_LENGTH // TRAIN_BATCH_SIZE,
"validation_steps": EVAL_LENGTH // EVAL_BATCH_SIZE,
},
)
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/modules/utils.py | import absl
def INFO(text: str):
absl.logging.info(text)
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/pipeline/configs.py | import os
import tensorflow_model_analysis as tfma
import tfx.extensions.google_cloud_ai_platform.constants as vertex_const
import tfx.extensions.google_cloud_ai_platform.trainer.executor as vertex_training_const
import tfx.extensions.google_cloud_ai_platform.tuner.executor as vertex_tuner_const
PIPELINE_NAME = "vit-e2e-pipeline-hf-integration"
try:
import google.auth # pylint: disable=g-import-not-at-top # pytype: disable=import-error
try:
_, GOOGLE_CLOUD_PROJECT = google.auth.default()
except google.auth.exceptions.DefaultCredentialsError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
except ImportError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
GOOGLE_CLOUD_REGION = "us-central1"
GCS_BUCKET_NAME = GOOGLE_CLOUD_PROJECT + "-complete-mlops"
PIPELINE_IMAGE = f"gcr.io/{GOOGLE_CLOUD_PROJECT}/{PIPELINE_NAME}"
OUTPUT_DIR = os.path.join("gs://", GCS_BUCKET_NAME)
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", PIPELINE_NAME)
DATA_PATH = "gs://beans-lowres/tfrecords/"
SCHEMA_PATH = "pipeline/schema.pbtxt"
TRAINING_FN = "modules.train.run_fn"
TUNER_FN = "modules.tuning.tuner_fn"
PREPROCESSING_FN = "modules.preprocessing.preprocessing_fn"
EXAMPLE_GEN_BEAM_ARGS = None
TRANSFORM_BEAM_ARGS = None
TRAIN_BATCH_SIZE = 32
EVAL_BATCH_SIZE = 32
TRAIN_LENGTH = 1034
EVAL_LENGTH = 128
HYPER_PARAMETERS = {
"learning_rate": {"values": [1e-4, 1e-3, 1e-2, 1e-1], "default": 1e-4},
}
EVAL_CONFIGS = tfma.EvalConfig(
model_specs=[
tfma.ModelSpec(
signature_name="from_examples",
preprocessing_function_names=["transform_features"],
label_key="labels",
prediction_key="labels",
)
],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(
metrics=[
tfma.MetricConfig(
class_name="SparseCategoricalAccuracy",
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={"value": 0.55}
),
# Change threshold will be ignored if there is no
# baseline model resolved from MLMD (first run).
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={"value": -1e-3},
),
),
)
]
)
],
)
GCP_AI_PLATFORM_TRAINING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_training_const.TRAINING_ARGS_KEY: {
"project": GOOGLE_CLOUD_PROJECT,
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
],
},
"use_gpu": True,
}
fullres_data = os.environ.get("FULL_RES_DATA", "false")
if fullres_data.lower() == "true":
DATA_PATH = "gs://beans-fullres/tfrecords/"
DATAFLOW_SERVICE_ACCOUNT = "[email protected]"
DATAFLOW_MACHINE_TYPE = "n1-standard-4"
DATAFLOW_MAX_WORKERS = 4
DATAFLOW_DISK_SIZE_GB = 100
EXAMPLE_GEN_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
]
TRANSFORM_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
"--worker_harness_container_image=" + PIPELINE_IMAGE,
]
GCP_AI_PLATFORM_TRAINING_ARGS[vertex_training_const.TRAINING_ARGS_KEY][
"worker_pool_specs"
] = [
{
"machine_spec": {
"machine_type": "n1-standard-8",
"accelerator_type": "NVIDIA_TESLA_V100",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
]
NUM_PARALLEL_TRIALS = 3
GCP_AI_PLATFORM_TUNER_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_tuner_const.TUNING_ARGS_KEY: {
"project": GOOGLE_CLOUD_PROJECT,
"job_spec": {
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
],
},
},
vertex_tuner_const.REMOTE_TRIALS_WORKING_DIR_KEY: os.path.join(
PIPELINE_ROOT, "trials"
),
"use_gpu": True,
"hyperparameters": HYPER_PARAMETERS,
}
GCP_AI_PLATFORM_SERVING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_const.VERTEX_CONTAINER_IMAGE_URI_KEY: "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest",
vertex_const.SERVING_ARGS_KEY: {
"project_id": GOOGLE_CLOUD_PROJECT,
"deployed_model_display_name": PIPELINE_NAME.replace("-", "_"),
"endpoint_name": "prediction-" + PIPELINE_NAME.replace("-", "_"),
"traffic_split": {"0": 100},
"machine_type": "n1-standard-4",
"min_replica_count": 1,
"max_replica_count": 1,
},
}
HF_PUSHER_ARGS = {
"username": "chansung",
"access_token": "$HF_ACCESS_TOKEN",
"repo_name": PIPELINE_NAME,
"space_config": {
"app_path": "app.gradio",
}
}
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/pipeline/kubeflow_pipeline.py | from typing import Any, Dict, List, Optional, Text
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Evaluator
from tfx.extensions.google_cloud_ai_platform.trainer.component import (
Trainer as VertexTrainer,
)
from tfx.extensions.google_cloud_ai_platform.pusher.component import (
Pusher as VertexPusher,
)
from tfx.extensions.google_cloud_ai_platform.tuner.component import Tuner as VertexTuner
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
from tfx.proto import tuner_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import (
LatestBlessedModelResolver,
)
from pipeline.components.HFPusher.component import HFPusher
# from pipeline.components.HFPusher.component import HFSpaceConfig
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
schema_path: Text,
modules: Dict[Text, Text],
eval_configs: tfma.EvalConfig,
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
ai_platform_training_args: Optional[Dict[Text, Text]] = None,
ai_platform_tuner_args: Optional[Dict[Text, Text]] = None,
tuner_args: tuner_pb2.TuneArgs = None,
ai_platform_serving_args: Optional[Dict[Text, Any]] = None,
example_gen_beam_args: Optional[List] = None,
transform_beam_args: Optional[List] = None,
hf_pusher_args: Optional[Dict[Text, Any]] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
if example_gen_beam_args is not None:
example_gen.with_beam_pipeline_args(example_gen_beam_args)
components.append(example_gen)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
components.append(statistics_gen)
schema_gen = tfx.components.ImportSchemaGen(schema_file=schema_path)
components.append(schema_gen)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"],
)
components.append(example_validator)
transform_args = {
"examples": example_gen.outputs["examples"],
"schema": schema_gen.outputs["schema"],
"preprocessing_fn": modules["preprocessing_fn"],
}
transform = Transform(**transform_args)
if transform_beam_args is not None:
transform.with_beam_pipeline_args(transform_beam_args)
components.append(transform)
tuner = VertexTuner(
tuner_fn=modules["tuner_fn"],
examples=transform.outputs["transformed_examples"],
transform_graph=transform.outputs["transform_graph"],
tune_args=tuner_args,
custom_config=ai_platform_tuner_args,
)
components.append(tuner)
trainer_args = {
"run_fn": modules["training_fn"],
"transformed_examples": transform.outputs["transformed_examples"],
"transform_graph": transform.outputs["transform_graph"],
"schema": schema_gen.outputs["schema"],
"hyperparameters": tuner.outputs["best_hyperparameters"],
"custom_config": ai_platform_training_args,
}
trainer = VertexTrainer(**trainer_args)
components.append(trainer)
model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")
components.append(model_resolver)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)
components.append(evaluator)
pusher_args = {
"model": trainer.outputs["model"],
"model_blessing": evaluator.outputs["blessing"],
"custom_config": ai_platform_serving_args,
}
pusher = VertexPusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
hf_pusher_args['model'] = trainer.outputs["model"]
hf_pusher_args['model_blessing'] = evaluator.outputs["blessing"]
hf_pusher = HFPusher(**hf_pusher_args)
components.append(hf_pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/pipeline/local_pipeline.py | from typing import Dict, Optional, Text
import tensorflow_model_analysis as tfma
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Tuner
from tfx.components import Trainer
from tfx.components import Evaluator
from tfx.components import Pusher
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import (
LatestBlessedModelResolver,
)
from pipeline.components.HFPusher.component import HFPusher
# from pipeline.components.HFPusher.component import HFSpaceConfig
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
schema_path: Text,
modules: Dict[Text, Text],
hyperparameters: Dict[Text, Text],
eval_configs: tfma.EvalConfig,
serving_model_dir: Text,
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-00-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-00-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
components.append(example_gen)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
components.append(statistics_gen)
schema_gen = tfx.components.ImportSchemaGen(schema_file=schema_path)
components.append(schema_gen)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"],
)
components.append(example_validator)
transform_args = {
"examples": example_gen.outputs["examples"],
"schema": schema_gen.outputs["schema"],
"preprocessing_fn": modules["preprocessing_fn"],
}
transform = Transform(**transform_args)
components.append(transform)
tuner = Tuner(
tuner_fn=modules["tuner_fn"],
examples=transform.outputs["transformed_examples"],
schema=schema_gen.outputs["schema"],
transform_graph=transform.outputs["transform_graph"],
custom_config={"hyperparameters": hyperparameters},
)
components.append(tuner)
trainer_args = {
"run_fn": modules["training_fn"],
"transformed_examples": transform.outputs["transformed_examples"],
"transform_graph": transform.outputs["transform_graph"],
"schema": schema_gen.outputs["schema"],
"hyperparameters": tuner.outputs["best_hyperparameters"],
"custom_config": {"is_local": True},
}
trainer = Trainer(**trainer_args)
components.append(trainer)
model_resolver = resolver.Resolver(
strategy_class=LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing),
).with_id("latest_blessed_model_resolver")
components.append(model_resolver)
evaluator = Evaluator(
examples=example_gen.outputs["examples"],
model=trainer.outputs["model"],
baseline_model=model_resolver.outputs["model"],
eval_config=eval_configs,
)
components.append(evaluator)
pusher_args = {
"model": trainer.outputs["model"],
"model_blessing": evaluator.outputs["blessing"],
"push_destination": tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=serving_model_dir
)
),
}
pusher = Pusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
# pusher_args = {
# "model": trainer.outputs["model"],
# "model_blessing": evaluator.outputs["blessing"],
# "username": "chansung",
# "access_token": "<REPLACE_WITH_YOUR_OWN_TOKEN>",
# "repo_name": "vit-e2e-pipeline-hf-integration",
# }
# hf_pusher = HFPusher(**pusher_args)
# components.append(hf_pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=False,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | intermediate/modules/ViT.py | import tensorflow as tf
from transformers import TFViTForImageClassification
from .common import LABELS
from .common import PRETRAIN_CHECKPOINT
from .utils import INFO
def build_model():
id2label = {str(i): c for i, c in enumerate(LABELS)}
label2id = {c: str(i) for i, c in enumerate(LABELS)}
model = TFViTForImageClassification.from_pretrained(
PRETRAIN_CHECKPOINT,
num_labels=len(LABELS),
label2id=label2id,
id2label=id2label,
)
model.layers[0].trainable = False
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer="adam", loss=loss, metrics=["accuracy"])
INFO(model.summary())
return model
|
deep-diver/mlops-hf-tf-vision-models | intermediate/modules/common.py | IMAGE_TFREC_KEY = "image"
IMAGE_SHAPE_TFREC_KEY = "image_shape"
LABEL_TFREC_KEY = "label"
MODEL_INPUT_IMAGE_KEY = "pixel_values"
MODEL_INPUT_LABEL_KEY = "labels"
IMAGE_MODEL_KEY = "pixel_values"
LABEL_MODEL_KEY = "labels"
CONCRETE_INPUT = "pixel_values"
PRETRAIN_CHECKPOINT = "google/vit-base-patch16-224-in21k"
LABELS = ["angular_leaf_spot", "bean_rust", "healthy"]
|
deep-diver/mlops-hf-tf-vision-models | intermediate/modules/hyperparams.py | EPOCHS = 1
BATCH_SIZE = 32
TRAIN_BATCH_SIZE = 32
EVAL_BATCH_SIZE = 32
TRAIN_LENGTH = 1034
EVAL_LENGTH = 128
INPUT_IMG_SIZE = 224
|
deep-diver/mlops-hf-tf-vision-models | intermediate/modules/preprocessing.py | import tensorflow as tf
from .common import IMAGE_TFREC_KEY, LABEL_TFREC_KEY
from .common import IMAGE_MODEL_KEY, LABEL_MODEL_KEY
from .hyperparams import INPUT_IMG_SIZE
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
inputs[IMAGE_TFREC_KEY] = tf.image.resize(
inputs[IMAGE_TFREC_KEY], [INPUT_IMG_SIZE, INPUT_IMG_SIZE]
)
inputs[IMAGE_TFREC_KEY] = inputs[IMAGE_TFREC_KEY] / 255.0
inputs[IMAGE_TFREC_KEY] = tf.transpose(inputs[IMAGE_TFREC_KEY], [0, 3, 1, 2])
outputs[IMAGE_MODEL_KEY] = inputs[IMAGE_TFREC_KEY]
outputs[LABEL_MODEL_KEY] = inputs[LABEL_TFREC_KEY]
return outputs
|
deep-diver/mlops-hf-tf-vision-models | intermediate/modules/signatures.py | import tensorflow as tf
from transformers import ViTFeatureExtractor
from .common import PRETRAIN_CHECKPOINT
from .common import CONCRETE_INPUT
feature_extractor = ViTFeatureExtractor.from_pretrained(PRETRAIN_CHECKPOINT)
def _normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def _preprocess_serving(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
resized = tf.image.resize(decoded, size=(224, 224))
normalized = _normalize_img(resized)
normalized = tf.transpose(
normalized, (2, 0, 1)
) # Since HF models are channel-first.
return normalized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _preprocess_fn(string_input):
decoded_images = tf.map_fn(
_preprocess_serving, string_input, dtype=tf.float32, back_prop=False
)
return {CONCRETE_INPUT: decoded_images}
def model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(shape=[None, 3, 224, 224], dtype=tf.float32, name=CONCRETE_INPUT)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
labels = tf.constant(list(model.config.id2label.values()), dtype=tf.string)
images = _preprocess_fn(string_input)
predictions = m_call(**images)
indices = tf.argmax(predictions.logits, axis=1)
pred_source = tf.gather(params=labels, indices=indices)
probs = tf.nn.softmax(predictions.logits, axis=1)
pred_confidence = tf.reduce_max(probs, axis=1)
return {"label": pred_source, "confidence": pred_confidence}
return serving_fn
|
deep-diver/mlops-hf-tf-vision-models | intermediate/modules/train.py | import tensorflow_transform as tft
from tfx.components.trainer.fn_args_utils import FnArgs
from .train_data import input_fn
from .ViT import build_model
from .signatures import model_exporter
from .hyperparams import TRAIN_BATCH_SIZE, EVAL_BATCH_SIZE
from .hyperparams import TRAIN_LENGTH, EVAL_LENGTH
from .hyperparams import EPOCHS
def run_fn(fn_args: FnArgs):
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=TRAIN_BATCH_SIZE,
)
eval_dataset = input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=EVAL_BATCH_SIZE,
)
model = build_model()
model.fit(
train_dataset,
steps_per_epoch=TRAIN_LENGTH // TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=EVAL_LENGTH // TRAIN_BATCH_SIZE,
epochs=EPOCHS,
)
model.save(
fn_args.serving_model_dir, save_format="tf", signatures=model_exporter(model)
)
|
deep-diver/mlops-hf-tf-vision-models | intermediate/modules/train_data.py | from typing import List
import tensorflow as tf
import tensorflow_transform as tft
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import DataAccessor
from .utils import INFO
from .common import LABEL_MODEL_KEY
from .hyperparams import BATCH_SIZE
def input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = BATCH_SIZE,
) -> tf.data.Dataset:
INFO(f"Reading data from: {file_pattern}")
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=LABEL_MODEL_KEY
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
|
deep-diver/mlops-hf-tf-vision-models | intermediate/modules/utils.py | import absl
def INFO(text: str):
absl.logging.info(text)
|
deep-diver/mlops-hf-tf-vision-models | intermediate/pipeline/configs.py | import os # pylint: disable=unused-import
import tfx.extensions.google_cloud_ai_platform.constants as vertex_const
import tfx.extensions.google_cloud_ai_platform.trainer.executor as vertex_training_const
PIPELINE_NAME = "vit-e2e-pipeline-intermediate"
try:
import google.auth # pylint: disable=g-import-not-at-top # pytype: disable=import-error
try:
_, GOOGLE_CLOUD_PROJECT = google.auth.default()
except google.auth.exceptions.DefaultCredentialsError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
except ImportError:
GOOGLE_CLOUD_PROJECT = "gcp-ml-172005"
GOOGLE_CLOUD_REGION = "us-central1"
GCS_BUCKET_NAME = GOOGLE_CLOUD_PROJECT + "-complete-mlops"
PIPELINE_IMAGE = f"gcr.io/{GOOGLE_CLOUD_PROJECT}/{PIPELINE_NAME}"
OUTPUT_DIR = os.path.join("gs://", GCS_BUCKET_NAME)
PIPELINE_ROOT = os.path.join(OUTPUT_DIR, "tfx_pipeline_output", PIPELINE_NAME)
DATA_PATH = "gs://beans-lowres/tfrecords/"
SCHEMA_PATH = "pipeline/schema.pbtxt"
TRAINING_FN = "modules.train.run_fn"
PREPROCESSING_FN = "modules.preprocessing.preprocessing_fn"
EXAMPLE_GEN_BEAM_ARGS = None
TRANSFORM_BEAM_ARGS = None
GCP_AI_PLATFORM_TRAINING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_training_const.TRAINING_ARGS_KEY: {
"project": GOOGLE_CLOUD_PROJECT,
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": "NVIDIA_TESLA_K80",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
],
},
"use_gpu": True,
}
fullres_data = os.environ.get("FULL_RES_DATA", "false")
if fullres_data.lower() == "true":
DATA_PATH = "gs://beans-fullres/tfrecords/"
DATAFLOW_SERVICE_ACCOUNT = "[email protected]"
DATAFLOW_MACHINE_TYPE = "n1-standard-4"
DATAFLOW_MAX_WORKERS = 4
DATAFLOW_DISK_SIZE_GB = 100
EXAMPLE_GEN_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
]
TRANSFORM_BEAM_ARGS = [
"--runner=DataflowRunner",
"--project=" + GOOGLE_CLOUD_PROJECT,
"--region=" + GOOGLE_CLOUD_REGION,
"--service_account_email=" + DATAFLOW_SERVICE_ACCOUNT,
"--machine_type=" + DATAFLOW_MACHINE_TYPE,
"--experiments=use_runner_v2",
"--max_num_workers=" + str(DATAFLOW_MAX_WORKERS),
"--disk_size_gb=" + str(DATAFLOW_DISK_SIZE_GB),
"--worker_harness_container_image=" + PIPELINE_IMAGE,
]
GCP_AI_PLATFORM_TRAINING_ARGS[vertex_training_const.TRAINING_ARGS_KEY][
"worker_pool_specs"
] = [
{
"machine_spec": {
"machine_type": "n1-standard-8",
"accelerator_type": "NVIDIA_TESLA_V100",
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": PIPELINE_IMAGE,
},
}
]
GCP_AI_PLATFORM_SERVING_ARGS = {
vertex_const.ENABLE_VERTEX_KEY: True,
vertex_const.VERTEX_REGION_KEY: GOOGLE_CLOUD_REGION,
vertex_const.VERTEX_CONTAINER_IMAGE_URI_KEY: "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest",
vertex_const.SERVING_ARGS_KEY: {
"project_id": GOOGLE_CLOUD_PROJECT,
"deployed_model_display_name": PIPELINE_NAME.replace("-", "_"),
"endpoint_name": "prediction-" + PIPELINE_NAME.replace("-", "_"),
"traffic_split": {"0": 100},
"machine_type": "n1-standard-4",
"min_replica_count": 1,
"max_replica_count": 1,
},
}
|
deep-diver/mlops-hf-tf-vision-models | intermediate/pipeline/kubeflow_pipeline.py | from typing import Any, Dict, List, Optional, Text
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.extensions.google_cloud_ai_platform.trainer.component import (
Trainer as VertexTrainer,
)
from tfx.extensions.google_cloud_ai_platform.pusher.component import (
Pusher as VertexPusher,
)
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
schema_path: Text,
modules: Dict[Text, Text],
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
ai_platform_training_args: Optional[Dict[Text, Text]] = None,
ai_platform_serving_args: Optional[Dict[Text, Any]] = None,
example_gen_beam_args: Optional[List] = None,
transform_beam_args: Optional[List] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
if example_gen_beam_args is not None:
example_gen.with_beam_pipeline_args(example_gen_beam_args)
components.append(example_gen)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
components.append(statistics_gen)
schema_gen = tfx.components.ImportSchemaGen(schema_file=schema_path)
components.append(schema_gen)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"],
)
components.append(example_validator)
transform_args = {
"examples": example_gen.outputs["examples"],
"schema": schema_gen.outputs["schema"],
"preprocessing_fn": modules["preprocessing_fn"],
}
transform = Transform(**transform_args)
if transform_beam_args is not None:
transform.with_beam_pipeline_args(transform_beam_args)
components.append(transform)
trainer_args = {
"run_fn": modules["training_fn"],
"transformed_examples": transform.outputs["transformed_examples"],
"transform_graph": transform.outputs["transform_graph"],
"schema": schema_gen.outputs["schema"],
"custom_config": ai_platform_training_args,
}
trainer = VertexTrainer(**trainer_args)
components.append(trainer)
pusher_args = {
"model": trainer.outputs["model"],
"custom_config": ai_platform_serving_args,
}
pusher = VertexPusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | intermediate/pipeline/local_pipeline.py | from typing import Dict, Optional, Text
from tfx import v1 as tfx
from ml_metadata.proto import metadata_store_pb2
from tfx.proto import example_gen_pb2
from tfx.components import ImportExampleGen
from tfx.components import StatisticsGen
from tfx.components import ExampleValidator
from tfx.components import Transform
from tfx.components import Trainer
from tfx.components import Pusher
from tfx.orchestration import pipeline
from tfx.proto import example_gen_pb2
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_path: Text,
schema_path: Text,
modules: Dict[Text, Text],
serving_model_dir: Text,
metadata_connection_config: Optional[metadata_store_pb2.ConnectionConfig] = None,
) -> tfx.dsl.Pipeline:
components = []
input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-00-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-00-*.tfrec"),
]
)
example_gen = ImportExampleGen(input_base=data_path, input_config=input_config)
components.append(example_gen)
statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])
components.append(statistics_gen)
schema_gen = tfx.components.ImportSchemaGen(schema_file=schema_path)
components.append(schema_gen)
example_validator = ExampleValidator(
statistics=statistics_gen.outputs["statistics"],
schema=schema_gen.outputs["schema"],
)
components.append(example_validator)
transform_args = {
"examples": example_gen.outputs["examples"],
"schema": schema_gen.outputs["schema"],
"preprocessing_fn": modules["preprocessing_fn"],
}
transform = Transform(**transform_args)
components.append(transform)
trainer_args = {
"run_fn": modules["training_fn"],
"transformed_examples": transform.outputs["transformed_examples"],
"transform_graph": transform.outputs["transform_graph"],
"schema": schema_gen.outputs["schema"],
}
trainer = Trainer(**trainer_args)
components.append(trainer)
pusher_args = {
"model": trainer.outputs["model"],
"push_destination": tfx.proto.PushDestination(
filesystem=tfx.proto.PushDestination.Filesystem(
base_directory=serving_model_dir
)
),
}
pusher = Pusher(**pusher_args) # pylint: disable=unused-variable
components.append(pusher)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=False,
metadata_connection_config=metadata_connection_config,
)
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/app/gradio/app.py | import gradio as gr
import numpy as np
from PIL import Image
import tensorflow as tf
from transformers import ViTFeatureExtractor
from huggingface_hub import from_pretrained_keras
PRETRAIN_CHECKPOINT = "google/vit-base-patch16-224-in21k"
feature_extractor = ViTFeatureExtractor.from_pretrained(PRETRAIN_CHECKPOINT)
# $MODEL_REPO_ID should be like chansung/test-vit
# $MODEL_VERSION
MODEL_CKPT = "$MODEL_REPO_ID@$MODEL_VERSION"
MODEL = from_pretrained_keras(MODEL_CKPT)
RESOLTUION = 224
labels = []
with open(r"labels.txt", "r") as fp:
for line in fp:
labels.append(line[:-1])
def normalize_img(
img, mean=feature_extractor.image_mean, std=feature_extractor.image_std
):
img = img / 255
mean = tf.constant(mean)
std = tf.constant(std)
return (img - mean) / std
def preprocess_input(image: Image) -> tf.Tensor:
image = np.array(image)
image = tf.convert_to_tensor(image)
image = tf.image.resize(image, (RESOLTUION, RESOLTUION))
image = normalize_img(image)
image = tf.transpose(
image, (2, 0, 1)
) # Since HF models are channel-first.
return {
"pixel_values": tf.expand_dims(image, 0)
}
def get_predictions(image: Image) -> tf.Tensor:
preprocessed_image = preprocess_input(image)
prediction = MODEL.predict(preprocessed_image)
probs = tf.nn.softmax(prediction['logits'], axis=1)
confidences = {labels[i]: float(probs[0][i]) for i in range(3)}
return confidences
title = "Simple demo for a Image Classification of the Beans Dataset with HF ViT model"
demo = gr.Interface(
get_predictions,
gr.inputs.Image(type="pil"),
gr.outputs.Label(num_top_classes=3),
allow_flagging="never",
title=title,
)
demo.launch(debug=True)
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/pipeline/components/HFPusher/__init__.py | # Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/pipeline/components/HFPusher/component.py | # Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""HuggingFace(HF) Pusher TFX Component.
The HFPusher is used to push model and prototype application to HuggingFace Hub.
"""
from typing import Text, Dict, Any, Optional
from tfx import types
from tfx.dsl.components.base import base_component, executor_spec
from tfx.types import standard_artifacts
from tfx.types.component_spec import ChannelParameter, ExecutionParameter
from pipeline.components.HFPusher import executor
MODEL_KEY = "model"
PUSHED_MODEL_KEY = "pushed_model"
MODEL_BLESSING_KEY = "model_blessing"
class HFPusherSpec(types.ComponentSpec):
"""ComponentSpec for TFX HFPusher Component."""
PARAMETERS = {
"username": ExecutionParameter(type=str),
"access_token": ExecutionParameter(type=str),
"repo_name": ExecutionParameter(type=str),
"space_config": ExecutionParameter(type=Dict[Text, Any], optional=True),
}
INPUTS = {
MODEL_KEY: ChannelParameter(type=standard_artifacts.Model, optional=True),
MODEL_BLESSING_KEY: ChannelParameter(
type=standard_artifacts.ModelBlessing, optional=True
),
}
OUTPUTS = {
PUSHED_MODEL_KEY: ChannelParameter(type=standard_artifacts.PushedModel),
}
class HFPusher(base_component.BaseComponent):
"""Component for pushing model and application to HuggingFace Hub.
The `HFPusher` is a [TFX Component](https://www.tensorflow.org/tfx
/guide/understanding_tfx_pipelines#component), and its primary pur
pose is to push a model from an upstream component such as [`Train
er`](https://www.tensorflow.org/tfx/guide/trainer) to HuggingFace
Model Hub. It also provides a secondary feature that pushes an app
lication to HuggingFace Space Hub.
"""
SPEC_CLASS = HFPusherSpec
EXECUTOR_SPEC = executor_spec.ExecutorClassSpec(executor.Executor)
def __init__(
self,
username: str,
access_token: str,
repo_name: str,
space_config: Optional[Dict[Text, Any]] = None,
model: Optional[types.Channel] = None,
model_blessing: Optional[types.Channel] = None,
):
"""The HFPusher TFX component.
HFPusher pushes a trained or blessed model to HuggingFace Model Hub.
This is designed to work as a downstream component of Trainer and o
ptionally Evaluator(optional) components. Trainer gives trained mod
el, and Evaluator gives information whether the trained model is bl
essed or not after evaluation of the model. HFPusher component only
publishes a model when it is blessed. If Evaluator is not specified,
the input model will always be pushed.
Args:
username: the ID of HuggingFace Hub
access_token: the access token obtained from HuggingFace Hub for the
given username. Refer to [this document](https://huggingface.co./
docs/hub/security-tokens) to know how to obtain one.
repo_name: the name of Model Hub repository where the model will be
pushed. This should be unique name under the username within th
e Model Hub. repository is identified as {username}/{repo_name}.
space_config: optional configurations set when to push an application
to HuggingFace Space Hub. This is a dictionary, and the following
information could be set.
app_path: the path where the application related files are stored.
this should follow the form either of app.gradio.segmentation
or app/gradio/segmentation. This is a required parameter when
space_config is set. This could be a local or GCS paths.
space_sdk: Space Hub supports gradio, streamit, and static types
of application. The default is set to gradio.
placeholders: placeholders to replace in every files under the a
pp_path. This is used to replace special string with the mod
el related values. If this is not set, the default placehold
ers will be used as follows.
```
placeholders = {
"MODEL_REPO_ID" : "$MODEL_REPO_ID",
"MODEL_REPO_URL": "$MODEL_REPO_URL",
"MODEL_VERSION" : "$MODEL_VERSION",
}
```
In this case, "$MODEL_REPO_ID", "$MODEL_REPO_URL", "$MODEL_VE
RSION" strings will be replaced with appropriate values at ru
ntime. If placeholders are set, custom strings will be used.
repo_name: the name of Space Hub repository where the application
will be pushed. This should be unique name under the username
within the Space Hub. repository is identified as {username}/
{repo_name}. If this is not set, the same name to the Model H
ub repository will be used.
model: a TFX input channel containing a Model artifact. this is usually
comes from the standard [`Trainer`]
(https://www.tensorflow.org/tfx/guide/trainer) component.
model_blessing: a TFX input channel containing a ModelBlessing artifact.
this is usually comes from the standard [`Evaluator`]
(https://www.tensorflow.org/tfx/guide/evaluator) component.
Returns:
a TFX output channel containing a PushedModel artifact. It contains
information where the model is published at and whether the model is
pushed or not.
Raises:
RuntimeError: if app_path is not set when space_config is provided.
Example:
Basic usage example:
```py
trainer = Trainer(...)
evaluator = Evaluator(...)
hf_pusher = HFPusher(
username="chansung",
access_token=<YOUR-HUGGINGFACE-ACCESS-TOKEN>,
repo_name="my-model",
model=trainer.outputs["model"],
model_blessing=evaluator.outputs["blessing"],
space_config={
"app_path": "apps.gradio.semantic_segmentation"
}
)
```
"""
pushed_model = types.Channel(type=standard_artifacts.PushedModel)
spec = HFPusherSpec(
username=username,
access_token=access_token,
repo_name=repo_name,
space_config=space_config,
model=model,
model_blessing=model_blessing,
pushed_model=pushed_model,
)
super().__init__(spec=spec)
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/pipeline/components/HFPusher/component_test.py | # Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for TFX HuggingFace Pusher Custom Component."""
import tensorflow as tf
from tfx.types import standard_artifacts
from tfx.types import channel_utils
from pipeline.components.HFPusher.component import HFPusher
class HFPusherTest(tf.test.TestCase):
def testConstruct(self):
test_model = channel_utils.as_channel([standard_artifacts.Model()])
hf_pusher = HFPusher(
username="test_username",
access_token="test_access_token",
repo_name="test_repo_name",
model=test_model,
)
self.assertEqual(
standard_artifacts.PushedModel.TYPE_NAME,
hf_pusher.outputs["pushed_model"].type_name,
)
if __name__ == "__main__":
tf.test.main()
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/pipeline/components/HFPusher/executor.py | # Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""HF Pusher TFX Component Executor. The HF Pusher Executor calls
the workflow handler runner.deploy_model_for_hf_hub().
"""
import ast
import time
from typing import Any, Dict, List
from tfx import types
from tfx.components.pusher import executor as tfx_pusher_executor
from tfx.types import artifact_utils, standard_component_specs
from pipeline.components.HFPusher import runner
_USERNAME_KEY = "username"
_ACCESS_TOKEN_KEY = "access_token"
_REPO_NAME_KEY = "repo_name"
_SPACE_CONFIG_KEY = "space_config"
class Executor(tfx_pusher_executor.Executor):
"""Pushes a model and an app to HuggingFace Model and Space Hubs respectively"""
def Do(
self,
input_dict: Dict[str, List[types.Artifact]],
output_dict: Dict[str, List[types.Artifact]],
exec_properties: Dict[str, Any],
):
"""Overrides the tfx_pusher_executor to leverage some of utility methods
Args:
input_dict: Input dict from input key to a list of artifacts, including:
- model_export: a TFX input channel containing a Model artifact.
- model_blessing: a TFX input channel containing a ModelBlessing
artifact.
output_dict: Output dict from key to a list of artifacts, including:
- pushed_model: a TFX output channel containing a PushedModel arti
fact. It contains information where the model is published at an
d whether the model is pushed or not. furthermore, pushed model
carries the following information.
- pushed : integer value to denote if the model is pushed or not.
This is set to 0 when the input model is not blessed, and it is
set to 1 when the model is successfully pushed.
- pushed_version : string value to indicate the current model ver
sion. This is decided by time.time() Python built-in function.
- repo_id : model repository ID where the model is pushed to. This
follows the format of f"{username}/{repo_name}".
- branch : branch name where the model is pushed to. The branch na
me is automatically assigned to the same value of pushed_version.
- commit_id : the id from the commit history (branch name could be
sufficient to retreive a certain version of the model) of the mo
del repository.
- repo_url : model repository URL. It is something like f"https://
huggingface.co/{repo_id}/{branch}"
- space_url : space repository URL. It is something like f"https://
huggingface.co/{repo_id}"f
exec_properties: An optional dict of execution properties, including:
- username: username of the HuggingFace user (can be an individual
user or an organization)
- access_token: access token value issued by HuggingFace for the s
pecified username.
- repo_name: the repository name to push the current version of the
model to. The default value is same as the TFX pipeline name.
- space_config: space_config carries additional values such as:
- app_path : path where the application templates are in the cont
ainer that runs the TFX pipeline. This is expressed either apps.
gradio.img_classifier or apps/gradio.img_classifier.
- repo_name : the repository name to push the application to. The
default value is same as the TFX pipeline name
- space_sdk : either gradio or streamlit. this will decide which a
pplication framework to be used for the Space repository. The de
fault value is gradio
- placeholders : dictionary which placeholders to replace with mod
el specific information. The keys represents describtions, and t
he values represents the actual placeholders to replace in the f
iles under the app_path. There are currently two predefined keys,
and if placeholders is set to None, the default values will be used.
"""
self._log_startup(input_dict, output_dict, exec_properties)
model_push = artifact_utils.get_single_instance(
output_dict[standard_component_specs.PUSHED_MODEL_KEY]
)
# if the model is not blessed
if not self.CheckBlessing(input_dict):
self._MarkNotPushed(model_push)
return
model_path = self.GetModelPath(input_dict)
model_version_name = f"v{int(time.time())}"
space_config = exec_properties.get(_SPACE_CONFIG_KEY, None)
if space_config is not None:
space_config = ast.literal_eval(space_config)
pushed_properties = runner.deploy_model_for_hf_hub(
username=exec_properties.get(_USERNAME_KEY, None),
access_token=exec_properties.get(_ACCESS_TOKEN_KEY, None),
repo_name=exec_properties.get(_REPO_NAME_KEY, None),
space_config=space_config,
model_path=model_path,
model_version=model_version_name,
)
self._MarkPushed(model_push, pushed_destination=pushed_properties["repo_url"])
for key in pushed_properties:
value = pushed_properties[key]
if key != "repo_url":
model_push.set_string_custom_property(key, value)
|
deep-diver/mlops-hf-tf-vision-models | hf_integration/pipeline/components/HFPusher/runner.py | # Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""HuggingFace Pusher runner module.
This module handles the workflow to publish
machine learning model to HuggingFace Hub.
"""
from typing import Text, Any, Dict, Optional
import tempfile
import tensorflow as tf
from absl import logging
from tfx.utils import io_utils
from huggingface_hub import Repository
from huggingface_hub import HfApi
from requests.exceptions import HTTPError
_MODEL_REPO_KEY = "MODEL_REPO_ID"
_MODEL_URL_KEY = "MODEL_REPO_URL"
_MODEL_VERSION_KEY = "MODEL_VERSION"
_DEFAULT_MODEL_REPO_PLACEHOLDER_KEY = "$MODEL_REPO_ID"
_DEFAULT_MODEL_URL_PLACEHOLDER_KEY = "$MODEL_REPO_URL"
_DEFAULT_MODEL_VERSION_PLACEHOLDER_KEY = "$MODEL_VERSION"
def _replace_placeholders_in_files(
root_dir: str, placeholder_to_replace: Dict[str, str]
):
"""Recursively open every files under the root_dir, and then
replace special tokens with the given values in placeholder_
to_replace"""
files = tf.io.gfile.listdir(root_dir)
for file in files:
path = tf.io.gfile.join(root_dir, file)
if tf.io.gfile.isdir(path):
_replace_placeholders_in_files(path, placeholder_to_replace)
else:
_replace_placeholders_in_file(path, placeholder_to_replace)
def _replace_placeholders_in_file(
filepath: str, placeholder_to_replace: Dict[str, str]
):
"""replace special tokens with the given values in placeholder_
to_replace. This function gets called by _replace_placeholders
_in_files function"""
with tf.io.gfile.GFile(filepath, "r") as f:
source_code = f.read()
for placeholder in placeholder_to_replace:
source_code = source_code.replace(
placeholder, placeholder_to_replace[placeholder]
)
with tf.io.gfile.GFile(filepath, "w") as f:
f.write(source_code)
def _replace_placeholders(
target_dir: str,
placeholders: Dict[str, str],
model_repo_id: str,
model_repo_url: str,
model_version: str,
):
"""set placeholder_to_replace before calling _replace_placeholde
rs_in_files function"""
if placeholders is None:
placeholders = {
_MODEL_REPO_KEY: _DEFAULT_MODEL_REPO_PLACEHOLDER_KEY,
_MODEL_URL_KEY: _DEFAULT_MODEL_URL_PLACEHOLDER_KEY,
_MODEL_VERSION_KEY: _DEFAULT_MODEL_VERSION_PLACEHOLDER_KEY,
}
placeholder_to_replace = {
placeholders[_MODEL_REPO_KEY]: model_repo_id,
placeholders[_MODEL_URL_KEY]: model_repo_url,
placeholders[_MODEL_VERSION_KEY]: model_version,
}
_replace_placeholders_in_files(target_dir, placeholder_to_replace)
def _replace_files(src_path, dst_path):
"""replace the contents(files/folders) of the repository with the
latest contents"""
not_to_delete = [".gitattributes", ".git"]
inside_root_dst_path = tf.io.gfile.listdir(dst_path)
for content_name in inside_root_dst_path:
content = f"{dst_path}/{content_name}"
if content_name not in not_to_delete:
if tf.io.gfile.isdir(content):
tf.io.gfile.rmtree(content)
else:
tf.io.gfile.remove(content)
inside_root_src_path = tf.io.gfile.listdir(src_path)
for content_name in inside_root_src_path:
content = f"{src_path}/{content_name}"
dst_content = f"{dst_path}/{content_name}"
if tf.io.gfile.isdir(content):
io_utils.copy_dir(content, dst_content)
else:
tf.io.gfile.copy(content, dst_content)
def _create_remote_repo(
access_token: str, repo_id: str, repo_type: str = "model", space_sdk: str = None
):
"""create a remote repository on HuggingFace Hub platform. HTTPError
exception is raised when the repository already exists"""
logging.info(f"repo_id: {repo_id}")
try:
HfApi().create_repo(
token=access_token,
repo_id=repo_id,
repo_type=repo_type,
space_sdk=space_sdk,
)
except HTTPError:
logging.warning(
f"this warning is expected if {repo_id} repository already exists"
)
def _clone_and_checkout(
repo_url: str, local_path: str, access_token: str, version: Optional[str] = None
) -> Repository:
"""clone the remote repository to the given local_path"""
repository = Repository(
local_dir=local_path, clone_from=repo_url, use_auth_token=access_token
)
if version is not None:
repository.git_checkout(revision=version, create_branch_ok=True)
return repository
def _push_to_remote_repo(repo: Repository, commit_msg: str, branch: str = "main"):
"""push any changes to the remote repository"""
repo.git_add(pattern=".", auto_lfs_track=True)
repo.git_commit(commit_message=commit_msg)
repo.git_push(upstream=f"origin {branch}")
def deploy_model_for_hf_hub(
username: str,
access_token: str,
repo_name: str,
model_path: str,
model_version: str,
space_config: Optional[Dict[Text, Any]] = None,
) -> Dict[str, str]:
"""Executes ML model deployment workflow to HuggingFace Hub. Refer to the
HFPusher component in component.py for generic description of each parame
ter. This docstring only explains how the workflow works.
step 1. push model to the Model Hub
step 1-1.
create a repository on the HuggingFace Hub. if there is an existing r
epository with the given repo_name, that rpository will be overwritten.
step 1-2.
clone the created or existing remote repository to the local path. Al
so, create a branch named with model version.
step 1-3.
remove every files under the cloned repository(local), and copies the
model related files to the cloned local repository path.
step 1-4.
push the updated repository to the given branch of remote Model Hub.
step 2. push application to the Space Hub
step 2-1.
create a repository on the HuggingFace Hub. if there is an existing r
epository with the given repo_name, that rpository will be overwritten.
step 2-2.
copies directory where the application related files are stored to a
temporary directory. Since the files could be hosted in GCS bucket, t
his process ensures every necessary files are located in the local fil
e system.
step 2-3.
replacek speical tokens in every files under the given directory.
step 2-4.
clone the created or existing remote repository to the local path.
step 2-5.
remove every files under the cloned repository(local), and copies the
application related files to the cloned local repository path.
step 2-6.
push the updated repository to the remote Space Hub. note that the br
anch is always set to "main", so that HuggingFace Space could build t
he application automatically when pushed.
"""
outputs = {}
# step 1
repo_url_prefix = "https://huggingface.co."
repo_id = f"{username}/{repo_name}"
repo_url = f"{repo_url_prefix}/{repo_id}"
# step 1-1
_create_remote_repo(access_token=access_token, repo_id=repo_id)
logging.info(f"remote repository at {repo_url} is prepared")
# step 1-2
local_path = "hf_model"
repository = _clone_and_checkout(
repo_url=repo_url,
local_path=local_path,
access_token=access_token,
version=model_version,
)
logging.info(
f"remote repository is cloned, and new branch {model_version} is created"
)
# step 1-3
_replace_files(model_path, local_path)
logging.info(
"current version of the model is copied to the cloned local repository"
)
# step 1-4
_push_to_remote_repo(
repo=repository,
commit_msg=f"updload new version({model_version})",
branch=model_version,
)
logging.info("updates are pushed to the remote repository")
outputs["repo_id"] = repo_id
outputs["branch"] = model_version
outputs["commit_id"] = f"{repository.git_head_hash()}"
outputs["repo_url"] = repo_url
# step 2
if space_config is not None:
if "app_path" not in space_config:
raise RuntimeError(
f"the app_path is not provided. "
f"app_path is required when space_config is set."
)
model_repo_id = repo_id
model_repo_url = repo_url
if "repo_name" in space_config:
repo_id = f"{username}/{repo_name}"
repo_url = f"{repo_url_prefix}/{repo_id}"
else:
repo_url = f"{repo_url_prefix}/spaces/{repo_id}"
app_path = space_config["app_path"]
app_path = app_path.replace(".", "/")
# step 2-1
_create_remote_repo(
access_token=access_token,
repo_id=repo_id,
repo_type="space",
space_sdk=space_config["space_sdk"]
if "space_sdk" in space_config
else "gradio",
)
# step 2-2
tmp_dir = tempfile.gettempdir()
io_utils.copy_dir(app_path, tmp_dir)
# step 2-3
_replace_placeholders(
target_dir=tmp_dir,
placeholders=space_config["placeholders"]
if "placeholders" in space_config
else None,
model_repo_id=model_repo_id,
model_repo_url=model_repo_url,
model_version=model_version,
)
# step 2-4
local_path = "hf_space"
repository = _clone_and_checkout(
repo_url=repo_url,
local_path=local_path,
access_token=access_token,
)
# step 2-5
_replace_files(tmp_dir, local_path)
# step 2-6
_push_to_remote_repo(
repo=repository,
commit_msg=f"upload {model_version} model",
)
outputs["space_url"] = repo_url
return outputs
|
deep-diver/semantic-segmentation-ml-pipeline | notebooks/gradio_demo_pets.ipynb | from huggingface_hub import from_pretrained_keras
model_ckpt = "chansung/segmentation-training-pipeline@v1667662600"
MODEL = from_pretrained_keras(model_ckpt)from PIL import Image
import numpy as np
import tensorflow as tf
RESOLTUION = 128
# MODEL = tf.keras.models.load_model(model_path)
def preprocess_input(image: Image) -> tf.Tensor:
image = np.array(image)
image = tf.convert_to_tensor(image)
image = tf.image.resize(image, (RESOLTUION, RESOLTUION))
image = image / 255
return tf.expand_dims(image, 0)
# The below utilities (sidewalk_palette(), get_seg_overlay()) are from:
# https://github.com/deep-diver/semantic-segmentation-ml-pipeline/blob/main/notebooks/inference_from_SavedModel.ipynb
def pets_palette():
"""Pets palette that maps each class to RGB values."""
return [
[ 0, 10, 146],
[ 38, 0, 44],
[255, 232, 0],
]
def get_seg_overlay(image, seg):
color_seg = np.zeros(
(seg.shape[0], seg.shape[1], 3), dtype=np.uint8
) # height, width, 3
palette = np.array(pets_palette())
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
# Show image + mask
img = np.array(image) * 0.5 + color_seg * 0.5
img *= 255
img = np.clip(img, 0, 255)
img = img.astype(np.uint8)
return img
def run_model(image: Image) -> tf.Tensor:
preprocessed_image = preprocess_input(image)
prediction = MODEL.predict(preprocessed_image)
seg_mask = tf.math.argmax(prediction, -1)
seg_mask = tf.squeeze(seg_mask)
return seg_mask
def get_predictions(image: Image):
predicted_segmentation_mask = run_model(image)
preprocessed_image = preprocess_input(image)
preprocessed_image = tf.squeeze(preprocessed_image, 0)
pred_img = get_seg_overlay(preprocessed_image.numpy(), predicted_segmentation_mask.numpy())
return Image.fromarray(pred_img)import gradio as gr
title = "Simple demo for a semantic segmentation model trained on the Sidewalks dataset."
description = """
Note that the outputs obtained in this demo won't be state-of-the-art. The underlying project has a different objective focusing more on the ops side of
deploying a semantic segmentation model. For more details, check out the repository: https://github.com/deep-diver/semantic-segmentation-ml-pipeline/.
"""
demo = gr.Interface(
get_predictions,
gr.inputs.Image(type="pil"),
"pil",
allow_flagging="never",
title=title,
description=description,
examples=[["test-image1.png"], ["test-image2.png"]]
)
demo.launch(debug=True) |
deep-diver/semantic-segmentation-ml-pipeline | notebooks/inference_SavedModel_VertexEndpoint.ipynb | model_path = "model"
!mkdir {model_path}
!cp -r tmp/Format-Serving/* {model_path}/import tensorflow as tf
model = tf.keras.models.load_model(model_path)test_image_path = tf.keras.utils.get_file(
"test-image.png", "https://i.ibb.co/F58NjRq/test-image.png"
)
test_label_path = tf.keras.utils.get_file(
"gt-image.png", "https://i.ibb.co/BjPbS6c/gt-image.png"
)import numpy as np
from tensorflow.keras.applications import mobilenet_v2
with open(test_image_path, "rb") as f:
test_image = f.read()
with open(test_label_path, "rb") as f:
gt_image = f.read()
test_image = tf.io.decode_png(test_image, channels=3)
test_image = tf.image.resize(test_image, size=(128, 128))
test_image = mobilenet_v2.preprocess_input(test_image)
test_gt = tf.io.decode_png(gt_image, channels=1)
input_numpy = np.array(test_image)
input_tensor = tf.convert_to_tensor(input_numpy)
input_tensor = input_tensor[tf.newaxis, ...]prediction = model.predict(input_tensor)
print(prediction.shape)
seg_mask = tf.math.argmax(prediction, -1)
seg_mask = tf.squeeze(seg_mask)
seg_maskimport matplotlib.pyplot as plt
f, axs = plt.subplots(1, 2)
f.set_figheight(10)
f.set_figwidth(20)
axs[0].set_title("Prediction", {"fontsize": 40})
axs[0].imshow(seg_mask)
axs[0].axis("off")
axs[1].set_title("Ground truth", {"fontsize": 40})
axs[1].imshow(tf.squeeze(test_gt, axis=2))
axs[1].axis("off")
plt.show()import google.auth # pip install -U google-auth
from google.auth.transport.requests import AuthorizedSession
import tensorflow as tf
import base64
import jsonPROJECT_ID = "gcp-ml-172005"
REGION = "us-central1"
ENDPOINT_ID = "2220630858061053952"credentials, _ = google.auth.default()
service_endpoint = f"https://{REGION}-aiplatform.googleapis.com"
authed_session = AuthorizedSession(credentials)url = "{}/v1/projects/{}/locations/{}/endpoints/{}:predict".format(
service_endpoint, PROJECT_ID, REGION, ENDPOINT_ID
)
print("Endpoint: ", url)serving_input = list(
model.signatures["serving_default"].structured_input_signature[1].keys()
)[0]
print("Serving function input:", serving_input)with open(test_image_path, "rb") as f:
image = f.read()
b64str = base64.urlsafe_b64encode(image).decode('utf-8')single_instance_request_body = {
"instances": [
{serving_input: b64str}
]
}
two_instances_request_body = {
"instances": [
{serving_input: b64str},
{serving_input: b64str},
]
}response = authed_session.post(url, data=json.dumps(single_instance_request_body))
print(response)
print(response.content)import ast
response = response.content.decode('utf-8')
response = ast.literal_eval(response)
seg_mask_ve = tf.convert_to_tensor(response['predictions'])
seg_mask_ve = tf.squeeze(seg_mask_ve)
seg_mask_veimport matplotlib.pyplot as plt
f, axs = plt.subplots(1, 3)
f.set_figheight(10)
f.set_figwidth(30)
axs[0].set_title("Prediction(local)", {"fontsize": 40})
axs[0].imshow(seg_mask)
axs[0].axis("off")
axs[1].set_title("Prediction(vertex endpoint)", {"fontsize": 40})
axs[1].imshow(seg_mask_ve)
axs[1].axis("off")
axs[2].set_title("Ground truth", {"fontsize": 40})
axs[2].imshow(tf.squeeze(test_gt, axis=2))
axs[2].axis("off")
plt.show()import numpy as np
np.testing.assert_allclose(seg_mask, seg_mask_ve)response = authed_session.post(url, data=json.dumps(two_instances_request_body))
print(response)
print(response.content)response = response.content.decode('utf-8')
response = ast.literal_eval(response)
seg_mask = tf.convert_to_tensor(response['predictions'])
seg_mask = tf.squeeze(seg_mask)
seg_mask |
deep-diver/semantic-segmentation-ml-pipeline | notebooks/parse_tfrecords_pets.ipynb | import tensorflow as tfGCS_PATH = "gs://pets-tfrecords/pets-tfrecords"
BATCH_SIZE = 4
AUTO = tf.data.AUTOTUNEdef parse_tfr(proto):
feature_description = {
"image": tf.io.VarLenFeature(tf.float32),
"image_shape": tf.io.VarLenFeature(tf.int64),
"label": tf.io.VarLenFeature(tf.float32),
"label_shape": tf.io.VarLenFeature(tf.int64),
}
rec = tf.io.parse_single_example(proto, feature_description)
image_shape = tf.sparse.to_dense(rec["image_shape"])
image = tf.reshape(tf.sparse.to_dense(rec["image"]), image_shape)
label_shape = tf.sparse.to_dense(rec["label_shape"])
label = tf.reshape(tf.sparse.to_dense(rec["label"]), label_shape)
return {"pixel_values": image, "label": label}
def prepare_dataset(GCS_PATH=GCS_PATH, split="train", batch_size=BATCH_SIZE):
if split not in ["train", "val"]:
raise ValueError(
"Invalid split provided. Supports splits are: `train` and `val`."
)
dataset = tf.data.TFRecordDataset(
[filename for filename in tf.io.gfile.glob(f"{GCS_PATH}/{split}-*")],
num_parallel_reads=AUTO,
).map(parse_tfr, num_parallel_calls=AUTO)
if split == "train":
dataset = dataset.shuffle(batch_size * 2)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(AUTO)
return datasettrain_dataset = prepare_dataset()
val_dataset = prepare_dataset(split="val")for batch in train_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape)for batch in val_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape) |
deep-diver/semantic-segmentation-ml-pipeline | notebooks/parse_tfrecords_sidewalks.ipynb | import tensorflow as tfGCS_PATH_FULL_RESOUTION = "gs://sidewalks-tfx-fullres/sidewalks-tfrecords"
GCS_PATH_LOW_RESOLUTION = "gs://sidewalks-tfx-lowres/sidewalks-tfrecords"
BATCH_SIZE = 4
AUTO = tf.data.AUTOTUNEdef parse_tfr(proto):
feature_description = {
"image": tf.io.VarLenFeature(tf.float32),
"image_shape": tf.io.VarLenFeature(tf.int64),
"label": tf.io.VarLenFeature(tf.float32),
"label_shape": tf.io.VarLenFeature(tf.int64),
}
rec = tf.io.parse_single_example(proto, feature_description)
image_shape = tf.sparse.to_dense(rec["image_shape"])
image = tf.reshape(tf.sparse.to_dense(rec["image"]), image_shape)
label_shape = tf.sparse.to_dense(rec["label_shape"])
label = tf.reshape(tf.sparse.to_dense(rec["label"]), label_shape)
return {"pixel_values": image, "label": label}
def prepare_dataset(GCS_PATH=GCS_PATH_FULL_RESOUTION, split="train", batch_size=BATCH_SIZE):
if split not in ["train", "val"]:
raise ValueError(
"Invalid split provided. Supports splits are: `train` and `val`."
)
dataset = tf.data.TFRecordDataset(
[filename for filename in tf.io.gfile.glob(f"{GCS_PATH}/{split}-*")],
num_parallel_reads=AUTO,
).map(parse_tfr, num_parallel_calls=AUTO)
if split == "train":
dataset = dataset.shuffle(batch_size * 2)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(AUTO)
return datasettrain_dataset = prepare_dataset()
val_dataset = prepare_dataset(split="val")for batch in train_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape)for batch in val_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape)train_dataset = prepare_dataset(GCS_PATH_LOW_RESOLUTION)
val_dataset = prepare_dataset(GCS_PATH_LOW_RESOLUTION, split="val")for batch in train_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape)for batch in val_dataset.take(1):
print(batch["pixel_values"].shape, batch["label"].shape) |
deep-diver/semantic-segmentation-ml-pipeline | notebooks/tfx_pipeline_pets.ipynb | data_path = "gs://sidewalks-tfx-lowres/sidewalks-tfrecords"
local_data_path = "data"
model_file = "modules/model.py"
model_fn = "modules.model.run_fn"
preprocessing_file = "modules/preprocessing.py"
preprocessing_fn = "modules.preprocessing.preprocessing_fn"import tfx
tfx.__version__from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.components import ImportExampleGen
from tfx.components import SchemaGen
from tfx.components import StatisticsGen
from tfx.components import Trainer
from tfx.components import Transform
from tfx.components import Evaluator
from tfx.proto import example_gen_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import LatestBlessedModelResolver
import tensorflow_model_analysis as tfmacontext = InteractiveContext()input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(
input_base=local_data_path,
input_config=input_config
)context.run(example_gen)statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])context.run(statistics_gen)context.show(statistics_gen.outputs['statistics'])schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'])context.run(schema_gen)%%writefile {preprocessing_file}
import tensorflow as tf
from tensorflow.keras.applications import mobilenet_v2
_INPUT_IMG_SIZE = 128
_IMAGE_KEY = "image"
_IMAGE_SHAPE_KEY = "image_shape"
_LABEL_KEY = "label"
_LABEL_SHAPE_KEY = "label_shape"
def _transformed_name(key: str) -> str:
return key + "_xf"
# output should have the same keys as inputs
def preprocess(inputs):
image_shape = inputs[_IMAGE_SHAPE_KEY]
label_shape = inputs[_LABEL_SHAPE_KEY]
images = tf.reshape(inputs[_IMAGE_KEY], [image_shape[0], image_shape[1], 3])
labels = tf.reshape(inputs[_LABEL_KEY], [label_shape[0], label_shape[1], 1])
return {
_IMAGE_KEY: images,
_IMAGE_SHAPE_KEY: inputs[_IMAGE_SHAPE_KEY],
_LABEL_KEY: labels,
_LABEL_SHAPE_KEY: inputs[_LABEL_SHAPE_KEY],
}
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
features = tf.map_fn(preprocess, inputs)
features[_IMAGE_KEY] = tf.image.resize(features[_IMAGE_KEY], [_INPUT_IMG_SIZE, _INPUT_IMG_SIZE])
features[_LABEL_KEY] = tf.image.resize(features[_LABEL_KEY], [_INPUT_IMG_SIZE, _INPUT_IMG_SIZE])
image_features = mobilenet_v2.preprocess_input(features[_IMAGE_KEY])
outputs[_transformed_name(_IMAGE_KEY)] = image_features
outputs[_transformed_name(_LABEL_KEY)] = features[_LABEL_KEY]
return outputstransform = Transform(
examples=example_gen.outputs["examples"],
schema=schema_gen.outputs["schema"],
preprocessing_fn=preprocessing_fn,
)context.run(transform)%%writefile {model_file}
from typing import List, Dict, Tuple
import absl
import tensorflow as tf
import tensorflow_transform as tft
from tensorflow.keras.optimizers import Adam
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx.components.trainer.fn_args_utils import DataAccessor
_CONCRETE_INPUT = "pixel_values"
_RAW_IMG_SIZE = 256
_INPUT_IMG_SIZE = 128
_TRAIN_LENGTH = 800
_EVAL_LENGTH = 200
_TRAIN_BATCH_SIZE = 64
_EVAL_BATCH_SIZE = 64
_EPOCHS = 1
_LR = 0.00006
_IMAGE_KEY = "image"
_LABEL_KEY = "label"
def INFO(text: str):
absl.logging.info(text)
def _transformed_name(key: str) -> str:
return key + "_xf"
"""
_serving_preprocess, _serving_preprocess_fn, and
_model_exporter functions are defined to provide pre-
processing capabilities when the model is served.
"""
def _serving_preprocess(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
decoded = decoded / 255
resized = tf.image.resize(decoded, size=(_INPUT_IMG_SIZE, _INPUT_IMG_SIZE))
return resized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _serving_preprocess_fn(string_input):
decoded_images = tf.map_fn(
_serving_preprocess, string_input, dtype=tf.float32, back_prop=False
)
return {_CONCRETE_INPUT: decoded_images}
def _model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(shape=[None, _INPUT_IMG_SIZE, _INPUT_IMG_SIZE, 3], dtype=tf.float32, name=_CONCRETE_INPUT)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
images = _serving_preprocess_fn(string_input)
logits = m_call(**images)
seg_mask = tf.math.argmax(logits, -1)
return {"seg_mask": seg_mask}
return serving_fn
def _get_transform_features_signature(model, tf_transform_output):
# the layer is added as an attribute to the model in order to make sure that
# the model assets are handled correctly when exporting.
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(serialized_tf_examples):
"""Returns the output to be used in the serving signature."""
feature_spec = tf_transform_output.raw_feature_spec()
parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
transformed_features = model.tft_layer(parsed_features)
return transformed_features
return serve_tf_examples_fn
def _get_tf_examples_serving_signature(model, tf_transform_output):
"""
Returns a serving signature that accepts `tensorflow.Example`.
This signature will be used for evaluation or bulk inference.
"""
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(
serialized_tf_example: tf.Tensor,
) -> Dict[str, tf.Tensor]:
"""Returns the output to be used in the serving signature."""
# Load the schema of raw examples.
raw_feature_spec = tf_transform_output.raw_feature_spec()
# Remove label feature since these will not be present at serving time.
# Parse the examples using schema into raw features
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
# Preprocess the raw features
transformed_features = model.tft_layer(raw_features)
# Run preprocessed inputs through the model to get the prediction
outputs = model(transformed_features)
return {
_transformed_name(_LABEL_KEY): outputs
}
return serve_tf_examples_fn
"""
_input_fn reads TFRecord files passed from the upstream
TFX component, Transform. Assume the dataset is already
transformed appropriately.
"""
def _input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = 200,
) -> tf.data.Dataset:
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_transformed_name(_LABEL_KEY)
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
def _build_model(num_labels) -> tf.keras.Model:
base_model = tf.keras.applications.MobileNetV2(
input_shape=[128, 128, 3], include_top=False
)
# Use the activations of these layers
layer_names = [
"block_1_expand_relu", # 64x64
"block_3_expand_relu", # 32x32
"block_6_expand_relu", # 16x16
"block_13_expand_relu", # 8x8
"block_16_project", # 4x4
]
base_model_outputs = [base_model.get_layer(name).output for name in layer_names]
# Create the feature extraction model
down_stack = tf.keras.Model(inputs=base_model.input, outputs=base_model_outputs)
down_stack.trainable = False
up_stack = [
upsample(512, 3), # 4x4 -> 8x8
upsample(256, 3), # 8x8 -> 16x16
upsample(128, 3), # 16x16 -> 32x32
upsample(64, 3), # 32x32 -> 64x64
]
inputs = tf.keras.layers.Input(
shape=[128, 128, 3], name=_transformed_name(_IMAGE_KEY)
)
# Downsampling through the model
skips = down_stack(inputs)
x = skips[-1]
skips = reversed(skips[:-1])
# Upsampling and establishing the skip connections
for up, skip in zip(up_stack, skips):
x = up(x)
concat = tf.keras.layers.Concatenate()
x = concat([x, skip])
# This is the last layer of the model
last = tf.keras.layers.Conv2DTranspose(
filters=num_labels, kernel_size=3, strides=2, padding="same", name=_transformed_name(_LABEL_KEY)
) # 64x64 -> 128x128
x = last(x)
model = tf.keras.Model(inputs=inputs, outputs=x)
model.compile(
optimizer=Adam(learning_rate=_LR),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=["sparse_categorical_accuracy"],
)
return model
"""
InstanceNormalization class and upsample function are
borrowed from pix2pix in [TensorFlow Example repository](
https://github.com/tensorflow/examples/tree/master/tensorflow_examples/models/pix2pix)
"""
class InstanceNormalization(tf.keras.layers.Layer):
"""Instance Normalization Layer (https://arxiv.org/abs/1607.08022)."""
def __init__(self, epsilon=1e-5):
super(InstanceNormalization, self).__init__()
self.epsilon = epsilon
def build(self, input_shape):
self.scale = self.add_weight(
name="scale",
shape=input_shape[-1:],
initializer=tf.random_normal_initializer(1.0, 0.02),
trainable=True,
)
self.offset = self.add_weight(
name="offset", shape=input_shape[-1:], initializer="zeros", trainable=True
)
def call(self, x):
mean, variance = tf.nn.moments(x, axes=[1, 2], keepdims=True)
inv = tf.math.rsqrt(variance + self.epsilon)
normalized = (x - mean) * inv
return self.scale * normalized + self.offset
def upsample(filters, size, norm_type="batchnorm", apply_dropout=False):
"""Upsamples an input.
Conv2DTranspose => Batchnorm => Dropout => Relu
Args:
filters: number of filters
size: filter size
norm_type: Normalization type; either 'batchnorm' or 'instancenorm'.
apply_dropout: If True, adds the dropout layer
Returns:
Upsample Sequential Model
"""
initializer = tf.random_normal_initializer(0.0, 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2DTranspose(
filters,
size,
strides=2,
padding="same",
kernel_initializer=initializer,
use_bias=False,
)
)
if norm_type.lower() == "batchnorm":
result.add(tf.keras.layers.BatchNormalization())
elif norm_type.lower() == "instancenorm":
result.add(InstanceNormalization())
if apply_dropout:
result.add(tf.keras.layers.Dropout(0.5))
result.add(tf.keras.layers.ReLU())
return result
def run_fn(fn_args: FnArgs):
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=_TRAIN_BATCH_SIZE,
)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=_EVAL_BATCH_SIZE,
)
num_labels = 35
model = _build_model(num_labels)
model.fit(
train_dataset,
steps_per_epoch=_TRAIN_LENGTH // _TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=_EVAL_LENGTH // _TRAIN_BATCH_SIZE,
epochs=_EPOCHS,
)
model.save(
fn_args.serving_model_dir, save_format="tf", signatures={
"serving_default": _model_exporter(model),
"transform_features": _get_transform_features_signature(
model, tf_transform_output
),
"from_examples": _get_tf_examples_serving_signature(
model, tf_transform_output
),
}
)
trainer = Trainer(
run_fn=model_fn,
transformed_examples=transform.outputs["transformed_examples"],
transform_graph=transform.outputs["transform_graph"],
schema=schema_gen.outputs["schema"],
)context.run(trainer) |
deep-diver/semantic-segmentation-ml-pipeline | notebooks/tfx_pipeline_sidewalks.ipynb | data_path = "gs://sidewalks-tfx-lowres/sidewalks-tfrecords"
local_data_path = "data"
model_file = "modules/model.py"
model_fn = "modules.model.run_fn"
preprocessing_file = "modules/preprocessing.py"
preprocessing_fn = "modules.preprocessing.preprocessing_fn"import tfx
tfx.__version__from tfx import v1 as tfx
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx.components import ImportExampleGen
from tfx.components import SchemaGen
from tfx.components import StatisticsGen
from tfx.components import Trainer
from tfx.components import Transform
from tfx.components import Evaluator
from tfx.proto import example_gen_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.dsl.components.common import resolver
from tfx.dsl.experimental.latest_blessed_model_resolver import LatestBlessedModelResolver
import tensorflow_model_analysis as tfmacontext = InteractiveContext()input_config = example_gen_pb2.Input(
splits=[
example_gen_pb2.Input.Split(name="train", pattern="train-*.tfrec"),
example_gen_pb2.Input.Split(name="eval", pattern="val-*.tfrec"),
]
)
example_gen = ImportExampleGen(
input_base=local_data_path,
input_config=input_config
)context.run(example_gen)statistics_gen = StatisticsGen(examples=example_gen.outputs["examples"])context.run(statistics_gen)context.show(statistics_gen.outputs['statistics'])schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'])context.run(schema_gen)%%writefile {preprocessing_file}
import tensorflow as tf
from tensorflow.keras.applications import mobilenet_v2
_INPUT_IMG_SIZE = 128
_IMAGE_KEY = "image"
_IMAGE_SHAPE_KEY = "image_shape"
_LABEL_KEY = "label"
_LABEL_SHAPE_KEY = "label_shape"
def _transformed_name(key: str) -> str:
return key + "_xf"
# output should have the same keys as inputs
def preprocess(inputs):
image_shape = inputs[_IMAGE_SHAPE_KEY]
label_shape = inputs[_LABEL_SHAPE_KEY]
images = tf.reshape(inputs[_IMAGE_KEY], [image_shape[0], image_shape[1], 3])
labels = tf.reshape(inputs[_LABEL_KEY], [label_shape[0], label_shape[1], 1])
return {
_IMAGE_KEY: images,
_IMAGE_SHAPE_KEY: inputs[_IMAGE_SHAPE_KEY],
_LABEL_KEY: labels,
_LABEL_SHAPE_KEY: inputs[_LABEL_SHAPE_KEY],
}
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# print(inputs)
outputs = {}
features = tf.map_fn(preprocess, inputs)
features[_IMAGE_KEY] = tf.image.resize(features[_IMAGE_KEY], [_INPUT_IMG_SIZE, _INPUT_IMG_SIZE])
features[_LABEL_KEY] = tf.image.resize(features[_LABEL_KEY], [_INPUT_IMG_SIZE, _INPUT_IMG_SIZE])
image_features = mobilenet_v2.preprocess_input(features[_IMAGE_KEY])
outputs[_transformed_name(_IMAGE_KEY)] = image_features
outputs[_transformed_name(_LABEL_KEY)] = features[_LABEL_KEY]
return outputstransform = Transform(
examples=example_gen.outputs["examples"],
schema=schema_gen.outputs["schema"],
preprocessing_fn=preprocessing_fn,
)context.run(transform)%%writefile {model_file}
from typing import List, Dict, Tuple
import absl
import tensorflow as tf
import tensorflow_transform as tft
from tensorflow.keras.optimizers import Adam
from tfx_bsl.tfxio import dataset_options
from tfx.components.trainer.fn_args_utils import FnArgs
from tfx.components.trainer.fn_args_utils import DataAccessor
_CONCRETE_INPUT = "pixel_values"
_RAW_IMG_SIZE = 256
_INPUT_IMG_SIZE = 128
_TRAIN_LENGTH = 800
_EVAL_LENGTH = 200
_TRAIN_BATCH_SIZE = 64
_EVAL_BATCH_SIZE = 64
_EPOCHS = 1
_LR = 0.00006
_IMAGE_KEY = "image"
_LABEL_KEY = "label"
def INFO(text: str):
absl.logging.info(text)
def _transformed_name(key: str) -> str:
return key + "_xf"
"""
_serving_preprocess, _serving_preprocess_fn, and
_model_exporter functions are defined to provide pre-
processing capabilities when the model is served.
"""
def _serving_preprocess(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
decoded = decoded / 255
resized = tf.image.resize(decoded, size=(_INPUT_IMG_SIZE, _INPUT_IMG_SIZE))
return resized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _serving_preprocess_fn(string_input):
decoded_images = tf.map_fn(
_serving_preprocess, string_input, dtype=tf.float32, back_prop=False
)
return {_CONCRETE_INPUT: decoded_images}
def _model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(shape=[None, _INPUT_IMG_SIZE, _INPUT_IMG_SIZE, 3], dtype=tf.float32, name=_CONCRETE_INPUT)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
images = _serving_preprocess_fn(string_input)
logits = m_call(**images)
seg_mask = tf.math.argmax(logits, -1)
return {"seg_mask": seg_mask}
return serving_fn
def _get_transform_features_signature(model, tf_transform_output):
# the layer is added as an attribute to the model in order to make sure that
# the model assets are handled correctly when exporting.
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(serialized_tf_examples):
"""Returns the output to be used in the serving signature."""
feature_spec = tf_transform_output.raw_feature_spec()
parsed_features = tf.io.parse_example(serialized_tf_examples, feature_spec)
transformed_features = model.tft_layer(parsed_features)
return transformed_features
return serve_tf_examples_fn
def _get_tf_examples_serving_signature(model, tf_transform_output):
"""
Returns a serving signature that accepts `tensorflow.Example`.
This signature will be used for evaluation or bulk inference.
"""
model.tft_layer = tf_transform_output.transform_features_layer()
@tf.function(
input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string, name="examples")]
)
def serve_tf_examples_fn(
serialized_tf_example: tf.Tensor,
) -> Dict[str, tf.Tensor]:
"""Returns the output to be used in the serving signature."""
# Load the schema of raw examples.
raw_feature_spec = tf_transform_output.raw_feature_spec()
# Remove label feature since these will not be present at serving time.
# Parse the examples using schema into raw features
raw_features = tf.io.parse_example(serialized_tf_example, raw_feature_spec)
# Preprocess the raw features
transformed_features = model.tft_layer(raw_features)
# Run preprocessed inputs through the model to get the prediction
outputs = model(transformed_features)
return {
_transformed_name(_LABEL_KEY): outputs
}
return serve_tf_examples_fn
"""
_input_fn reads TFRecord files passed from the upstream
TFX component, Transform. Assume the dataset is already
transformed appropriately.
"""
def _input_fn(
file_pattern: List[str],
data_accessor: DataAccessor,
tf_transform_output: tft.TFTransformOutput,
is_train: bool = False,
batch_size: int = 200,
) -> tf.data.Dataset:
dataset = data_accessor.tf_dataset_factory(
file_pattern,
dataset_options.TensorFlowDatasetOptions(
batch_size=batch_size, label_key=_transformed_name(_LABEL_KEY)
),
tf_transform_output.transformed_metadata.schema,
)
return dataset
def _build_model(num_labels) -> tf.keras.Model:
base_model = tf.keras.applications.MobileNetV2(
input_shape=[128, 128, 3], include_top=False
)
# Use the activations of these layers
layer_names = [
"block_1_expand_relu", # 64x64
"block_3_expand_relu", # 32x32
"block_6_expand_relu", # 16x16
"block_13_expand_relu", # 8x8
"block_16_project", # 4x4
]
base_model_outputs = [base_model.get_layer(name).output for name in layer_names]
# Create the feature extraction model
down_stack = tf.keras.Model(inputs=base_model.input, outputs=base_model_outputs)
down_stack.trainable = False
up_stack = [
upsample(512, 3), # 4x4 -> 8x8
upsample(256, 3), # 8x8 -> 16x16
upsample(128, 3), # 16x16 -> 32x32
upsample(64, 3), # 32x32 -> 64x64
]
inputs = tf.keras.layers.Input(
shape=[128, 128, 3], name=_transformed_name(_IMAGE_KEY)
)
# Downsampling through the model
skips = down_stack(inputs)
x = skips[-1]
skips = reversed(skips[:-1])
# Upsampling and establishing the skip connections
for up, skip in zip(up_stack, skips):
x = up(x)
concat = tf.keras.layers.Concatenate()
x = concat([x, skip])
# This is the last layer of the model
last = tf.keras.layers.Conv2DTranspose(
filters=num_labels, kernel_size=3, strides=2, padding="same", name=_transformed_name(_LABEL_KEY)
) # 64x64 -> 128x128
x = last(x)
model = tf.keras.Model(inputs=inputs, outputs=x)
model.compile(
optimizer=Adam(learning_rate=_LR),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=["sparse_categorical_accuracy"],
)
return model
"""
InstanceNormalization class and upsample function are
borrowed from pix2pix in [TensorFlow Example repository](
https://github.com/tensorflow/examples/tree/master/tensorflow_examples/models/pix2pix)
"""
class InstanceNormalization(tf.keras.layers.Layer):
"""Instance Normalization Layer (https://arxiv.org/abs/1607.08022)."""
def __init__(self, epsilon=1e-5):
super(InstanceNormalization, self).__init__()
self.epsilon = epsilon
def build(self, input_shape):
self.scale = self.add_weight(
name="scale",
shape=input_shape[-1:],
initializer=tf.random_normal_initializer(1.0, 0.02),
trainable=True,
)
self.offset = self.add_weight(
name="offset", shape=input_shape[-1:], initializer="zeros", trainable=True
)
def call(self, x):
mean, variance = tf.nn.moments(x, axes=[1, 2], keepdims=True)
inv = tf.math.rsqrt(variance + self.epsilon)
normalized = (x - mean) * inv
return self.scale * normalized + self.offset
def upsample(filters, size, norm_type="batchnorm", apply_dropout=False):
"""Upsamples an input.
Conv2DTranspose => Batchnorm => Dropout => Relu
Args:
filters: number of filters
size: filter size
norm_type: Normalization type; either 'batchnorm' or 'instancenorm'.
apply_dropout: If True, adds the dropout layer
Returns:
Upsample Sequential Model
"""
initializer = tf.random_normal_initializer(0.0, 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2DTranspose(
filters,
size,
strides=2,
padding="same",
kernel_initializer=initializer,
use_bias=False,
)
)
if norm_type.lower() == "batchnorm":
result.add(tf.keras.layers.BatchNormalization())
elif norm_type.lower() == "instancenorm":
result.add(InstanceNormalization())
if apply_dropout:
result.add(tf.keras.layers.Dropout(0.5))
result.add(tf.keras.layers.ReLU())
return result
def run_fn(fn_args: FnArgs):
tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
train_dataset = _input_fn(
fn_args.train_files,
fn_args.data_accessor,
tf_transform_output,
is_train=True,
batch_size=_TRAIN_BATCH_SIZE,
)
eval_dataset = _input_fn(
fn_args.eval_files,
fn_args.data_accessor,
tf_transform_output,
is_train=False,
batch_size=_EVAL_BATCH_SIZE,
)
num_labels = 35
model = _build_model(num_labels)
model.fit(
train_dataset,
steps_per_epoch=_TRAIN_LENGTH // _TRAIN_BATCH_SIZE,
validation_data=eval_dataset,
validation_steps=_EVAL_LENGTH // _TRAIN_BATCH_SIZE,
epochs=_EPOCHS,
)
model.save(
fn_args.serving_model_dir, save_format="tf", signatures={
"serving_default": _model_exporter(model),
"transform_features": _get_transform_features_signature(
model, tf_transform_output
),
"from_examples": _get_tf_examples_serving_signature(
model, tf_transform_output
),
}
)
trainer = Trainer(
run_fn=model_fn,
transformed_examples=transform.outputs["transformed_examples"],
transform_graph=transform.outputs["transform_graph"],
schema=schema_gen.outputs["schema"],
)context.run(trainer) |
deep-diver/semantic-segmentation-ml-pipeline | notebooks/unet_training_sidewalks.ipynb | import tensorflow as tf
import numpy as npGCS_PATH = "gs://sidewalks-tfx-hf/sidewalks-tfrecords"
BATCH_SIZE = 2
AUTO = tf.data.AUTOTUNEdef parse_tfr(proto):
feature_description = {
"image": tf.io.FixedLenFeature([], tf.string),
"label": tf.io.FixedLenFeature([], tf.string)
}
rec = tf.io.parse_single_example(proto, feature_description)
image = tf.io.parse_tensor(rec["image"], tf.float32)
label = tf.io.parse_tensor(rec["label"], tf.float32)
return {"pixel_values": image, "labels": label}
def prepare_dataset(split="train", batch_size=BATCH_SIZE):
if split not in ["train", "val"]:
raise ValueError(
"Invalid split provided. Supports splits are: `train` and `val`."
)
dataset = tf.data.TFRecordDataset(
[filename for filename in tf.io.gfile.glob(f"{GCS_PATH}/{split}-*")],
num_parallel_reads=AUTO,
).map(parse_tfr, num_parallel_calls=AUTO)
if split == "train":
dataset = dataset.shuffle(batch_size * 2)
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(AUTO)
return datasettrain_dataset = prepare_dataset()
val_dataset = prepare_dataset(split="val")batch = next(iter(train_dataset))
batch[0].shape, batch[1].shapedef preprocess(example_batch):
images = example_batch["pixel_values"]
images = tf.transpose(images, perm=[0, 1, 2, 3]) # (batch_size, height, width, num_channels)
labels = tf.expand_dims(example_batch["labels"], -1) # Adds extra dimension, otherwise tf.image.resize won't work.
labels = tf.transpose(labels, perm=[0, 1, 2, 3]) # So, that TF can evaluation the shapes.
images = tf.image.resize(images, (128, 128))
labels = tf.image.resize(labels, (128, 128))
# images = tf.transpose(images, perm=[0, 3, 1, 2]) # (batch_size, num_channels, height, width)
labels = tf.squeeze(labels, -1)
return images, labelstrain_dataset = train_dataset.map(preprocess)
val_dataset = val_dataset.map(preprocess)# Investigate a single batch.
batch = next(iter(train_dataset))
batch[0].shape, batch[1].shapedef sidewalk_palette():
"""Sidewalk palette that maps each class to RGB values."""
return [
[0, 0, 0],
[216, 82, 24],
[255, 255, 0],
[125, 46, 141],
[118, 171, 47],
[161, 19, 46],
[255, 0, 0],
[0, 128, 128],
[190, 190, 0],
[0, 255, 0],
[0, 0, 255],
[170, 0, 255],
[84, 84, 0],
[84, 170, 0],
[84, 255, 0],
[170, 84, 0],
[170, 170, 0],
[170, 255, 0],
[255, 84, 0],
[255, 170, 0],
[255, 255, 0],
[33, 138, 200],
[0, 170, 127],
[0, 255, 127],
[84, 0, 127],
[84, 84, 127],
[84, 170, 127],
[84, 255, 127],
[170, 0, 127],
[170, 84, 127],
[170, 170, 127],
[170, 255, 127],
[255, 0, 127],
[255, 84, 127],
[255, 170, 127],
]def get_seg_overlay(image, seg):
# image = tf.transpose(image, [1, 2, 0])
color_seg = np.zeros(
(seg.shape[0], seg.shape[1], 3), dtype=np.uint8
) # height, width, 3
palette = np.array(sidewalk_palette())
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
# Show image + mask
img = np.array(image) * 0.5 + color_seg * 0.5
img = img.astype(np.uint8)
return imggt_img = get_seg_overlay(
batch[0][0],
np.array(batch[1][0])
)import matplotlib.pyplot as plt
f, axs = plt.subplots(1, 1)
f.set_figheight(5)
f.set_figwidth(10)
axs.set_title("Ground truth", {"fontsize": 20})
axs.imshow(gt_img)
axs.axis("off")
plt.show()class InstanceNormalization(tf.keras.layers.Layer):
"""Instance Normalization Layer (https://arxiv.org/abs/1607.08022)."""
def __init__(self, epsilon=1e-5):
super(InstanceNormalization, self).__init__()
self.epsilon = epsilon
def build(self, input_shape):
self.scale = self.add_weight(
name='scale',
shape=input_shape[-1:],
initializer=tf.random_normal_initializer(1., 0.02),
trainable=True)
self.offset = self.add_weight(
name='offset',
shape=input_shape[-1:],
initializer='zeros',
trainable=True)
def call(self, x):
mean, variance = tf.nn.moments(x, axes=[1, 2], keepdims=True)
inv = tf.math.rsqrt(variance + self.epsilon)
normalized = (x - mean) * inv
return self.scale * normalized + self.offsetdef upsample(filters, size, norm_type='batchnorm', apply_dropout=False):
"""Upsamples an input.
Conv2DTranspose => Batchnorm => Dropout => Relu
Args:
filters: number of filters
size: filter size
norm_type: Normalization type; either 'batchnorm' or 'instancenorm'.
apply_dropout: If True, adds the dropout layer
Returns:
Upsample Sequential Model
"""
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
padding='same',
kernel_initializer=initializer,
use_bias=False))
if norm_type.lower() == 'batchnorm':
result.add(tf.keras.layers.BatchNormalization())
elif norm_type.lower() == 'instancenorm':
result.add(InstanceNormalization())
if apply_dropout:
result.add(tf.keras.layers.Dropout(0.5))
result.add(tf.keras.layers.ReLU())
return resultbase_model = tf.keras.applications.MobileNetV2(input_shape=[128, 128, 3], include_top=False)
# Use the activations of these layers
layer_names = [
'block_1_expand_relu', # 64x64
'block_3_expand_relu', # 32x32
'block_6_expand_relu', # 16x16
'block_13_expand_relu', # 8x8
'block_16_project', # 4x4
]
base_model_outputs = [base_model.get_layer(name).output for name in layer_names]
# Create the feature extraction model
down_stack = tf.keras.Model(inputs=base_model.input, outputs=base_model_outputs)
down_stack.trainable = Falseup_stack = [
upsample(512, 3), # 4x4 -> 8x8
upsample(256, 3), # 8x8 -> 16x16
upsample(128, 3), # 16x16 -> 32x32
upsample(64, 3), # 32x32 -> 64x64
]def unet_model(output_channels:int):
inputs = tf.keras.layers.Input(shape=[128, 128, 3], name="pixel_values")
# Downsampling through the model
skips = down_stack(inputs)
x = skips[-1]
skips = reversed(skips[:-1])
# Upsampling and establishing the skip connections
for up, skip in zip(up_stack, skips):
x = up(x)
concat = tf.keras.layers.Concatenate()
x = concat([x, skip])
# This is the last layer of the model
last = tf.keras.layers.Conv2DTranspose(
filters=output_channels, kernel_size=3, strides=2,
padding='same',
name="labels") #64x64 -> 128x128
x = last(x)
return tf.keras.Model(inputs=inputs, outputs=x)OUTPUT_CLASSES = len(sidewalk_palette())
model = unet_model(output_channels=OUTPUT_CLASSES)
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])model.summary()EPOCHS = 5
model_history = model.fit(
train_dataset,
validation_data=val_dataset,
epochs=EPOCHS
)test_batch = next(iter(val_dataset))pred_mask = model.predict(test_batch[0])pred_seg = tf.math.argmax(pred_mask[0], axis=-1)import matplotlib.pyplot as plt
seg_im = get_seg_overlay(test_batch[0][0], pred_seg)
f, axis = plt.subplots(1, 2)
f.set_figheight(5)
f.set_figwidth(20)
axis[0].set_title("Image", {"fontsize": 20})
axis[1].set_title("Prediction", {"fontsize": 20})
axis[0].axis("off")
axis[1].axis("off")
axis[0].imshow(test_batch[0][0])
axis[1].imshow(seg_im)_IMAGE_SHAPE = (128, 128)
_CONCRETE_INPUT = "pixel_values"
def _serving_preprocess(string_input):
decoded_input = tf.io.decode_base64(string_input)
decoded = tf.io.decode_jpeg(decoded_input, channels=3)
decoded = decoded / 255
resized = tf.image.resize(decoded, size=_IMAGE_SHAPE)
return resized
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def _serving_preprocess_fn(string_input):
decoded_images = tf.map_fn(
_serving_preprocess, string_input, dtype=tf.float32, back_prop=False
)
return {_CONCRETE_INPUT: decoded_images}
def _model_exporter(model: tf.keras.Model):
m_call = tf.function(model.call).get_concrete_function(
tf.TensorSpec(
shape=[None, 128, 128, 3], dtype=tf.float32, name=_CONCRETE_INPUT
)
)
@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])
def serving_fn(string_input):
images = _serving_preprocess_fn(string_input)
logits = m_call(**images)
seg_mask = tf.math.argmax(logits, -1)
return {"seg_mask": seg_mask}
return serving_fnmodel.save(
"./test_model",
save_format="tf",
signatures=_model_exporter(model)
)model = tf.keras.models.load_model('./test_model')pred_mask = model.predict(test_batch[0])pred_mask.shape |
deep-diver/semantic-segmentation-ml-pipeline | tfrecords/create_tfrecords.py | """
Script to generate TFRecord shards from the Sidewalks dataset as shown in this
blog post: https://huggingface.co./blog/fine-tune-segformer.
The recommended way to obtain TFRecord shards is via an Apache Beam
Pipeline with an execution runner like Dataflow. Example:
https://github.com/GoogleCloudPlatform/practical-ml-vision-book/blob/master/05_create_dataset/jpeg_to_tfrecord.py.
Usage:
python create_tfrecords --batch_size 16
python create_tfrecords --resize 256 # without --resize flag, no resizing is applied
References:
* https://github.com/GoogleCloudPlatform/practical-ml-vision-book/blob/master/05_create_dataset/05_split_tfrecord.ipynb
* https://www.tensorflow.org/tutorials/images/segmentation
"""
import argparse
import math
import os
from typing import Tuple
import datasets
import numpy as np
import tensorflow as tf
import tqdm
from PIL import Image
RESOLUTION = 256
def load_sidewalks_dataset(args):
hf_dataset_identifier = "segments/sidewalk-semantic"
ds = datasets.load_dataset(hf_dataset_identifier)
ds = ds.shuffle(seed=1)
ds = ds["train"].train_test_split(test_size=args.split, seed=args.seed)
train_ds = ds["train"]
val_ds = ds["test"]
return train_ds, val_ds
def resize_img(
image: tf.Tensor, label: tf.Tensor, resize: int
) -> Tuple[tf.Tensor, tf.Tensor]:
image = tf.image.resize(image, (resize, resize))
label = tf.image.resize(label[..., None], (resize, resize))
label = tf.squeeze(label, -1)
return image, label
def process_image(
image: Image, label: Image, resize: int
) -> Tuple[tf.Tensor, tf.Tensor]:
image = np.array(image)
label = np.array(label)
image = tf.convert_to_tensor(image)
label = tf.convert_to_tensor(label)
if resize:
image, label = resize_img(image, label, resize)
return image, label
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def _float_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def create_tfrecord(image: Image, label: Image, resize: int):
image, label = process_image(image, label, resize)
image_dims = image.shape
label_dims = label.shape
image = tf.reshape(image, [-1]) # flatten to 1D array
label = tf.reshape(label, [-1]) # flatten to 1D array
return tf.train.Example(
features=tf.train.Features(
feature={
"image": _float_feature(image.numpy()),
"image_shape": _int64_feature(
[image_dims[0], image_dims[1], image_dims[2]]
),
"label": _float_feature(label.numpy()),
"label_shape": _int64_feature([label_dims[0], label_dims[1]]),
}
)
).SerializeToString()
def write_tfrecords(root_dir, dataset, split, batch_size, resize):
print(f"Preparing TFRecords for split: {split}.")
for step in tqdm.tnrange(int(math.ceil(len(dataset) / batch_size))):
temp_ds = dataset[step * batch_size : (step + 1) * batch_size]
shard_size = len(temp_ds["pixel_values"])
filename = os.path.join(
root_dir, "{}-{:02d}-{}.tfrec".format(split, step, shard_size)
)
with tf.io.TFRecordWriter(filename) as out_file:
for i in range(shard_size):
image = temp_ds["pixel_values"][i]
label = temp_ds["label"][i]
example = create_tfrecord(image, label, resize)
out_file.write(example)
print("Wrote file {} containing {} records".format(filename, shard_size))
def main(args):
train_ds, val_ds = load_sidewalks_dataset(args)
print("Dataset loaded from HF.")
if not os.path.exists(args.root_tfrecord_dir):
os.makedirs(args.root_tfrecord_dir, exist_ok=True)
print(args.resize)
write_tfrecords(
args.root_tfrecord_dir, train_ds, "train", args.batch_size, args.resize
)
write_tfrecords(args.root_tfrecord_dir, val_ds, "val", args.batch_size, args.resize)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--split", help="Train and test split.", default=0.2, type=float
)
parser.add_argument(
"--seed",
help="Seed to be used while performing train-test splits.",
default=2022,
type=int,
)
parser.add_argument(
"--root_tfrecord_dir",
help="Root directory where the TFRecord shards will be serialized.",
default="sidewalks-tfrecords",
type=str,
)
parser.add_argument(
"--batch_size",
help="Number of samples to process in a batch before serializing a single TFRecord shard.",
default=32,
type=int,
)
parser.add_argument(
"--resize",
help="Width and height size the image will be resized to. No resizing will be applied when this isn't set.",
type=int,
)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
main(args)
|
deep-diver/semantic-segmentation-ml-pipeline | tfrecords/create_tfrecords_pets.py | """
Script to generate TFRecord shards from the Pets dataset as shown in this
tutorial: https://keras.io/examples/vision/oxford_pets_image_segmentation/.
The recommended way to obtain TFRecord shards is via an Apache Beam
Pipeline with an execution runner like Dataflow. Example:
https://github.com/GoogleCloudPlatform/practical-ml-vision-book/blob/master/05_create_dataset/jpeg_to_tfrecord.py.
Usage:
python create_tfrecords_pets.py --batch_size 64
python create_tfrecords_pets.py --resize 128 # without --resize flag, no resizing is applied
References:
* https://github.com/GoogleCloudPlatform/practical-ml-vision-book/blob/master/05_create_dataset/05_split_tfrecord.ipynb
* https://www.tensorflow.org/tutorials/images/segmentation
* https://keras.io/examples/vision/oxford_pets_image_segmentation/
"""
import argparse
import os
import random
from typing import List, Tuple
import numpy as np
import tensorflow as tf
import tqdm
from PIL import Image
RESOLUTION = 128
SEED = 2022
def load_paths(args) -> Tuple[List[str], List[str]]:
input_img_paths = sorted(
[
os.path.join(args.input_dir, fname)
for fname in os.listdir(args.input_dir)
if fname.endswith(".jpg")
]
)
target_img_paths = sorted(
[
os.path.join(args.target_dir, fname)
for fname in os.listdir(args.target_dir)
if fname.endswith(".png") and not fname.startswith(".")
]
)
return input_img_paths, target_img_paths
def resize_img(
image: tf.Tensor, label: tf.Tensor, resize: int
) -> Tuple[tf.Tensor, tf.Tensor]:
image = tf.image.resize(image, (resize, resize))
label = tf.image.resize(label[..., None], (resize, resize))
label = tf.squeeze(label, -1)
label -= 1
return image, label
def process_image(
image_path: str, label_path: str, resize: int
) -> Tuple[tf.Tensor, tf.Tensor]:
image = Image.open(image_path).convert("RGB")
label = Image.open(label_path).convert("L")
image = np.array(image)
label = np.array(label)
image = tf.convert_to_tensor(image)
label = tf.convert_to_tensor(label)
if resize:
image, label = resize_img(image, label, resize)
return image, label
def split_paths(img_paths: List[str], target_paths: List[str], split: float):
val_samples = int(len(img_paths) * split)
random.Random(SEED).shuffle(img_paths)
random.Random(SEED).shuffle(target_paths)
train_img_paths = img_paths[:-val_samples]
train_target_paths = target_paths[:-val_samples]
val_img_paths = img_paths[-val_samples:]
val_target_paths = target_paths[-val_samples:]
return train_img_paths, train_target_paths, val_img_paths, val_target_paths
def prepare_tf_dataset(
img_paths: List[str], target_paths: List[str], batch_size: int
) -> tf.data.Dataset:
tf_dataset = tf.data.Dataset.from_tensor_slices((img_paths, target_paths))
return tf_dataset.batch(batch_size)
def get_tf_datasets(args) -> Tuple[tf.data.Dataset, tf.data.Dataset]:
input_img_paths, target_img_paths = load_paths(args)
train_img_paths, train_target_paths, val_img_paths, val_target_paths = split_paths(
input_img_paths, target_img_paths, args.split
)
train_ds = prepare_tf_dataset(train_img_paths, train_target_paths, args.batch_size)
val_ds = prepare_tf_dataset(val_img_paths, val_target_paths, args.batch_size)
return train_ds, val_ds
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def _float_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def create_tfrecord(image: Image, label: Image, resize: int):
image, label = process_image(image, label, resize)
image_dims = image.shape
label_dims = label.shape
image = tf.reshape(image, [-1]) # flatten to 1D array
label = tf.reshape(label, [-1]) # flatten to 1D array
return tf.train.Example(
features=tf.train.Features(
feature={
"image": _float_feature(image.numpy()),
"image_shape": _int64_feature(
[image_dims[0], image_dims[1], image_dims[2]]
),
"label": _float_feature(label.numpy()),
"label_shape": _int64_feature([label_dims[0], label_dims[1]]),
}
)
).SerializeToString()
def write_tfrecords(root_dir: str, dataset: tf.data.Dataset, split: str, resize: int):
print(f"Preparing TFRecords for split: {split}.")
for shard, (image_paths, label_paths) in enumerate(tqdm.tqdm(dataset)):
shard_size = image_paths.numpy().shape[0]
filename = os.path.join(
root_dir, "{}-{:02d}-{}.tfrec".format(split, shard, shard_size)
)
with tf.io.TFRecordWriter(filename) as out_file:
for i in range(shard_size):
img_path = image_paths[i]
label_path = label_paths[i]
example = create_tfrecord(img_path.numpy(), label_path.numpy(), resize)
out_file.write(example)
print("Wrote file {} containing {} records".format(filename, shard_size))
def main(args):
train_ds, val_ds = get_tf_datasets(args)
print("TensorFlow datasets loaded.")
if not os.path.exists(args.root_tfrecord_dir):
os.makedirs(args.root_tfrecord_dir, exist_ok=True)
write_tfrecords(args.root_tfrecord_dir, train_ds, "train", args.resize)
write_tfrecords(args.root_tfrecord_dir, val_ds, "val", args.resize)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--split", help="Train and test split.", default=0.2, type=float)
parser.add_argument(
"--input_dir",
help="Path to the directory containing all images.",
default="images/",
type=str,
)
parser.add_argument(
"--target_dir",
help="Path to the directory containing all targets.",
default="annotations/trimaps/",
type=str,
)
parser.add_argument(
"--root_tfrecord_dir",
help="Root directory where the TFRecord shards will be serialized.",
default="pets-tfrecords",
type=str,
)
parser.add_argument(
"--batch_size",
help="Number of samples to process in a batch before serializing a single TFRecord shard.",
default=32,
type=int,
)
parser.add_argument(
"--resize",
help="Width and height size the image will be resized to. No resizing will be applied when this isn't set.",
type=int,
)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
main(args)
|
deep-diver/semantic-segmentation-ml-pipeline | tfrecords/create_tfrecords_str.py | """
Script to generate TFRecord shards from the Sidewalks dataset as shown in this
blog post: https://huggingface.co./blog/fine-tune-segformer.
The recommended way to obtain TFRecord shards is via an Apache Beam
Pipeline with an execution runner like Dataflow. Example:
https://github.com/GoogleCloudPlatform/practical-ml-vision-book/blob/master/05_create_dataset/jpeg_to_tfrecord.py.
Usage:
python create_tfrecords --batch_size 16
References:
* https://github.com/sayakpaul/TF-2.0-Hacks/blob/master/Cats_vs_Dogs_TFRecords.ipynb
* https://www.tensorflow.org/tutorials/images/segmentation
"""
import argparse
import math
import os
from typing import Tuple
import datasets
import numpy as np
import tensorflow as tf
import tqdm
from PIL import Image
RESOLUTION = 256
def load_sidewalks_dataset(args):
hf_dataset_identifier = "segments/sidewalk-semantic"
ds = datasets.load_dataset(hf_dataset_identifier)
ds = ds.shuffle(seed=1)
ds = ds["train"].train_test_split(test_size=args.split, seed=args.seed)
train_ds = ds["train"]
val_ds = ds["test"]
return train_ds, val_ds
def resize_img(image: tf.Tensor, label: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
image = tf.image.resize(image, (RESOLUTION, RESOLUTION))
label = tf.image.resize(label[..., None], (RESOLUTION, RESOLUTION))
label = tf.squeeze(label, -1)
return image, label
def process_image(image: Image, label: Image) -> Tuple[tf.Tensor, tf.Tensor]:
image = np.array(image)
label = np.array(label)
image = tf.convert_to_tensor(image)
label = tf.convert_to_tensor(label)
image, label = resize_img(image, label)
image, label = normalize_img(image, label)
return image, label
def _bytestring_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
def create_tfrecord(image: Image, label: Image):
image, label = process_image(image, label)
image = tf.io.serialize_tensor(image)
label = tf.io.serialize_tensor(label)
return tf.train.Example(
features=tf.train.Features(
feature={
"image": _bytestring_feature([image.numpy()]),
"label": _bytestring_feature([label.numpy()]),
}
)
).SerializeToString()
def write_tfrecords(root_dir, dataset, split, batch_size):
print(f"Preparing TFRecords for split: {split}.")
for step in tqdm.tnrange(int(math.ceil(len(dataset) / batch_size))):
temp_ds = dataset[step * batch_size : (step + 1) * batch_size]
shard_size = len(temp_ds["pixel_values"])
filename = os.path.join(
root_dir, "{}-{:02d}-{}.tfrec".format(split, step, shard_size)
)
with tf.io.TFRecordWriter(filename) as out_file:
for i in range(shard_size):
image = temp_ds["pixel_values"][i]
label = temp_ds["label"][i]
example = create_tfrecord(image, label)
out_file.write(example)
print("Wrote file {} containing {} records".format(filename, shard_size))
def main(args):
train_ds, val_ds = load_sidewalks_dataset(args)
print("Dataset loaded from HF.")
if not os.path.exists(args.root_tfrecord_dir):
os.makedirs(args.root_tfrecord_dir, exist_ok=True)
write_tfrecords(args.root_tfrecord_dir, train_ds, "train", args.batch_size)
write_tfrecords(args.root_tfrecord_dir, val_ds, "val", args.batch_size)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--split", help="Train and test split.", default=0.2, type=float
)
parser.add_argument(
"--seed",
help="Seed to be used while performing train-test splits.",
default=2022,
type=int,
)
parser.add_argument(
"--root_tfrecord_dir",
help="Root directory where the TFRecord shards will be serialized.",
default="sidewalks-tfrecords",
type=str,
)
parser.add_argument(
"--batch_size",
help="Number of samples to process in a batch before serializing a single TFRecord shard.",
default=32,
type=int,
)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
main(args)
|
deep-diver/semantic-segmentation-ml-pipeline | training_pipeline/__init__.py | # Copyright 2020 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
|
deep-diver/semantic-segmentation-ml-pipeline | training_pipeline/data_validation.ipynb | # import required libs
import glob
import os
import tensorflow as tf
import tensorflow_data_validation as tfdv
print('TF version: {}'.format(tf.version.VERSION))
print('TFDV version: {}'.format(tfdv.version.__version__))# Read artifact information from metadata store.
import beam_dag_runner
from tfx.orchestration import metadata
from tfx.types import standard_artifacts
metadata_connection_config = metadata.sqlite_metadata_connection_config(
beam_dag_runner.METADATA_PATH)
with metadata.Metadata(metadata_connection_config) as store:
stats_artifacts = store.get_artifacts_by_type(standard_artifacts.ExampleStatistics.TYPE_NAME)
schema_artifacts = store.get_artifacts_by_type(standard_artifacts.Schema.TYPE_NAME)
anomalies_artifacts = store.get_artifacts_by_type(standard_artifacts.ExampleAnomalies.TYPE_NAME)# configure output paths
# Exact paths to output artifacts can also be found on KFP Web UI if you are using kubeflow.
stats_path = stats_artifacts[-1].uri
train_stats_file = os.path.join(stats_path, 'train', 'stats_tfrecord')
eval_stats_file = os.path.join(stats_path, 'eval', 'stats_tfrecord')
print("Train stats file:{}, Eval stats file:{}".format(
train_stats_file, eval_stats_file))
schema_file = os.path.join(schema_artifacts[-1].uri, 'schema.pbtxt')
print("Generated schame file:{}".format(schema_file))
anomalies_file = os.path.join(anomalies_artifacts[-1].uri, 'anomalies.pbtxt')
print("Generated anomalies file:{}".format(anomalies_file))# load generated statistics from StatisticsGen
train_stats = tfdv.load_statistics(train_stats_file)
eval_stats = tfdv.load_statistics(eval_stats_file)
tfdv.visualize_statistics(lhs_statistics=eval_stats, rhs_statistics=train_stats,
lhs_name='EVAL_DATASET', rhs_name='TRAIN_DATASET')# load generated schema from SchemaGen
schema = tfdv.load_schema_text(schema_file)
tfdv.display_schema(schema=schema)# load data vaildation result from ExampleValidator
anomalies = tfdv.load_anomalies_text(anomalies_file)
tfdv.display_anomalies(anomalies) |