Documentation

Describes the Docker container for the model package.

See: newModelPackageContainerDefinition smart constructor.

Create a value of ModelPackageContainerDefinition with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:containerHostname:ModelPackageContainerDefinition', modelPackageContainerDefinition_containerHostname - The DNS host name for the Docker container.

$sel:environment:ModelPackageContainerDefinition', modelPackageContainerDefinition_environment - The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

$sel:framework:ModelPackageContainerDefinition', modelPackageContainerDefinition_framework - The machine learning framework of the model package container image.

$sel:frameworkVersion:ModelPackageContainerDefinition', modelPackageContainerDefinition_frameworkVersion - The framework version of the Model Package Container Image.

$sel:imageDigest:ModelPackageContainerDefinition', modelPackageContainerDefinition_imageDigest - An MD5 hash of the training algorithm that identifies the Docker image used for training.

$sel:modelDataUrl:ModelPackageContainerDefinition', modelPackageContainerDefinition_modelDataUrl - The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

The model artifacts must be in an S3 bucket that is in the same region as the model package.

$sel:modelInput:ModelPackageContainerDefinition', modelPackageContainerDefinition_modelInput - A structure with Model Input details.

$sel:nearestModelName:ModelPackageContainerDefinition', modelPackageContainerDefinition_nearestModelName - The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.

$sel:productId:ModelPackageContainerDefinition', modelPackageContainerDefinition_productId - The Amazon Web Services Marketplace product ID of the model package.

$sel:image:ModelPackageContainerDefinition', modelPackageContainerDefinition_image - The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

The DNS host name for the Docker container.

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

The machine learning framework of the model package container image.

The framework version of the Model Package Container Image.

An MD5 hash of the training algorithm that identifies the Docker image used for training.

The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

The model artifacts must be in an S3 bucket that is in the same region as the model package.

A structure with Model Input details.

The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.

The Amazon Web Services Marketplace product ID of the model package.

The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.