ContainerDefinition

class aws_cdk.aws_stepfunctions_tasks.ContainerDefinition(*, container_host_name=None, environment_variables=None, image=None, mode=None, model_package_name=None, model_s3_location=None)

Bases: object

(experimental) Describes the container, as part of model definition.

See

https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ContainerDefinition.html

Stability

experimental

Parameters
  • container_host_name (Optional[str]) – (experimental) This parameter is ignored for models that contain only a PrimaryContainer. When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. Default: - None

  • environment_variables (Optional[TaskInput]) – (experimental) The environment variables to set in the Docker container. Default: - No variables

  • image (Optional[DockerImage]) – (experimental) The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored. Default: - None

  • mode (Optional[Mode]) – (experimental) Defines how many models the container hosts. Default: - Mode.SINGLE_MODEL

  • model_package_name (Optional[str]) – (experimental) The name or Amazon Resource Name (ARN) of the model package to use to create the model. Default: - None

  • model_s3_location (Optional[S3Location]) – (experimental) The 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 S3 path is required for Amazon SageMaker built-in algorithms, but not if you use your own algorithms. Default: - None

Stability

experimental

Methods

bind(task)

(experimental) Called when the ContainerDefinition type configured on Sagemaker Task.

Parameters

task (ISageMakerTask) –

Stability

experimental

Return type

ContainerDefinitionConfig