describe-project-versions¶

Description¶

Lists and describes the versions of a model in an Amazon Rekognition Custom Labels project. You can specify up to 10 model versions in ProjectVersionArns . If you don’t specify a value, descriptions for all model versions in the project are returned.

This operation requires permissions to perform the rekognition:DescribeProjectVersions action.

Synopsis¶

  describe-project-versions
--project-arn <value>
[--version-names <value>]
[--cli-input-json | --cli-input-yaml]
[--starting-token <value>]
[--page-size <value>]
[--max-items <value>]
[--generate-cli-skeleton <value>]

Options¶

--project-arn (string)

The Amazon Resource Name (ARN) of the project that contains the models you want to describe.

--version-names (list)

A list of model version names that you want to describe. You can add up to 10 model version names to the list. If you don’t specify a value, all model descriptions are returned. A version name is part of a model (ProjectVersion) ARN. For example, my-model.2020-01-21T09.10.15 is the version name in the following ARN. arn:aws:rekognition:us-east-1:123456789012:project/getting-started/version/*my-model.2020-01-21T09.10.15* /1234567890123 .

(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.

--starting-token (string)

A token to specify where to start paginating. This is the NextToken from a previously truncated response.

For usage examples, see Pagination in the AWS Command Line Interface User Guide .

--page-size (integer)

The size of each page to get in the AWS service call. This does not affect the number of items returned in the command’s output. Setting a smaller page size results in more calls to the AWS service, retrieving fewer items in each call. This can help prevent the AWS service calls from timing out.

For usage examples, see Pagination in the AWS Command Line Interface User Guide .

--max-items (integer)

The total number of items to return in the command’s output. If the total number of items available is more than the value specified, a NextToken is provided in the command’s output. To resume pagination, provide the NextToken value in the starting-token argument of a subsequent command. Do not use the NextToken response element directly outside of the AWS CLI.

For usage examples, see Pagination in the AWS Command Line Interface User Guide .

--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.

See ‘aws help’ for descriptions of global parameters.

Output¶

ProjectVersionDescriptions -> (list)

A list of model descriptions. The list is sorted by the creation date and time of the model versions, latest to earliest.

(structure)

A description of a version of an Amazon Rekognition Custom Labels model.

ProjectVersionArn -> (string)

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

CreationTimestamp -> (timestamp)

The Unix datetime for the date and time that training started.

MinInferenceUnits -> (integer)

The minimum number of inference units used by the model. For more information, see StartProjectVersion .

Status -> (string)

The current status of the model version.

StatusMessage -> (string)

A descriptive message for an error or warning that occurred.

BillableTrainingTimeInSeconds -> (long)

The duration, in seconds, that you were billed for a successful training of the model version. This value is only returned if the model version has been successfully trained.

TrainingEndTimestamp -> (timestamp)

The Unix date and time that training of the model ended.

OutputConfig -> (structure)

The location where training results are saved.

S3Bucket -> (string)

The S3 bucket where training output is placed.

S3KeyPrefix -> (string)

The prefix applied to the training output files.

TrainingDataResult -> (structure)

Contains information about the training results.

Input -> (structure)

The training assets that you supplied for training.

Assets -> (list)

A Sagemaker GroundTruth manifest file that contains the training images (assets).

(structure)

Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.

GroundTruthManifest -> (structure)

The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.

S3Object -> (structure)

Provides the S3 bucket name and object name.

The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.

For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.

Bucket -> (string)

Name of the S3 bucket.

Name -> (string)

S3 object key name.

Version -> (string)

If the bucket is versioning enabled, you can specify the object version.

Output -> (structure)

The images (assets) that were actually trained by Amazon Rekognition Custom Labels.

Assets -> (list)

A Sagemaker GroundTruth manifest file that contains the training images (assets).

(structure)

Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.

GroundTruthManifest -> (structure)

The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.

S3Object -> (structure)

Provides the S3 bucket name and object name.

The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.

For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.

Bucket -> (string)

Name of the S3 bucket.

Name -> (string)

S3 object key name.

Version -> (string)

If the bucket is versioning enabled, you can specify the object version.

Validation -> (structure)

The location of the data validation manifest. The data validation manifest is created for the training dataset during model training.

Assets -> (list)

The assets that comprise the validation data.

(structure)

Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.

GroundTruthManifest -> (structure)

The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.

S3Object -> (structure)

Provides the S3 bucket name and object name.

The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.

For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.

Bucket -> (string)

Name of the S3 bucket.

Name -> (string)

S3 object key name.

Version -> (string)

If the bucket is versioning enabled, you can specify the object version.

TestingDataResult -> (structure)

Contains information about the testing results.

Input -> (structure)

The testing dataset that was supplied for training.

Assets -> (list)

The assets used for testing.

(structure)

Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.

GroundTruthManifest -> (structure)

The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.

S3Object -> (structure)

Provides the S3 bucket name and object name.

The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.

For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.

Bucket -> (string)

Name of the S3 bucket.

Name -> (string)

S3 object key name.

Version -> (string)

If the bucket is versioning enabled, you can specify the object version.

AutoCreate -> (boolean)

If specified, Amazon Rekognition Custom Labels temporarily splits the training dataset (80%) to create a test dataset (20%) for the training job. After training completes, the test dataset is not stored and the training dataset reverts to its previous size.

Output -> (structure)

The subset of the dataset that was actually tested. Some images (assets) might not be tested due to file formatting and other issues.

Assets -> (list)

The assets used for testing.

(structure)

Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.

GroundTruthManifest -> (structure)

The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.

S3Object -> (structure)

Provides the S3 bucket name and object name.

The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.

For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.

Bucket -> (string)

Name of the S3 bucket.

Name -> (string)

S3 object key name.

Version -> (string)

If the bucket is versioning enabled, you can specify the object version.

AutoCreate -> (boolean)

If specified, Amazon Rekognition Custom Labels temporarily splits the training dataset (80%) to create a test dataset (20%) for the training job. After training completes, the test dataset is not stored and the training dataset reverts to its previous size.

Validation -> (structure)

The location of the data validation manifest. The data validation manifest is created for the test dataset during model training.

Assets -> (list)

The assets that comprise the validation data.

(structure)

Assets are the images that you use to train and evaluate a model version. Assets can also contain validation information that you use to debug a failed model training.

GroundTruthManifest -> (structure)

The S3 bucket that contains an Amazon Sagemaker Ground Truth format manifest file.

S3Object -> (structure)

Provides the S3 bucket name and object name.

The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.

For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.

Bucket -> (string)

Name of the S3 bucket.

Name -> (string)

S3 object key name.

Version -> (string)

If the bucket is versioning enabled, you can specify the object version.

EvaluationResult -> (structure)

The training results. EvaluationResult is only returned if training is successful.

F1Score -> (float)

The F1 score for the evaluation of all labels. The F1 score metric evaluates the overall precision and recall performance of the model as a single value. A higher value indicates better precision and recall performance. A lower score indicates that precision, recall, or both are performing poorly.

Summary -> (structure)

The S3 bucket that contains the training summary.

S3Object -> (structure)

Provides the S3 bucket name and object name.

The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.

For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.

Bucket -> (string)

Name of the S3 bucket.

Name -> (string)

S3 object key name.

Version -> (string)

If the bucket is versioning enabled, you can specify the object version.

ManifestSummary -> (structure)

The location of the summary manifest. The summary manifest provides aggregate data validation results for the training and test datasets.

S3Object -> (structure)

Provides the S3 bucket name and object name.

The region for the S3 bucket containing the S3 object must match the region you use for Amazon Rekognition operations.

For Amazon Rekognition to process an S3 object, the user must have permission to access the S3 object. For more information, see How Amazon Rekognition works with IAM in the Amazon Rekognition Developer Guide.

Bucket -> (string)

Name of the S3 bucket.

Name -> (string)

S3 object key name.

Version -> (string)

If the bucket is versioning enabled, you can specify the object version.

KmsKeyId -> (string)

The identifer for the AWS Key Management Service key (AWS KMS key) that was used to encrypt the model during training.

NextToken -> (string)

If the previous response was incomplete (because there is more results to retrieve), Amazon Rekognition Custom Labels returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

Table of Contents

Feedback

User Guide

describe-project-versions¶

Description¶

Synopsis¶

Options¶

Output¶