Run Scripts with Your Own Processing Container - Amazon SageMaker

Run Scripts with Your Own Processing Container

You can use scikit-learn scripts to preprocess data and evaluate your models. To see how to run scikit-learn scripts to perform these tasks see the scikit-learn Processing sample notebook. This notebook uses the ScriptProcessor class from the Amazon SageMaker Python SDK for Processing,

The following example shows how to use a ScriptProcessor class to run a Python script with your own image that runs a processing job that processes input data, and saves the processed data in Amazon Simple Storage Service (Amazon S3).

The notebook shows the general workflow for using a ScriptProcessor class.

  1. Create a Docker directory and add the Dockerfile used to create the processing container. Install pandas and scikit-learn into it. (You could also install your own dependencies with a similar RUN command.)

    mkdir docker %%writefile docker/Dockerfile FROM python:3.7-slim-buster RUN pip3 install pandas==0.25.3 scikit-learn==0.21.3 ENV PYTHONUNBUFFERED=TRUE ENTRYPOINT ["python3"]
  2. Build the container using the docker command, creates an Amazon Elastic Container Registry (Amazon ECR) repository, and pushes the image to Amazon ECR.

    import boto3 account_id = boto3.client('sts').get_caller_identity().get('Account') ecr_repository = 'sagemaker-processing-container' tag = ':latest' processing_repository_uri = '{}.dkr.ecr.{}{}'.format(account_id, region, ecr_repository + tag) # Create ECR repository and push docker image !docker build -t $ecr_repository docker !$(aws ecr get-login --region $region --registry-ids $account_id --no-include-email) !aws ecr create-repository --repository-name $ecr_repository !docker tag {ecr_repository + tag} $processing_repository_uri !docker push $processing_repository_uri
  3. Set up the ScriptProcessor from the Amazon SageMaker Python SDK to run the script.

    from sagemaker.processing import ScriptProcessor, ProcessingInput, ProcessingOutput script_processor = ScriptProcessor(command=['python3'], image_uri='<image_uri>', role='<role_arn>', instance_count=1, instance_type='ml.m5.xlarge')
  4. Run the script.'', inputs=[ProcessingInput( source='s3://path/to/my/input-data.csv', destination='/opt/ml/processing/input')], outputs=[ProcessingOutput(source='/opt/ml/processing/output/train'), ProcessingOutput(source='/opt/ml/processing/output/validation'), ProcessingOutput(source='/opt/ml/processing/output/test')]

You can use the same procedure with any other library or system dependencies. You can also use existing Docker images, including images that you run on other platforms, such Kubernetes.