Amazon SageMaker
Developer Guide

Use Your Own Inference Code with Batch Transform

This section explains how Amazon SageMaker interacts with a Docker container that runs your own inference code for batch transform. Use this information to write inference code and create a Docker image.

How Amazon SageMaker Runs Your Inference Image

To configure a container to run as an executable, use an ENTRYPOINT instruction in a Dockerfile. Note the following:

  • For batch transforms, Amazon SageMaker runs the container as:

    docker run image serve

    Amazon SageMaker overrides default CMD statements in a container by specifying the serve argument after the image name. The serve argument overrides arguments that you provide with the CMD command in the Dockerfile.

     

  • We recommend that you use the exec form of the ENTRYPOINT instruction:

    ENTRYPOINT ["executable", "param1", "param2"]

    For example:

    ENTRYPOINT ["python", "k_means_inference.py"]

     

  • Amazon SageMaker sets environment variables specified in CreateModel and CreateTransformJob on your container. Additionally, the following environment variables will be populated:

    • SAGEMAKER_BATCH is always set to true when the container runs in Batch Transform.

    • SAGEMAKER_MAX_PAYLOAD_IN_MB is set to the largest size payload that will be sent to the container via HTTP.

    • SAGEMAKER_BATCH_STRATEGY will be set to SINGLE_RECORD when the container will be sent a single record per call to invocations and MULTI_RECORD when the container will get as many records as will fit in the payload.

    • SAGEMAKER_MAX_CONCURRENT_TRANSFORMS is set to the maximum number of /invocations requests that can be opened simultaneously.

    Note

    The last three environment variables come from the API call made by the user. If the user doesn’t set values for them, they aren't passed. In that case, either the default values or the values requested by the algorithm (in response to the /execution-parameters) are used.

  • If you plan to use GPU devices for model inferences (by specifying GPU-based ML compute instances in your CreateTransformJob request), make sure that your containers are nvidia-docker compatible. Don't bundle NVIDIA drivers with the image. For more information about nvidia-docker, see NVIDIA/nvidia-docker.

     

  • You can't use the init initializer as your entry point in Amazon SageMaker containers because it gets confused by the train and serve arguments.

How Amazon SageMaker Loads Your Model Artifacts

In a CreateModel request, container definitions includes the ModelDataUrl parameter, which identifies the location in Amazon S3 where model artifacts are stored. When you use Amazon SageMaker to run inferences, it uses this information to determine where to copy the model artifacts from. It copies the artifacts to the /opt/ml/model directory in the Docker container for use by your inference code.

The ModelDataUrl parameter must point to a tar.gz file. Otherwise, Amazon SageMaker can't download the file. If you train a model in Amazon SageMaker, it saves the artifacts as a single compressed tar file in Amazon S3. If you train a model in another framework, you need to store the model artifacts in Amazon S3 as a compressed tar file. Amazon SageMaker decompresses this tar file and saves it in the /opt/ml/model directory in the container before the batch transform job starts.

How Containers Serve Requests

Containers must implement a web server that responds to invocations and ping requests on port 8080. For batch transforms,you have the option to set algorithms to implement execution-parameters requests to provide a dynamic runtime configuration to Amazon SageMaker. Amazon SageMaker uses the following endpoints:

  • ping—Used to periodically check the health of the container. Amazon SageMaker waits for an HTTP 200 status code and an empty body for a successful ping request before sending an invocations request. You might use a ping request to load a model into memory to generate inference when invocations requests are sent.

  • (Optional) execution-parameters—Allows the algorithm to provide the optimal tuning parameters for a job during runtime. Based on the memory and CPUs available for a container, the algorithm chooses the appropriate MaxConcurrentTransforms, BatchStrategy, and MaxPayloadInMB values for the job.

Before calling the invocations request, Amazon SageMaker attempts to invoke the execution-parameters request. When you create a batch transform job, you can provide values for the MaxConcurrentTransforms, BatchStrategy, and MaxPayloadInMB parameters. Amazon SageMaker determines the values for these parameters using this order of precedence:

  1. The parameter values that you provide when you create the CreateTransformJob request,

  2. The values that the model container returns when Amazon SageMaker invokes the execution-parameters endpoint

  3. The parameters default values, listed in the following table.

    Parameter Default Values
    MaxConcurrentTransforms

    1

    BatchStrategy

    MULTI_RECORD

    MaxPayloadInMB

    6

The response for a GET execution-parameters request is a JSON object with keys for MaxConcurrentTransforms, BatchStrategy, and MaxPayloadInMB parameters. This is an example of a valid response:

{ “MaxConcurrentTransforms”: 8, “BatchStrategy": "MULTI_RECORD", "MaxPayloadInMB": 6 }

How Your Container Should Respond to Health Check (Ping) Requests

The simplest requirement on the container is to respond with an HTTP 200 status code and an empty body. This indicates to Amazon SageMaker that the container is ready to accept inference requests at the /invocations endpoint.

While the minimum bar is for the container to return a static 200, a container developer can use this functionality to perform deeper checks. The request timeout on /ping attempts is 2 seconds.