[ aws . sagemaker ]

describe-auto-ml-job-v2

Description

Returns information about an Amazon SageMaker AutoML V2 job.

Note

This API action is callable through SageMaker Canvas only. Calling it directly from the CLI or an SDK results in an error.

See also: AWS API Documentation

Synopsis

  describe-auto-ml-job-v2
--auto-ml-job-name <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

--auto-ml-job-name (string)

Requests information about an AutoML V2 job using its unique name.

--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.

--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

AutoMLJobName -> (string)

Returns the name of the AutoML V2 job.

AutoMLJobArn -> (string)

Returns the Amazon Resource Name (ARN) of the AutoML V2 job.

AutoMLJobInputDataConfig -> (list)

Returns an array of channel objects describing the input data and their location.

(structure)

A channel is a named input source that training algorithms can consume. This channel is used for the non tabular training data of an AutoML job using the V2 API. For tabular training data, see AutoMLChannel . For more information, see Channel .

ChannelType -> (string)

The type of channel. Defines whether the data are used for training or validation. The default value is training . Channels for training and validation must share the same ContentType

ContentType -> (string)

The content type of the data from the input source. The following are the allowed content types for different problems:

• ImageClassification: image/png , image/jpeg , image/*
• TextClassification: text/csv;header=present

CompressionType -> (string)

The allowed compression types depend on the input format. We allow the compression type Gzip for S3Prefix inputs only. For all other inputs, the compression type should be None . If no compression type is provided, we default to None .

DataSource -> (structure)

The data source for an AutoML channel.

S3DataSource -> (structure)

The Amazon S3 location of the input data.

S3DataType -> (string)

The data type.

• If you choose S3Prefix , S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. The S3Prefix should have the following format: s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE
• If you choose ManifestFile , S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. A ManifestFile should have the format shown below: [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"}, "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1", "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2", ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]
• If you choose AugmentedManifestFile , S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2 ). Here is a minimal, single-record example of an AugmentedManifestFile : {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg", "label-metadata": {"class-name": "cat" } For more information on AugmentedManifestFile , see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File .

S3Uri -> (string)

The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.

OutputDataConfig -> (structure)

Returns the job’s output data config.

KmsKeyId -> (string)

The Key Management Service (KMS) encryption key ID.

S3OutputPath -> (string)

The Amazon S3 output path. Must be 128 characters or less.

RoleArn -> (string)

The ARN of the Identity and Access Management role that has read permission to the input data location and write permission to the output data location in Amazon S3.

AutoMLJobObjective -> (structure)

Returns the job’s objective.

MetricName -> (string)

The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model’s parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset.

For the list of all available metrics supported by Autopilot, see Autopilot metrics .

If you do not specify a metric explicitly, the default behavior is to automatically use:

• MSE : for regression.
• F1 : for binary classification
• Accuracy : for multiclass classification.

AutoMLProblemTypeConfig -> (tagged union structure)

Returns the configuration settings of the problem type set for the AutoML V2 job.

Note

This is a Tagged Union structure. Only one of the following top level keys can be set: ImageClassificationJobConfig, TextClassificationJobConfig.

ImageClassificationJobConfig -> (structure)

Settings used to configure an AutoML job using the V2 API for the image classification problem type.

CompletionCriteria -> (structure)

How long a job is allowed to run, or how many candidates a job is allowed to generate.

MaxCandidates -> (integer)

The maximum number of times a training job is allowed to run.

For V2 jobs (jobs created by calling CreateAutoMLJobV2 ), the supported value is 1.

MaxRuntimePerTrainingJobInSeconds -> (integer)

The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

For V2 jobs (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

MaxAutoMLJobRuntimeInSeconds -> (integer)

The maximum runtime, in seconds, an AutoML job has to complete.

If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

TextClassificationJobConfig -> (structure)

Settings used to configure an AutoML job using the V2 API for the text classification problem type.

CompletionCriteria -> (structure)

How long a job is allowed to run, or how many candidates a job is allowed to generate.

MaxCandidates -> (integer)

The maximum number of times a training job is allowed to run.

For V2 jobs (jobs created by calling CreateAutoMLJobV2 ), the supported value is 1.

MaxRuntimePerTrainingJobInSeconds -> (integer)

The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action.

For V2 jobs (jobs created by calling CreateAutoMLJobV2 ), this field controls the runtime of the job candidate.

MaxAutoMLJobRuntimeInSeconds -> (integer)

The maximum runtime, in seconds, an AutoML job has to complete.

If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.

ContentColumn -> (string)

The name of the column used to provide the sentences to be classified. It should not be the same as the target column.

TargetLabelColumn -> (string)

The name of the column used to provide the class labels. It should not be same as the content column.

CreationTime -> (timestamp)

Returns the creation time of the AutoML V2 job.

EndTime -> (timestamp)

Returns the end time of the AutoML V2 job.

LastModifiedTime -> (timestamp)

Returns the job’s last modified time.

FailureReason -> (string)

Returns the reason for the failure of the AutoML V2 job, when applicable.

PartialFailureReasons -> (list)

Returns a list of reasons for partial failures within an AutoML V2 job.

(structure)

The reason for a partial failure of an AutoML job.

PartialFailureMessage -> (string)

The message containing the reason for a partial failure of an AutoML job.

BestCandidate -> (structure)

Information about the candidate produced by an AutoML training job V2, including its status, steps, and other properties.

CandidateName -> (string)

The name of the candidate.

FinalAutoMLJobObjectiveMetric -> (structure)

The best candidate result from an AutoML training job.

Type -> (string)

The type of metric with the best result.

MetricName -> (string)

The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName .

Value -> (float)

The value of the metric with the best result.

StandardMetricName -> (string)

The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics .

ObjectiveStatus -> (string)

The objective’s status.

CandidateSteps -> (list)

Information about the candidate’s steps.

(structure)

Information about the steps for a candidate and what step it is working on.

CandidateStepType -> (string)

Whether the candidate is at the transform, training, or processing step.

CandidateStepArn -> (string)

The ARN for the candidate’s step.

CandidateStepName -> (string)

The name for the candidate’s step.

CandidateStatus -> (string)

The candidate’s status.

InferenceContainers -> (list)

Information about the recommended inference container definitions.

(structure)

A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition .

Image -> (string)

The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition .

ModelDataUrl -> (string)

The location of the model artifacts. For more information, see ContainerDefinition .

Environment -> (map)

The environment variables to set in the container. For more information, see ContainerDefinition .

key -> (string)

value -> (string)

CreationTime -> (timestamp)

The creation time.

EndTime -> (timestamp)

The end time.

LastModifiedTime -> (timestamp)

The last modified time.

FailureReason -> (string)

The failure reason.

CandidateProperties -> (structure)

The properties of an AutoML candidate job.

CandidateArtifactLocations -> (structure)

The Amazon S3 prefix to the artifacts generated for an AutoML candidate.

Explainability -> (string)

The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.

ModelInsights -> (string)

The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.

CandidateMetrics -> (list)

Information about the candidate metrics for an AutoML job.

(structure)

Information about the metric for a candidate produced by an AutoML job.

MetricName -> (string)

The name of the metric.

Value -> (float)

The value of the metric.

Set -> (string)

The dataset split from which the AutoML job produced the metric.

StandardMetricName -> (string)

The name of the standard metric.

Note

For definitions of the standard metrics, see ` Autopilot candidate metrics https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-metrics`__ .

InferenceContainerDefinitions -> (map)

The mapping of all supported processing unit (CPU, GPU, etc…) to inference container definitions for the candidate. This field is populated for the V2 API only (for example, for jobs created by calling CreateAutoMLJobV2 ).

key -> (string)

Processing unit for an inference container. Currently Autopilot only supports CPU or GPU .

value -> (list)

Information about the recommended inference container definitions.

(structure)

A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition .

Image -> (string)

The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition .

ModelDataUrl -> (string)

The location of the model artifacts. For more information, see ContainerDefinition .

Environment -> (map)

The environment variables to set in the container. For more information, see ContainerDefinition .

key -> (string)

value -> (string)

AutoMLJobStatus -> (string)

Returns the status of the AutoML V2 job.

AutoMLJobSecondaryStatus -> (string)

Returns the secondary status of the AutoML V2 job.

ModelDeployConfig -> (structure)

Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.

AutoGenerateEndpointName -> (boolean)

Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False .

Note

If you set AutoGenerateEndpointName to True , do not specify the EndpointName ; otherwise a 400 error is thrown.

EndpointName -> (string)

Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.

Note

Specify the EndpointName if and only if you set AutoGenerateEndpointName to False ; otherwise a 400 error is thrown.

ModelDeployResult -> (structure)

Provides information about endpoint for the model deployment.

EndpointName -> (string)

The name of the endpoint to which the model has been deployed.

Note

If model deployment fails, this field is omitted from the response.

DataSplitConfig -> (structure)

Returns the configuration settings of how the data are split into train and validation datasets.

ValidationFraction -> (float)

The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

SecurityConfig -> (structure)

Returns the security configuration for traffic encryption or Amazon VPC settings.

VolumeKmsKeyId -> (string)

The key used to encrypt stored data.

EnableInterContainerTrafficEncryption -> (boolean)

Whether to use traffic encryption between the container layers.

VpcConfig -> (structure)

The VPC configuration.

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. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones .

(string)