class Aws::SageMaker::Types::ContainerDefinition

Describes the container, as part of model definition.

@note When making an API call, you may pass ContainerDefinition

data as a hash:

    {
      container_hostname: "ContainerHostname",
      image: "ContainerImage",
      image_config: {
        repository_access_mode: "Platform", # required, accepts Platform, Vpc
        repository_auth_config: {
          repository_credentials_provider_arn: "RepositoryCredentialsProviderArn", # required
        },
      },
      mode: "SingleModel", # accepts SingleModel, MultiModel
      model_data_url: "Url",
      environment: {
        "EnvironmentKey" => "EnvironmentValue",
      },
      model_package_name: "VersionedArnOrName",
      multi_model_config: {
        model_cache_setting: "Enabled", # accepts Enabled, Disabled
      },
    }

@!attribute [rw] container_hostname

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. For information, see [Use Logs and
Metrics to Monitor an Inference Pipeline][1]. If you don't specify
a value for this parameter for a `ContainerDefinition` that is part
of an inference pipeline, a unique name is automatically assigned
based on the position of the `ContainerDefinition` in the pipeline.
If you specify a value for the `ContainerHostName` for any
`ContainerDefinition` that is part of an inference pipeline, you
must specify a value for the `ContainerHostName` parameter of every
`ContainerDefinition` in that pipeline.

[1]: https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipeline-logs-metrics.html
@return [String]

@!attribute [rw] image

The path where inference code is stored. This can be either in
Amazon EC2 Container Registry or in a Docker registry that is
accessible from the same VPC that you configure for your endpoint.
If you are using your own custom algorithm instead of an algorithm
provided by Amazon SageMaker, the inference code must meet Amazon
SageMaker requirements. Amazon SageMaker supports both
`registry/repository[:tag]` and `registry/repository[@digest]` image
path formats. For more information, see [Using Your Own Algorithms
with Amazon SageMaker][1]

[1]: https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html
@return [String]

@!attribute [rw] image_config

Specifies whether the model container is in Amazon ECR or a private
Docker registry accessible from your Amazon Virtual Private Cloud
(VPC). For information about storing containers in a private Docker
registry, see [Use a Private Docker Registry for Real-Time Inference
Containers][1]

[1]: https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-containers-inference-private.html
@return [Types::ImageConfig]

@!attribute [rw] mode

Whether the container hosts a single model or multiple models.
@return [String]

@!attribute [rw] model_data_url

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. For more information on built-in algorithms, see [Common
Parameters][1].

<note markdown="1"> The model artifacts must be in an S3 bucket that is in the same
region as the model or endpoint you are creating.

 </note>

If you provide a value for this parameter, Amazon SageMaker uses
Amazon Web Services Security Token Service to download model
artifacts from the S3 path you provide. Amazon Web Services STS is
activated in your IAM user account by default. If you previously
deactivated Amazon Web Services STS for a region, you need to
reactivate Amazon Web Services STS for that region. For more
information, see [Activating and Deactivating Amazon Web Services
STS in an Amazon Web Services Region][2] in the *Amazon Web Services
Identity and Access Management User Guide*.

If you use a built-in algorithm to create a model, Amazon SageMaker
requires that you provide a S3 path to the model artifacts in
`ModelDataUrl`.

[1]: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html
[2]: https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html
@return [String]

@!attribute [rw] 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.
@return [Hash<String,String>]

@!attribute [rw] model_package_name

The name or Amazon Resource Name (ARN) of the model package to use
to create the model.
@return [String]

@!attribute [rw] multi_model_config

Specifies additional configuration for multi-model endpoints.
@return [Types::MultiModelConfig]

@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/ContainerDefinition AWS API Documentation

Constants

SENSITIVE