# Model authoring guidelines for the inference container
<a name="inference-model-guidelines"></a>

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](https://docs.aws.amazon.com/sagemaker/latest/dg-ecr-paths/sagemaker-algo-docker-registry-paths.html). 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.