Model authoring guidelines for the training container - AWS Clean Rooms

Model authoring guidelines for the training container

This section details the guidelines that model providers should follow when creating a custom ML model algorithm for Clean Rooms ML.

  • Use the appropriate SageMaker AI training-supported container base image, as described in the SageMaker AI Developer Guide. The following code allows you to pull the supported container base images from public SageMaker AI endpoints.

    ecr_registry_endpoint='763104351884.dkr.ecr.$REGION.amazonaws.com'
base_image='pytorch-training:2.3.0-cpu-py311-ubuntu20.04-sagemaker'
aws ecr get-login-password --region $REGION | docker login --username AWS --password-stdin $ecr_registry_endpoint
docker pull $ecr_registry_endpoint/$base_image

  • When authoring the model locally, ensure the following so that you can test your model locally, on a development instance, on SageMaker AI Training in your AWS account, and on Clean Rooms ML.

    • We recommend writing a training script that accesses useful properties about the training environment through various environment variables. Clean Rooms ML uses the following arguments to invoke training on your model code: SM_MODEL_DIR, SM_OUTPUT_DIR, SM_CHANNEL_TRAIN, and FILE_FORMAT. These defaults are used by Clean Rooms ML to train your ML model in its own execution environment with the data from all parties.

    • Clean Rooms ML makes your training input channels available via the /opt/ml/input/data/channel-name directories in the docker container. Each ML input channel is mapped based on its corresponding channel_name provided in the CreateTrainedModel request.

      parser = argparse.ArgumentParser()# Data, model, and output directories

parser.add_argument('--model_dir', type=str, default=os.environ.get('SM_MODEL_DIR', "/opt/ml/model"))
parser.add_argument('--output_dir', type=str, default=os.environ.get('SM_OUTPUT_DIR', "/opt/ml/output/data"))
parser.add_argument('--train_dir', type=str, default=os.environ.get('SM_CHANNEL_TRAIN', "/opt/ml/input/data/train"))
parser.add_argument('--train_file_format', type=str, default=os.environ.get('FILE_FORMAT', "csv"))

    • Ensure that you are able to generate a synthetic or test dataset based on the schema of the collaborators that will be used in your model code.

    • Ensure that you can run a SageMaker AI training job on your own AWS account before you associate the model algorithm with a AWS Clean Rooms collaboration.

      The following code contains a sample Docker file that is compatible with local testing, SageMaker AI Training environment testing, and Clean Rooms ML

      FROM  763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.3.0-cpu-py311-ubuntu20.04-sagemaker
MAINTAINER $author_name

ENV PYTHONDONTWRITEBYTECODE=1 \
    PYTHONUNBUFFERED=1 \
    LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/lib"

ENV PATH="/opt/ml/code:${PATH}"

# this environment variable is used by the SageMaker PyTorch container to determine our user code directory
ENV SAGEMAKER_SUBMIT_DIRECTORY /opt/ml/code

# copy the training script inside the container
COPY train.py /opt/ml/code/train.py
# define train.py as the script entry point
ENV SAGEMAKER_PROGRAM train.py
ENTRYPOINT ["python", "/opt/ml/code/train.py"]

  • To best monitor container failures, we recommend catching exceptions or handling all failure modes in your code and writing them to /opt/ml/output/failure. In a GetTrainedModel response, Clean Rooms ML returns the first 1024 characters from this file under StatusDetails.

  • After you have completed any model changes and you are ready to test it in the SageMaker AI environment, run the following commands in the order provided.

    export ACCOUNT_ID=xxx
export REPO_NAME=xxx
export REPO_TAG=xxx
export REGION=xxx

docker build -t $ACCOUNT_ID.dkr.ecr.us-west-2.amazonaws.com/$REPO_NAME:$REPO_TAG

# Sign into AWS $ACCOUNT_ID/ Run aws configure
# Check the account and make sure it is the correct role/credentials
aws sts get-caller-identity
aws ecr create-repository --repository-name $REPO_NAME --region $REGION
aws ecr describe-repositories --repository-name $REPO_NAME --region $REGION

# Authenticate Doker
aws ecr get-login-password --region $REGION | docker login --username AWS --password-stdin $ACCOUNT_ID.dkr.ecr.$REGION.amazonaws.com

# Push To ECR Images
docker push  $ACCOUNT_ID.dkr.ecr.$REGION.amazonaws.com$REPO_NAME:$REPO_TAG

# Create Sagemaker Training job
# Configure the training_job.json with
# 1. TrainingImage
# 2. Input DataConfig
# 3. Output DataConfig
aws sagemaker create-training-job --cli-input-json file://training_job.json --region $REGION

    After the SageMaker AI job is complete and you are satisfied with your model algorithm, you can register the Amazon ECR Registry with AWS Clean Rooms ML. Use the CreateConfiguredModelAlgorithm action to register the model algorithm and the CreateConfiguredModelAlgorithmAssociation to associate it to a collaboration.