ContainerDefinitionOptions

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

Bases: object

Properties to define a ContainerDefinition.

Parameters:

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

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

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

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

  • model_s3_location (Optional[S3Location]) – 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

See:

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

ExampleMetadata:

infused

Example:

tasks.SageMakerCreateModel(self, "Sagemaker",
    model_name="MyModel",
    primary_container=tasks.ContainerDefinition(
        image=tasks.DockerImage.from_json_expression(sfn.JsonPath.string_at("$.Model.imageName")),
        mode=tasks.Mode.SINGLE_MODEL,
        model_s3_location=tasks.S3Location.from_json_expression("$.TrainingJob.ModelArtifacts.S3ModelArtifacts")
    )
)

Attributes

container_host_name

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

The environment variables to set in the Docker container.

Default:

  • No variables

image

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

Default:

  • None

mode

Defines how many models the container hosts.

Default:

  • Mode.SINGLE_MODEL

model_package_name

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

Default:

  • None

model_s3_location

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