ContainerDefinition

Gets and sets the property AdditionalModelDataSources.

Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.

Gets and sets the property 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.

Gets and sets the property Environment.

The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables.

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.

Gets and sets the property 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.

Gets and sets the property 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.

Gets and sets the property InferenceSpecificationName.

The inference specification name in the model package version.

Gets and sets the property Mode.

Whether the container hosts a single model or multiple models.

Gets and sets the property ModelDataSource.

Specifies the location of ML model data to deploy.

Gets and sets the property 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.

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.

Gets and sets the property ModelPackageName.

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

Gets and sets the property MultiModelConfig.

Specifies additional configuration for multi-model endpoints.