SageMaker / Client / describe_auto_ml_job

describe_auto_ml_job#

SageMaker.Client.describe_auto_ml_job(**kwargs)#

Returns information about an AutoML job created by calling CreateAutoMLJob.

Note

AutoML jobs created by calling CreateAutoMLJobV2 cannot be described by DescribeAutoMLJob.

See also: AWS API Documentation

Request Syntax

response = client.describe_auto_ml_job(
    AutoMLJobName='string'
)
Parameters:

AutoMLJobName (string) –

[REQUIRED]

Requests information about an AutoML job using its unique name.

Return type:

dict

Returns:

Response Syntax

{
    'AutoMLJobName': 'string',
    'AutoMLJobArn': 'string',
    'InputDataConfig': [
        {
            'DataSource': {
                'S3DataSource': {
                    'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile',
                    'S3Uri': 'string'
                }
            },
            'CompressionType': 'None'|'Gzip',
            'TargetAttributeName': 'string',
            'ContentType': 'string',
            'ChannelType': 'training'|'validation',
            'SampleWeightAttributeName': 'string'
        },
    ],
    'OutputDataConfig': {
        'KmsKeyId': 'string',
        'S3OutputPath': 'string'
    },
    'RoleArn': 'string',
    'AutoMLJobObjective': {
        'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
    },
    'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
    'AutoMLJobConfig': {
        'CompletionCriteria': {
            'MaxCandidates': 123,
            'MaxRuntimePerTrainingJobInSeconds': 123,
            'MaxAutoMLJobRuntimeInSeconds': 123
        },
        'SecurityConfig': {
            'VolumeKmsKeyId': 'string',
            'EnableInterContainerTrafficEncryption': True|False,
            'VpcConfig': {
                'SecurityGroupIds': [
                    'string',
                ],
                'Subnets': [
                    'string',
                ]
            }
        },
        'CandidateGenerationConfig': {
            'FeatureSpecificationS3Uri': 'string',
            'AlgorithmsConfig': [
                {
                    'AutoMLAlgorithms': [
                        'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai'|'cnn-qr'|'deepar'|'prophet'|'npts'|'arima'|'ets',
                    ]
                },
            ]
        },
        'DataSplitConfig': {
            'ValidationFraction': ...
        },
        'Mode': 'AUTO'|'ENSEMBLING'|'HYPERPARAMETER_TUNING'
    },
    'CreationTime': datetime(2015, 1, 1),
    'EndTime': datetime(2015, 1, 1),
    'LastModifiedTime': datetime(2015, 1, 1),
    'FailureReason': 'string',
    'PartialFailureReasons': [
        {
            'PartialFailureMessage': 'string'
        },
    ],
    'BestCandidate': {
        'CandidateName': 'string',
        'FinalAutoMLJobObjectiveMetric': {
            'Type': 'Maximize'|'Minimize',
            'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss',
            'Value': ...,
            'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
        },
        'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed',
        'CandidateSteps': [
            {
                'CandidateStepType': 'AWS::SageMaker::TrainingJob'|'AWS::SageMaker::TransformJob'|'AWS::SageMaker::ProcessingJob',
                'CandidateStepArn': 'string',
                'CandidateStepName': 'string'
            },
        ],
        'CandidateStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
        'InferenceContainers': [
            {
                'Image': 'string',
                'ModelDataUrl': 'string',
                'Environment': {
                    'string': 'string'
                }
            },
        ],
        'CreationTime': datetime(2015, 1, 1),
        'EndTime': datetime(2015, 1, 1),
        'LastModifiedTime': datetime(2015, 1, 1),
        'FailureReason': 'string',
        'CandidateProperties': {
            'CandidateArtifactLocations': {
                'Explainability': 'string',
                'ModelInsights': 'string',
                'BacktestResults': 'string'
            },
            'CandidateMetrics': [
                {
                    'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss',
                    'Value': ...,
                    'Set': 'Train'|'Validation'|'Test',
                    'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'LogLoss'|'InferenceLatency'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'|'Rouge1'|'Rouge2'|'RougeL'|'RougeLSum'|'Perplexity'|'ValidationLoss'|'TrainingLoss'
                },
            ]
        },
        'InferenceContainerDefinitions': {
            'string': [
                {
                    'Image': 'string',
                    'ModelDataUrl': 'string',
                    'Environment': {
                        'string': 'string'
                    }
                },
            ]
        }
    },
    'AutoMLJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
    'AutoMLJobSecondaryStatus': 'Starting'|'MaxCandidatesReached'|'Failed'|'Stopped'|'MaxAutoMLJobRuntimeReached'|'Stopping'|'CandidateDefinitionsGenerated'|'Completed'|'ExplainabilityError'|'DeployingModel'|'ModelDeploymentError'|'GeneratingModelInsightsReport'|'ModelInsightsError'|'AnalyzingData'|'FeatureEngineering'|'ModelTuning'|'GeneratingExplainabilityReport'|'TrainingModels'|'PreTraining',
    'GenerateCandidateDefinitionsOnly': True|False,
    'AutoMLJobArtifacts': {
        'CandidateDefinitionNotebookLocation': 'string',
        'DataExplorationNotebookLocation': 'string'
    },
    'ResolvedAttributes': {
        'AutoMLJobObjective': {
            'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'
        },
        'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression',
        'CompletionCriteria': {
            'MaxCandidates': 123,
            'MaxRuntimePerTrainingJobInSeconds': 123,
            'MaxAutoMLJobRuntimeInSeconds': 123
        }
    },
    'ModelDeployConfig': {
        'AutoGenerateEndpointName': True|False,
        'EndpointName': 'string'
    },
    'ModelDeployResult': {
        'EndpointName': 'string'
    }
}

Response Structure

  • (dict) –

    • AutoMLJobName (string) –

      Returns the name of the AutoML job.

    • AutoMLJobArn (string) –

      Returns the ARN of the AutoML job.

    • InputDataConfig (list) –

      Returns the input data configuration for the AutoML job.

      • (dict) –

        A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see Channel.

        Note

        A validation dataset must contain the same headers as the training dataset.

        • DataSource (dict) –

          The data source for an AutoML channel.

          • S3DataSource (dict) –

            The Amazon S3 location of the input data.

            • S3DataType (string) –

              The data type.

              • If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker AI 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 AI 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.

        • CompressionType (string) –

          You can use Gzip or None. The default value is None.

        • TargetAttributeName (string) –

          The name of the target variable in supervised learning, usually represented by ‘y’.

        • ContentType (string) –

          The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

        • ChannelType (string) –

          The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.

        • SampleWeightAttributeName (string) –

          If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

          Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

          Support for sample weights is available in Ensembling mode only.

    • OutputDataConfig (dict) –

      Returns the job’s output data config.

      • KmsKeyId (string) –

        The Key Management Service encryption key ID.

      • S3OutputPath (string) –

        The Amazon S3 output path. Must be 512 characters or less.

    • RoleArn (string) –

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

    • AutoMLJobObjective (dict) –

      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.

        The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.

        • For tabular problem types:

          • List of available metrics:

            • Regression: MAE, MSE, R2, RMSE

            • Binary classification: Accuracy, AUC, BalancedAccuracy, F1, Precision, Recall

            • Multiclass classification: Accuracy, BalancedAccuracy, F1macro, PrecisionMacro, RecallMacro

          For a description of each metric, see Autopilot metrics for classification and regression.

          • Default objective metrics:

            • Regression: MSE.

            • Binary classification: F1.

            • Multiclass classification: Accuracy.

        • For image or text classification problem types:

        • For time-series forecasting problem types:

        • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

    • ProblemType (string) –

      Returns the job’s problem type.

    • AutoMLJobConfig (dict) –

      Returns the configuration for the AutoML job.

      • CompletionCriteria (dict) –

        How long an AutoML 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 text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

        • 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 job V2s (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.

          For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

        • 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.

      • SecurityConfig (dict) –

        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 (dict) –

          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) –

      • CandidateGenerationConfig (dict) –

        The configuration for generating a candidate for an AutoML job (optional).

        • FeatureSpecificationS3Uri (string) –

          A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input FeatureAttributeNames (optional) in JSON format as shown below:

          { "FeatureAttributeNames":["col1", "col2", ...] }.

          You can also specify the data type of the feature (optional) in the format shown below:

          { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

          Note

          These column keys may not include the target column.

          In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

          If only FeatureDataTypes is provided, the column keys ( col1, col2,..) should be a subset of the column names in the input data.

          If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

          The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

        • AlgorithmsConfig (list) –

          Stores the configuration information for the selection of algorithms trained on tabular data.

          The list of available algorithms to choose from depends on the training mode set in TabularJobConfig.Mode.

          • AlgorithmsConfig should not be set if the training mode is set on AUTO.

          • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

          • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

          For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig.

          For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.

          • (dict) –

            The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

            • AutoMLAlgorithms (list) –

              The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

              • For the tabular problem type TabularJobConfig:

              Note

              Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode ( ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

              • In ENSEMBLING mode:

                • ”catboost”

                • ”extra-trees”

                • ”fastai”

                • ”lightgbm”

                • ”linear-learner”

                • ”nn-torch”

                • ”randomforest”

                • ”xgboost”

              • In HYPERPARAMETER_TUNING mode:

                • ”linear-learner”

                • ”mlp”

                • ”xgboost”

              • For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

                • Choose your algorithms from this list.

                  • ”cnn-qr”

                  • ”deepar”

                  • ”prophet”

                  • ”arima”

                  • ”npts”

                  • ”ets”

              • (string) –

      • DataSplitConfig (dict) –

        The configuration for splitting the input training dataset.

        Type: AutoMLDataSplitConfig

        • 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.

      • Mode (string) –

        The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

        The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

        The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

    • CreationTime (datetime) –

      Returns the creation time of the AutoML job.

    • EndTime (datetime) –

      Returns the end time of the AutoML job.

    • LastModifiedTime (datetime) –

      Returns the job’s last modified time.

    • FailureReason (string) –

      Returns the failure reason for an AutoML job, when applicable.

    • PartialFailureReasons (list) –

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

      • (dict) –

        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 (dict) –

      The best model candidate selected by SageMaker AI Autopilot using both the best objective metric and lowest InferenceLatency for an experiment.

      • CandidateName (string) –

        The name of the candidate.

      • FinalAutoMLJobObjectiveMetric (dict) –

        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.

        • (dict) –

          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.

        • (dict) –

          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 (dict) –

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

            • (string) –

              • (string) –

      • CreationTime (datetime) –

        The creation time.

      • EndTime (datetime) –

        The end time.

      • LastModifiedTime (datetime) –

        The last modified time.

      • FailureReason (string) –

        The failure reason.

      • CandidateProperties (dict) –

        The properties of an AutoML candidate job.

        • CandidateArtifactLocations (dict) –

          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.

          • BacktestResults (string) –

            The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type.

        • CandidateMetrics (list) –

          Information about the candidate metrics for an AutoML job.

          • (dict) –

            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.

      • InferenceContainerDefinitions (dict) –

        The mapping of all supported processing unit (CPU, GPU, etc…) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling CreateAutoMLJobV2) related to image or text classification problem types only.

        • (string) –

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

          • (list) –

            Information about the recommended inference container definitions.

            • (dict) –

              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 (dict) –

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

                • (string) –

                  • (string) –

    • AutoMLJobStatus (string) –

      Returns the status of the AutoML job.

    • AutoMLJobSecondaryStatus (string) –

      Returns the secondary status of the AutoML job.

    • GenerateCandidateDefinitionsOnly (boolean) –

      Indicates whether the output for an AutoML job generates candidate definitions only.

    • AutoMLJobArtifacts (dict) –

      Returns information on the job’s artifacts found in AutoMLJobArtifacts.

      • CandidateDefinitionNotebookLocation (string) –

        The URL of the notebook location.

      • DataExplorationNotebookLocation (string) –

        The URL of the notebook location.

    • ResolvedAttributes (dict) –

      Contains ProblemType, AutoMLJobObjective, and CompletionCriteria. If you do not provide these values, they are inferred.

      • AutoMLJobObjective (dict) –

        Specifies a metric to minimize or maximize as the objective of an AutoML job.

        • 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.

          The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.

          • For tabular problem types:

            • List of available metrics:

              • Regression: MAE, MSE, R2, RMSE

              • Binary classification: Accuracy, AUC, BalancedAccuracy, F1, Precision, Recall

              • Multiclass classification: Accuracy, BalancedAccuracy, F1macro, PrecisionMacro, RecallMacro

            For a description of each metric, see Autopilot metrics for classification and regression.

            • Default objective metrics:

              • Regression: MSE.

              • Binary classification: F1.

              • Multiclass classification: Accuracy.

          • For image or text classification problem types:

          • For time-series forecasting problem types:

          • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

      • ProblemType (string) –

        The problem type.

      • CompletionCriteria (dict) –

        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 text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.

        • 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 job V2s (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate.

          For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).

        • 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.

    • ModelDeployConfig (dict) –

      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 (dict) –

      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.

Exceptions

  • SageMaker.Client.exceptions.ResourceNotFound