Amazon SageMaker

Hugging Face and Amazon introduced new Hugging Face Deep Learning Containers (DLCs) to make it easier than ever to train Hugging Face Transformer models in Amazon SageMaker.

To learn how to use the new 🤗 DLCs with the Amazon SageMaker to run your 🤗 Accelerate scripts and raw training loops.0

Getting Started

Setup & Installation

Before you can run your 🤗 Accelerate scripts on Amazon SageMaker you need to sign up for an AWS account. If you do not have an AWS account yet learn more here.

After you have your AWS Account you need to install the sagemaker sdk for 🤗 Accelerate with.

pip install "accelerate[sagemaker]" --upgrade

🤗 Accelerate currently uses the 🤗 DLCs, with transformers, datasets and tokenizers pre-installed. 🤗 Accelerate is not in the DLC yet (will soon be added!) so to use it within Amazon SageMaker you need to create a requirements.txt in the same directory where your training script is located and add it as dependency.

accelerate

You should also add any other dependencies you have to this requirements.txt.

Configure 🤗 Accelerate

You can configure the launch configuration for Amazon SageMaker the same as you do for non SageMaker training jobs with the 🤗 Accelerate CLI.

accelerate config
# In which compute environment are you running? ([0] This machine, [1] AWS (Amazon SageMaker)): 1

🤗 Accelerate will go through a questionnaire about your Amazon SageMaker setup and create a config file you can edit.

Note

🤗 Accelerate is not saving any of your credentials.

Prepare a 🤗 Accelerate fine-tuning script

The training script is very similar to a training script you might run outside of SageMaker, but to save your model after training you need to specify either /opt/ml/model or use os.environ["SM_MODEL_DIR"] as your save directory. After training, artifacts in this directory are uploaded to S3.

- torch.save('/opt/ml/model`)
+ accelerator.save('/opt/ml/model')

Warning

SageMaker doesn’t support argparse actions. If you want to use, for example, boolean hyperparameters, you need to specify type as bool in your script and provide an explicit True or False value for this hyperparameter. [REF].

Launch Training

You can launch your training with 🤗 Accelerate CLI with

accelerate launch path_to_script.py --args_to_the_script

This will launch your training script using your configuration. The only thing you have to do is provide all the arguments needed by your training script as named arguments.

Examples

Note

If you run one of the example scripts, don’t forget to add accelerator.save('/opt/ml/model') to it.

accelerate launch ./examples/sagemaker_example.py

Outputs:

Configuring Amazon SageMaker environment
Converting Arguments to Hyperparameters
Creating Estimator
2021-04-08 11:56:50 Starting - Starting the training job...
2021-04-08 11:57:13 Starting - Launching requested ML instancesProfilerReport-1617883008: InProgress
.........
2021-04-08 11:58:54 Starting - Preparing the instances for training.........
2021-04-08 12:00:24 Downloading - Downloading input data
2021-04-08 12:00:24 Training - Downloading the training image..................
2021-04-08 12:03:39 Training - Training image download completed. Training in progress..
........
epoch 0: {'accuracy': 0.7598039215686274, 'f1': 0.8178438661710037}
epoch 1: {'accuracy': 0.8357843137254902, 'f1': 0.882249560632689}
epoch 2: {'accuracy': 0.8406862745098039, 'f1': 0.8869565217391304}
........
2021-04-08 12:05:40 Uploading - Uploading generated training model
2021-04-08 12:05:40 Completed - Training job completed
Training seconds: 331
Billable seconds: 331
You can find your model data at: s3://your-bucket/accelerate-sagemaker-1-2021-04-08-11-56-47-108/output/model.tar.gz

Advanced Features

Distributed Training: Data Parallelism

currently in development, will be supported soon.

Distributed Training: Model Parallelism

currently in development, will be supported soon.

Python packages and dependencies

🤗 Accelerate currently uses the 🤗 DLCs, with transformers, datasets and tokenizers pre-installed. If you want to use different/other Python packages you can do this by adding them to the requirements.txt. These packages will be installed before your training script is started.

Remote scripts: Use scripts located on Github

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Use Spot Instances

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