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

Class ContainerDefinition

    • Method Detail

      • containerHostname

        public final 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.

        Returns:
        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.

      • image

        public final 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.

        Returns:
        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.

      • imageConfig

        public final 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.

        Returns:
        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.

      • mode

        public final ContainerMode mode()

        Whether the container hosts a single model or multiple models.

        If the service returns an enum value that is not available in the current SDK version, mode will return ContainerMode.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from modeAsString().

        Returns:
        Whether the container hosts a single model or multiple models.
        See Also:
        ContainerMode

      • modeAsString

        public final String modeAsString()

        Whether the container hosts a single model or multiple models.

        If the service returns an enum value that is not available in the current SDK version, mode will return ContainerMode.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from modeAsString().

        Returns:
        Whether the container hosts a single model or multiple models.
        See Also:
        ContainerMode

      • modelDataUrl

        public final 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.

        Returns:
        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.

      • modelDataSource

        public final 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.

        Returns:
        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.

      • hasEnvironment

        public final boolean hasEnvironment()
        For responses, this returns true if the service returned a value for the Environment property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • environment

        public final Map<String,​String> environment()

        The environment variables to set in the Docker container.

        The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasEnvironment() method.

        Returns:
        The environment variables to set in the Docker container.

        The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.

      • modelPackageName

        public final String modelPackageName()

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

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

      • inferenceSpecificationName

        public final String inferenceSpecificationName()

        The inference specification name in the model package version.

        Returns:
        The inference specification name in the model package version.

      • multiModelConfig

        public final MultiModelConfig multiModelConfig()

        Specifies additional configuration for multi-model endpoints.

        Returns:
        Specifies additional configuration for multi-model endpoints.

      • hashCode

        public final int hashCode()
        Overrides:
        hashCode in class Object

      • equals

        public final boolean equals​(Object obj)
        Overrides:
        equals in class Object

      • toString

        public final String toString()
        Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
        Overrides:
        toString in class Object

      • getValueForField

        public final <T> Optional<T> getValueForField​(String fieldName,
                                              Class<T> clazz)