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