Model authoring guidelines for the inference container - AWS Clean Rooms

Model authoring guidelines for the inference container

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

  • Use the appropriate SageMaker AI inference-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-inference: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 Batch Transform in your AWS account, and on Clean Rooms ML.

    • Clean Rooms ML makes your model artifacts from inference available for use by your inference code via the /opt/ml/model directory in the docker container.

    • Clean Rooms ML splits input by line, uses a MultiRecord batch strategy, and adds a newline character at the end of every transformed record.

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

    • Ensure that you can run a SageMaker AI batch transform 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 transform environment testing, and Clean Rooms ML

      FROM 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-inference:1.12.1-cpu-py38-ubuntu20.04-sagemaker

ENV PYTHONUNBUFFERED=1

COPY serve.py /opt/ml/code/serve.py
COPY inference_handler.py /opt/ml/code/inference_handler.py
COPY handler_service.py /opt/ml/code/handler_service.py
COPY model.py /opt/ml/code/model.py

RUN chmod +x /opt/ml/code/serve.py

ENTRYPOINT ["/opt/ml/code/serve.py"]

  • 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 Docker
aws ecr get-login-password --region $REGION | docker login --username AWS --password-stdin $ACCOUNT_ID.dkr.ecr.$REGION.amazonaws.com

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

# Create Sagemaker Model
# Configure the create_model.json with
# 1. Primary container - 
    # a. ModelDataUrl - S3 Uri of the model.tar from your training job
aws sagemaker create-model --cli-input-json file://create_model.json --region $REGION

# Create Sagemaker Transform Job
# Configure the transform_job.json with
# 1. Model created in the step above 
# 2. MultiRecord batch strategy
# 3. Line SplitType for TransformInput
# 4. AssembleWith Line for TransformOutput
aws sagemaker create-transform-job --cli-input-json file://transform_job.json --region $REGION

    After the SageMaker AI job is complete and you are satisfied with your batch transform, 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.