[ aws . sagemaker ]

batch-describe-model-package

Description

This action batch describes a list of versioned model packages

See also: AWS API Documentation

Synopsis

  batch-describe-model-package
--model-package-arn-list <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--debug]
[--endpoint-url <value>]
[--no-verify-ssl]
[--no-paginate]
[--output <value>]
[--query <value>]
[--profile <value>]
[--region <value>]
[--version <value>]
[--color <value>]
[--no-sign-request]
[--ca-bundle <value>]
[--cli-read-timeout <value>]
[--cli-connect-timeout <value>]
[--cli-binary-format <value>]
[--no-cli-pager]
[--cli-auto-prompt]
[--no-cli-auto-prompt]

Options

--model-package-arn-list (list)

The list of Amazon Resource Name (ARN) of the model package groups.

(string)

Syntax:

"string" "string" ...

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--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. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command. The generated JSON skeleton is not stable between versions of the AWS CLI and there are no backwards compatibility guarantees in the JSON skeleton generated.

Global Options

--debug (boolean)

Turn on debug logging.

--endpoint-url (string)

Override command’s default URL with the given URL.

--no-verify-ssl (boolean)

By default, the AWS CLI uses SSL when communicating with AWS services. For each SSL connection, the AWS CLI will verify SSL certificates. This option overrides the default behavior of verifying SSL certificates.

--no-paginate (boolean)

Disable automatic pagination. If automatic pagination is disabled, the AWS CLI will only make one call, for the first page of results.

--output (string)

The formatting style for command output.

• json
• text
• table
• yaml
• yaml-stream

--query (string)

A JMESPath query to use in filtering the response data.

--profile (string)

Use a specific profile from your credential file.

--region (string)

The region to use. Overrides config/env settings.

--version (string)

Display the version of this tool.

--color (string)

Turn on/off color output.

• on
• off
• auto

--no-sign-request (boolean)

Do not sign requests. Credentials will not be loaded if this argument is provided.

--ca-bundle (string)

The CA certificate bundle to use when verifying SSL certificates. Overrides config/env settings.

--cli-read-timeout (int)

The maximum socket read time in seconds. If the value is set to 0, the socket read will be blocking and not timeout. The default value is 60 seconds.

--cli-connect-timeout (int)

The maximum socket connect time in seconds. If the value is set to 0, the socket connect will be blocking and not timeout. The default value is 60 seconds.

--cli-binary-format (string)

The formatting style to be used for binary blobs. The default format is base64. The base64 format expects binary blobs to be provided as a base64 encoded string. The raw-in-base64-out format preserves compatibility with AWS CLI V1 behavior and binary values must be passed literally. When providing contents from a file that map to a binary blob fileb:// will always be treated as binary and use the file contents directly regardless of the cli-binary-format setting. When using file:// the file contents will need to properly formatted for the configured cli-binary-format.

• base64
• raw-in-base64-out

--no-cli-pager (boolean)

Disable cli pager for output.

--cli-auto-prompt (boolean)

Automatically prompt for CLI input parameters.

--no-cli-auto-prompt (boolean)

Disable automatically prompt for CLI input parameters.

Output

ModelPackageSummaries -> (map)

The summaries for the model package versions

key -> (string)

value -> (structure)

Provides summary information about the model package.

ModelPackageGroupName -> (string)

The group name for the model package

ModelPackageVersion -> (integer)

The version number of a versioned model.

ModelPackageArn -> (string)

The Amazon Resource Name (ARN) of the model package.

ModelPackageDescription -> (string)

The description of the model package.

CreationTime -> (timestamp)

The creation time of the mortgage package summary.

InferenceSpecification -> (structure)

Defines how to perform inference generation after a training job is run.

Containers -> (list)

The Amazon ECR registry path of the Docker image that contains the inference code.

(structure)

Describes the Docker container for the model package.

ContainerHostname -> (string)

The DNS host name for the Docker container.

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 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 .

ImageDigest -> (string)

An MD5 hash of the training algorithm that identifies the Docker image used for training.

ModelDataUrl -> (string)

The Amazon 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).

Note

The model artifacts must be in an S3 bucket that is in the same region as the model package.

ModelDataSource -> (structure)

Specifies the location of ML model data to deploy during endpoint creation.

S3DataSource -> (structure)

Specifies the S3 location of ML model data to deploy.

S3Uri -> (string)

Specifies the S3 path of ML model data to deploy.

S3DataType -> (string)

Specifies the type of ML model data to deploy.

If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/).

If you choose S3Object , S3Uri identifies an object that is the ML model data to deploy.

CompressionType -> (string)

Specifies how the ML model data is prepared.

If you choose Gzip and choose S3Object as the value of S3DataType , S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

If you choose None and chooose S3Object as the value of S3DataType , S3Uri identifies an object that represents an uncompressed ML model to deploy.

If you choose None and choose S3Prefix as the value of S3DataType , S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

• If you choose S3Object as the value of S3DataType , then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.
• If you choose S3Prefix as the value of S3DataType , then for each S3 object under the key name pefix referenced by S3Uri , SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model ) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.
• Do not use any of the following as file names or directory names:
  • An empty or blank string
  • A string which contains null bytes
  • A string longer than 255 bytes
  • A single dot (. )
  • A double dot (.. )
• Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType , then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).
• Do not organize the model artifacts in S3 console using folders . When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

ModelAccessConfig -> (structure)

Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig . You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

AcceptEula -> (boolean)

Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

HubAccessConfig -> (structure)

Configuration information for hub access.

HubContentArn -> (string)

The ARN of the hub content for which deployment access is allowed.

ManifestS3Uri -> (string)

The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations.

ProductId -> (string)

The Amazon Web Services Marketplace product ID of the model package.

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)

ModelInput -> (structure)

A structure with Model Input details.

DataInputConfig -> (string)

The input configuration object for the model.

Framework -> (string)

The machine learning framework of the model package container image.

FrameworkVersion -> (string)

The framework version of the Model Package Container Image.

NearestModelName -> (string)

The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata .

AdditionalS3DataSource -> (structure)

The additional data source that is used during inference in the Docker container for your model package.

S3DataType -> (string)

The data type of the additional data source that you specify for use in inference or training.

S3Uri -> (string)

The uniform resource identifier (URI) used to identify an additional data source used in inference or training.

CompressionType -> (string)

The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.

SupportedTransformInstanceTypes -> (list)

A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

This parameter is required for unversioned models, and optional for versioned models.

(string)

SupportedRealtimeInferenceInstanceTypes -> (list)

A list of the instance types that are used to generate inferences in real-time.

This parameter is required for unversioned models, and optional for versioned models.

(string)

SupportedContentTypes -> (list)

The supported MIME types for the input data.

(string)

SupportedResponseMIMETypes -> (list)

The supported MIME types for the output data.

(string)

ModelPackageStatus -> (string)

The status of the mortgage package.

ModelApprovalStatus -> (string)

The approval status of the model.

BatchDescribeModelPackageErrorMap -> (map)

A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.

key -> (string)

value -> (structure)

The error code and error description associated with the resource.

ErrorCode -> (string)

ErrorResponse -> (string)