#
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.

##
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.

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')

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

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

Set up the accelerate config by running `accelerate config` and answer the SageMaker questions and set it up. To use SageMaker DDP, select it when asked `What is the distributed mode? ([0] No distributed training, [1] data parallelism):`. Example config below:

    base_job_name: accelerate-sagemaker-1
    compute_environment: AMAZON_SAGEMAKER
    distributed_type: DATA_PARALLEL
    ec2_instance_type: ml.p3.16xlarge
    iam_role_name: xxxxx
    image_uri: null
    mixed_precision: fp16
    num_machines: 1
    profile: xxxxx
    py_version: py10
    pytorch_version: 2.5.0
    region: us-east-1
    transformers_version: 4.17.0
    use_cpu: false

###
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.

###
Local Training: SageMaker Local mode

The local mode in the SageMaker SDK allows you to run your training script locally inside the HuggingFace DLC (Deep Learning container) or using your custom container image. This is useful for debugging and testing your training script inside the final container environment. Local mode uses Docker compose (Note: Docker Compose V2 is not supported yet). The SDK will handle the authentication against ECR to pull the DLC to your local environment. You can emulate CPU (single and multi-instance) and GPU (single instance) SageMaker training jobs.

To use local mode, you need to set your `ec2_instance_type` to `local`.

    ec2_instance_type: local

###
Advanced configuration

The configuration allows you to override parameters for the Estimator. These settings have to be applied in the config file and are not part of `accelerate config`. You can control many additional aspects of the training job, e.g. use Spot instances, enable network isolation and many more.

    additional_args:
      # enable network isolation to restrict internet access for containers
      enable_network_isolation: True

You can find all available configuration here.

###
Use Spot Instances

You can use Spot Instances e.g. using (see Advanced configuration):

    additional_args:
      use_spot_instances: True
      max_wait: 86400

Note: Spot Instances are subject to be terminated and training to be continued from a checkpoint. This is not handled in Accelerate out of the box. Contact us if you would like this feature.

###
Remote scripts: Use scripts located on Github

undecided if feature is needed. Contact us if you would like this feature.

< > Update on GitHub