Getting started with AWS Trainium and Hugging Face Transformers

This tutorial is available in two different formats, as web page and notebook version.

This guide will help you to get started with AWS Trainium and Hugging Face Transformers. It will cover how to set up a Trainium instance on AWS, load & fine-tune a transformers model for text-classification.

You will learn how to:

1. Setup AWS environment
2. Load and process the dataset
3. Fine-tune BERT using Hugging Face Transformers and Optimum Neuron

Before we can start, make sure you have a Hugging Face Account to save artifacts and experiments.

Quick intro: AWS Trainium

AWS Trainium (Trn1) is a purpose-built EC2 for deep learning (DL) training workloads. Trainium is the successor of AWS Inferentia focused on high-performance training workloads claiming up to 50% cost-to-train savings over comparable GPU-based instances.

Trainium has been optimized for training natural language processing, computer vision, and recommender models used. The accelerator supports a wide range of data types, including FP32, TF32, BF16, FP16, UINT8, and configurable FP8.

The biggest Trainium instance, the trn1.32xlarge comes with over 500GB of memory, making it easy to fine-tune ~10B parameter models on a single instance. Below you will find an overview of the available instance types. More details here:

Now we know what Trainium offers, let’s get started. 🚀

Note: This tutorial was created on a trn1.2xlarge AWS EC2 Instance.

1. Setup AWS environment

In this tutorial, we will use the trn1.2xlarge instance on AWS with 1 Accelerator, including two Neuron Cores and the Hugging Face Neuron Deep Learning AMI.

Once the instance is up and running, we can ssh into it. But instead of developing inside a terminal we want to use a Jupyter environment, which we can use for preparing our dataset and launching the training. For this, we need to add a port for forwarding in the ssh command, which will tunnel our localhost traffic to the Trainium instance.

PUBLIC_DNS="" # IP address, e.g. ec2-3-80-....
KEY_PATH="" # local path to key, e.g. ssh/trn.pem

ssh -L 8080:localhost:8080 -i ${KEY_NAME}.pem ubuntu@$PUBLIC_DNS

We need to make sure we have the training extra installed, to get all the necessary dependencies:

python -m pip install .[training]

We can now start our jupyter server.

python -m notebook --allow-root --port=8080

You should see a familiar jupyter output with a URL to the notebook.

http://localhost:8080/?token=8c1739aff1755bd7958c4cfccc8d08cb5da5234f61f129a9

We can click on it, and a jupyter environment opens in our local browser.

We are going to use the Jupyter environment only for preparing the dataset and then torchrun for launching our training script on both neuron cores for distributed training. Lets create a new notebook and get started.

2. Load and process the dataset

We are training a Text Classification model on the emotion dataset to keep the example straightforward. The emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.

We will use the load_dataset() method from the 🤗 Datasets library to load the emotion.

from datasets import load_dataset


# Dataset id from huggingface.co/dataset
dataset_id = "dair-ai/emotion"

# Load raw dataset
raw_dataset = load_dataset(dataset_id)

print(f"Train dataset size: {len(raw_dataset['train'])}")
print(f"Test dataset size: {len(raw_dataset['test'])}")

# Train dataset size: 16000
# Test dataset size: 2000

Let’s check out an example of the dataset.

from random import randrange


random_id = randrange(len(raw_dataset["train"]))
raw_dataset["train"][random_id]
# {'text': 'i also like to listen to jazz whilst painting it makes me feel more artistic and ambitious actually look to the rainbow', 'label': 1}

We must convert our “Natural Language” to token IDs to train our model. This is done by a Tokenizer, which tokenizes the inputs (including converting the tokens to their corresponding IDs in the pre-trained vocabulary). if you want to learn more about this, out chapter 6 of the Hugging Face Course.

In order to avoid graph recompilation, inputs should have a fixed shape. We need to truncate or pad all samples to the same length.

import os

from transformers import AutoTokenizer


# Model id to load the tokenizer
model_id = "bert-base-uncased"

# Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)


# Tokenize helper function
def tokenize(batch):
    return tokenizer(batch["text"], padding="max_length", truncation=True, return_tensors="pt")


def tokenize_function(example):
    return tokenizer(
        example["text"],
        padding="max_length",
        truncation=True,
    )


# Tokenize dataset
tokenized_emotions = raw_dataset.map(tokenize, batched=True, remove_columns=["text"])

3. Fine-tune BERT using Hugging Face Transformers

We can use the Trainer and TrainingArguments to fine-tune PyTorch-based transformer models.

We prepared a simple train.py training script to perform training and evaluation on the dataset. Below is an excerpt:

from transformers import Trainer, TrainingArguments

def parse_args():
	...

def training_function(args):

    ...

    # Download the model from huggingface.co/models
    model = AutoModelForSequenceClassification.from_pretrained(
        args.model_id, num_labels=num_labels, label2id=label2id, id2label=id2label
    )

    training_args = TrainingArguments(
			...
    )

    # Create Trainer instance
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_emotions["train"],
        eval_dataset=tokenized_emotions["validation"],
        processing_class=tokenizer,
    )


    # Start training
    trainer.train()

We can load the training script into our environment using the wget command or manually copy it into the notebook from here.

!wget https://raw.githubusercontent.com/huggingface/optimum-neuron/main/notebooks/text-classification/scripts/train.py

We will use torchrun to launch our training script on both neuron cores for distributed training, thus allowing data parallelism. torchrun is a tool that automatically distributes a PyTorch model across multiple accelerators. We can pass the number of accelerators as nproc_per_node arguments alongside our hyperparameters.

We’ll use the following command to launch training:

!torchrun --nproc_per_node=2 train.py \
 --model_id bert-base-uncased \
 --lr 5e-5 \
 --per_device_train_batch_size 8 \
 --bf16 True \
 --epochs 3

After compilation, it will only take few minutes to complete the training.

***** train metrics *****
  epoch                    =        3.0
  eval_loss                =     0.1761
  eval_runtime             = 0:00:03.73
  eval_samples_per_second  =    267.956
  eval_steps_per_second    =     16.881
  total_flos               =  1470300GF
  train_loss               =     0.2024
  train_runtime            = 0:07:27.14
  train_samples_per_second =     53.674
  train_steps_per_second   =      6.709

Last but not least, terminate the EC2 instance to avoid unnecessary charges. Looking at the price-performance, our training only costs 20ct (1.34$/h * 0.13h = 0.18$)