NAME
Paws::SageMaker::ContainerDefinition
USAGE
This class represents one of two things:
Arguments in a call to a service
Use the attributes of this class as arguments to methods. You shouldn't make instances of this class. Each attribute should be used as a named argument in the calls that expect this type of object.
As an example, if Att1 is expected to be a Paws::SageMaker::ContainerDefinition object:
$service_obj->Method(Att1 => { ContainerHostname => $value, ..., MultiModelConfig => $value });Results returned from an API call
Use accessors for each attribute. If Att1 is expected to be an Paws::SageMaker::ContainerDefinition object:
$result = $service_obj->Method(...);
$result->Att1->ContainerHostnameDESCRIPTION
Describes the container, as part of model definition.
ATTRIBUTES
ContainerHostname => 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. For information, see Use Logs and Metrics to Monitor an Inference Pipeline (https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipeline-logs-metrics.html). 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.
Environment => Paws::SageMaker::EnvironmentMap
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.
Image => Str
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 (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html)
ImageConfig => Paws::SageMaker::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 (https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-containers-inference-private.html)
Mode => Str
Whether the container hosts a single model or multiple models.
ModelDataUrl => Str
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 (https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html).
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, 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 (https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html) 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.
ModelPackageName => Str
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
MultiModelConfig => Paws::SageMaker::MultiModelConfig
Specifies additional configuration for multi-model endpoints.
SEE ALSO
This class forms part of Paws, describing an object used in Paws::SageMaker
BUGS and CONTRIBUTIONS
The source code is located here: https://github.com/pplu/aws-sdk-perl
Please report bugs to: https://github.com/pplu/aws-sdk-perl/issues