Package software.amazon.awssdk.services.sagemaker.model

Interface ContainerDefinition.Builder

All Superinterfaces:
Buildable, CopyableBuilder<ContainerDefinition.Builder,ContainerDefinition>, SdkBuilder<ContainerDefinition.Builder,ContainerDefinition>, SdkPojo
Enclosing class:
ContainerDefinition
public static interface ContainerDefinition.Builder extends SdkPojo, CopyableBuilder<ContainerDefinition.Builder,ContainerDefinition>

  • Method Details

    • containerHostname

      ContainerDefinition.Builder containerHostname(String containerHostname)

      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. 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.

      Parameters:
      containerHostname - 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. 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.

      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • image

      ContainerDefinition.Builder image(String 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 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 model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

      Parameters:
      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 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 model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • imageConfig

      ContainerDefinition.Builder imageConfig(ImageConfig imageConfig)

      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.

      The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

      Parameters:
      imageConfig - 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.

      The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • imageConfig

      default ContainerDefinition.Builder imageConfig(Consumer<ImageConfig.Builder> imageConfig)

      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.

      The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

      This is a convenience method that creates an instance of the ImageConfig.Builder avoiding the need to create one manually via ImageConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to imageConfig(ImageConfig).

      Parameters:
      imageConfig - a consumer that will call methods on ImageConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • mode

      ContainerDefinition.Builder mode(String mode)

      Whether the container hosts a single model or multiple models.

      Parameters:
      mode - Whether the container hosts a single model or multiple models.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • mode

      ContainerDefinition.Builder mode(ContainerMode mode)

      Whether the container hosts a single model or multiple models.

      Parameters:
      mode - Whether the container hosts a single model or multiple models.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • modelDataUrl

      ContainerDefinition.Builder modelDataUrl(String modelDataUrl)

      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 SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

      The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

      If you provide a value for this parameter, 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 Amazon Web Services 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 in the Amazon Web Services Identity and Access Management User Guide.

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

      Parameters:
      modelDataUrl - 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 SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

      The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

      If you provide a value for this parameter, 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 Amazon Web Services 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 in the Amazon Web Services Identity and Access Management User Guide.

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

      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • environment

      ContainerDefinition.Builder environment(Map<String,String> 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.

      Parameters:
      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.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • modelPackageName

      ContainerDefinition.Builder modelPackageName(String modelPackageName)

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

      Parameters:
      modelPackageName - The name or Amazon Resource Name (ARN) of the model package to use to create the model.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • inferenceSpecificationName

      ContainerDefinition.Builder inferenceSpecificationName(String inferenceSpecificationName)

      The inference specification name in the model package version.

      Parameters:
      inferenceSpecificationName - The inference specification name in the model package version.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • multiModelConfig

      ContainerDefinition.Builder multiModelConfig(MultiModelConfig multiModelConfig)

      Specifies additional configuration for multi-model endpoints.

      Parameters:
      multiModelConfig - Specifies additional configuration for multi-model endpoints.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • multiModelConfig

      default ContainerDefinition.Builder multiModelConfig(Consumer<MultiModelConfig.Builder> multiModelConfig)

      Specifies additional configuration for multi-model endpoints.

      This is a convenience method that creates an instance of the MultiModelConfig.Builder avoiding the need to create one manually via MultiModelConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to multiModelConfig(MultiModelConfig).

      Parameters:
      multiModelConfig - a consumer that will call methods on MultiModelConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • modelDataSource

      ContainerDefinition.Builder modelDataSource(ModelDataSource modelDataSource)

      Specifies the location of ML model data to deploy.

      Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.

      Parameters:
      modelDataSource - Specifies the location of ML model data to deploy.

      Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.

      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • modelDataSource

      default ContainerDefinition.Builder modelDataSource(Consumer<ModelDataSource.Builder> modelDataSource)

      Specifies the location of ML model data to deploy.

      Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.

      This is a convenience method that creates an instance of the ModelDataSource.Builder avoiding the need to create one manually via ModelDataSource.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to modelDataSource(ModelDataSource).

      Parameters:
      modelDataSource - a consumer that will call methods on ModelDataSource.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also: