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[ aws . sagemaker ]

describe-model

Description

Describes a model that you created using the create-model API.

See also: AWS API Documentation

See 'aws help' for descriptions of global parameters.

Synopsis

  describe-model
--model-name <value>
[--cli-input-json <value>]
[--generate-cli-skeleton <value>]

Options

--model-name (string)

The name of the model.

--cli-input-json (string) Performs service operation based on the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, the CLI values will override the JSON-provided values.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

See 'aws help' for descriptions of global parameters.

Output

ModelName -> (string)

Name of the Amazon SageMaker model.

PrimaryContainer -> (structure)

The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.

ContainerHostname -> (string)

The DNS host name for the container after Amazon SageMaker deploys it.

Image -> (string)

The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored. 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

ModelDataUrl -> (string)

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).

If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS i an AWS Region in the AWS Identity and Access Management User Guide .

Environment -> (map)

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.

key -> (string)

value -> (string)

ExecutionRoleArn -> (string)

The Amazon Resource Name (ARN) of the IAM role that you specified for the model.

VpcConfig -> (structure)

A VpcConfig object that specifies the VPC that this model has access to. For more information, see host-vpc

SecurityGroupIds -> (list)

The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

(string)

Subnets -> (list)

The ID of the subnets in the VPC to which you want to connect your training job or model.

(string)

CreationTime -> (timestamp)

A timestamp that shows when the model was created.

ModelArn -> (string)

The Amazon Resource Name (ARN) of the model.