aws_sagemaker_automljob

account_id

Type: STRING

auto_ml_job_arn

Type: STRING
Provider name: AutoMLJobArn
Description: Returns the ARN of the AutoML job.

auto_ml_job_artifacts

Type: STRUCT
Provider name: AutoMLJobArtifacts
Description: Returns information on the job’s artifacts found in AutoMLJobArtifacts.

• candidate_definition_notebook_location
  Type: STRING
  Provider name: CandidateDefinitionNotebookLocation
  Description: The URL of the notebook location.
  
• data_exploration_notebook_location
  Type: STRING
  Provider name: DataExplorationNotebookLocation
  Description: The URL of the notebook location.
  

auto_ml_job_config

Type: STRUCT
Provider name: AutoMLJobConfig
Description: Returns the configuration for the AutoML job.

• candidate_generation_config
  Type: STRUCT
  Provider name: CandidateGenerationConfig
  Description: The configuration for generating a candidate for an AutoML job (optional).
  
  • algorithms_config
    Type: UNORDERED_LIST_STRUCT
    Provider name: AlgorithmsConfig
    Description: 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.
    
    • auto_ml_algorithms
      Type: UNORDERED_LIST_STRING
      Provider name: AutoMLAlgorithms
      Description: The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
      • For the tabular problem type TabularJobConfig: 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”
  • feature_specification_s3_uri
    Type: STRING
    Provider name: FeatureSpecificationS3Uri
    Description: 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” … } } 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.
    
• completion_criteria
  Type: STRUCT
  Provider name: CompletionCriteria
  Description: How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
  
  • max_auto_ml_job_runtime_in_seconds
    Type: INT32
    Provider name: MaxAutoMLJobRuntimeInSeconds
    Description: 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.
    
  • max_candidates
    Type: INT32
    Provider name: MaxCandidates
    Description: 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.
    
  • max_runtime_per_training_job_in_seconds
    Type: INT32
    Provider name: MaxRuntimePerTrainingJobInSeconds
    Description: 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).
    
• data_split_config
  Type: STRUCT
  Provider name: DataSplitConfig
  Description: The configuration for splitting the input training dataset. Type: AutoMLDataSplitConfig
  
  • validation_fraction
    Type: FLOAT
    Provider name: ValidationFraction
    Description: 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
  Type: STRING
  Provider name: Mode
  Description: 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.
  
• security_config
  Type: STRUCT
  Provider name: SecurityConfig
  Description: The security configuration for traffic encryption or Amazon VPC settings.
  
  • enable_inter_container_traffic_encryption
    Type: BOOLEAN
    Provider name: EnableInterContainerTrafficEncryption
    Description: Whether to use traffic encryption between the container layers.
    
  • volume_kms_key_id
    Type: STRING
    Provider name: VolumeKmsKeyId
    Description: The key used to encrypt stored data.
    
  • vpc_config
    Type: STRUCT
    Provider name: VpcConfig
    Description: The VPC configuration.
    
    • security_group_ids
      Type: UNORDERED_LIST_STRING
      Provider name: SecurityGroupIds
      Description: The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
      
    • subnets
      Type: UNORDERED_LIST_STRING
      Provider name: Subnets
      Description: 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.
      

auto_ml_job_name

Type: STRING
Provider name: AutoMLJobName
Description: Returns the name of the AutoML job.

auto_ml_job_objective

Type: STRUCT
Provider name: AutoMLJobObjective
Description: Returns the job’s objective.

• metric_name
  Type: STRING
  Provider name: MetricName
  Description: 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:
    • List of available metrics: Accuracy For a description of each metric, see Autopilot metrics for text and image classification.
    • Default objective metrics: Accuracy
  • For time-series forecasting problem types:
    • List of available metrics: RMSE, wQL, Average wQL, MASE, MAPE, WAPE For a description of each metric, see Autopilot metrics for time-series forecasting.
    • Default objective metrics: AverageWeightedQuantileLoss
  • 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.

auto_ml_job_secondary_status

Type: STRING
Provider name: AutoMLJobSecondaryStatus
Description: Returns the secondary status of the AutoML job.

auto_ml_job_status

Type: STRING
Provider name: AutoMLJobStatus
Description: Returns the status of the AutoML job.

best_candidate

Type: STRUCT
Provider name: BestCandidate
Description: The best model candidate selected by SageMaker AI Autopilot using both the best objective metric and lowest InferenceLatency for an experiment.

• candidate_name
  Type: STRING
  Provider name: CandidateName
  Description: The name of the candidate.
  
• candidate_properties
  Type: STRUCT
  Provider name: CandidateProperties
  Description: The properties of an AutoML candidate job.
  
  • candidate_artifact_locations
    Type: STRUCT
    Provider name: CandidateArtifactLocations
    Description: The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
    
    • backtest_results
      Type: STRING
      Provider name: BacktestResults
      Description: 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.
      
    • explainability
      Type: STRING
      Provider name: Explainability
      Description: The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.
      
    • model_insights
      Type: STRING
      Provider name: ModelInsights
      Description: The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.
      
  • candidate_metrics
    Type: UNORDERED_LIST_STRUCT
    Provider name: CandidateMetrics
    Description: Information about the candidate metrics for an AutoML job.
    
    • metric_name
      Type: STRING
      Provider name: MetricName
      Description: The name of the metric.
      
    • set
      Type: STRING
      Provider name: Set
      Description: The dataset split from which the AutoML job produced the metric.
      
    • standard_metric_name
      Type: STRING
      Provider name: StandardMetricName
      Description: The name of the standard metric. For definitions of the standard metrics, see Autopilot candidate metrics .
      
    • value
      Type: FLOAT
      Provider name: Value
      Description: The value of the metric.
      
• candidate_status
  Type: STRING
  Provider name: CandidateStatus
  Description: The candidate’s status.
  
• candidate_steps
  Type: UNORDERED_LIST_STRUCT
  Provider name: CandidateSteps
  Description: Information about the candidate’s steps.
  
  • candidate_step_arn
    Type: STRING
    Provider name: CandidateStepArn
    Description: The ARN for the candidate’s step.
    
  • candidate_step_name
    Type: STRING
    Provider name: CandidateStepName
    Description: The name for the candidate’s step.
    
  • candidate_step_type
    Type: STRING
    Provider name: CandidateStepType
    Description: Whether the candidate is at the transform, training, or processing step.
    
• creation_time
  Type: TIMESTAMP
  Provider name: CreationTime
  Description: The creation time.
  
• end_time
  Type: TIMESTAMP
  Provider name: EndTime
  Description: The end time.
  
• failure_reason
  Type: STRING
  Provider name: FailureReason
  Description: The failure reason.
  
• final_auto_ml_job_objective_metric
  Type: STRUCT
  Provider name: FinalAutoMLJobObjectiveMetric
  
  • metric_name
    Type: STRING
    Provider name: MetricName
    Description: The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName.
    
  • standard_metric_name
    Type: STRING
    Provider name: StandardMetricName
    Description: The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics.
    
  • type
    Type: STRING
    Provider name: Type
    Description: The type of metric with the best result.
    
  • value
    Type: FLOAT
    Provider name: Value
    Description: The value of the metric with the best result.
    
• inference_container_definitions
  Type: STRING
  Provider name: InferenceContainerDefinitions
  Description: 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.
  
• inference_containers
  Type: UNORDERED_LIST_STRUCT
  Provider name: InferenceContainers
  Description: Information about the recommended inference container definitions.
  
  • environment
    Type: MAP_STRING_STRING
    Provider name: Environment
    Description: The environment variables to set in the container. For more information, see ContainerDefinition.
    
  • image
    Type: STRING
    Provider name: Image
    Description: The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition.
    
  • model_data_url
    Type: STRING
    Provider name: ModelDataUrl
    Description: The location of the model artifacts. For more information, see ContainerDefinition.
    
• last_modified_time
  Type: TIMESTAMP
  Provider name: LastModifiedTime
  Description: The last modified time.
  
• objective_status
  Type: STRING
  Provider name: ObjectiveStatus
  Description: The objective’s status.
  

creation_time

Type: TIMESTAMP
Provider name: CreationTime
Description: Returns the creation time of the AutoML job.

end_time

Type: TIMESTAMP
Provider name: EndTime
Description: Returns the end time of the AutoML job.

failure_reason

Type: STRING
Provider name: FailureReason
Description: Returns the failure reason for an AutoML job, when applicable.

generate_candidate_definitions_only

Type: BOOLEAN
Provider name: GenerateCandidateDefinitionsOnly
Description: Indicates whether the output for an AutoML job generates candidate definitions only.

input_data_config

Type: UNORDERED_LIST_STRUCT
Provider name: InputDataConfig
Description: Returns the input data configuration for the AutoML job.

• channel_type
  Type: STRING
  Provider name: ChannelType
  Description: 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.
  
• compression_type
  Type: STRING
  Provider name: CompressionType
  Description: You can use Gzip or None. The default value is None.
  
• content_type
  Type: STRING
  Provider name: ContentType
  Description: 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.
  
• data_source
  Type: STRUCT
  Provider name: DataSource
  Description: The data source for an AutoML channel.
  
  • s3_data_source
    Type: STRUCT
    Provider name: S3DataSource
    Description: The Amazon S3 location of the input data.
    
    • s3_data_type
      Type: STRING
      Provider name: S3DataType
      Description: 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.
    • s3_uri
      Type: STRING
      Provider name: S3Uri
      Description: The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.
      
• sample_weight_attribute_name
  Type: STRING
  Provider name: SampleWeightAttributeName
  Description: 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.
  
• target_attribute_name
  Type: STRING
  Provider name: TargetAttributeName
  Description: The name of the target variable in supervised learning, usually represented by ‘y’.
  

last_modified_time

Type: TIMESTAMP
Provider name: LastModifiedTime
Description: Returns the job’s last modified time.

model_deploy_config

Type: STRUCT
Provider name: ModelDeployConfig
Description: Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.

• auto_generate_endpoint_name
  Type: BOOLEAN
  Provider name: AutoGenerateEndpointName
  Description: 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. If you set AutoGenerateEndpointName to True, do not specify the EndpointName; otherwise a 400 error is thrown.
  
• endpoint_name
  Type: STRING
  Provider name: EndpointName
  Description: Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically. Specify the EndpointName if and only if you set AutoGenerateEndpointName to False; otherwise a 400 error is thrown.
  

model_deploy_result

Type: STRUCT
Provider name: ModelDeployResult
Description: Provides information about endpoint for the model deployment.

• endpoint_name
  Type: STRING
  Provider name: EndpointName
  Description: The name of the endpoint to which the model has been deployed. If model deployment fails, this field is omitted from the response.
  

output_data_config

Type: STRUCT
Provider name: OutputDataConfig
Description: Returns the job’s output data config.

• kms_key_id
  Type: STRING
  Provider name: KmsKeyId
  Description: The Key Management Service encryption key ID.
  
• s3_output_path
  Type: STRING
  Provider name: S3OutputPath
  Description: The Amazon S3 output path. Must be 512 characters or less.
  

partial_failure_reasons

Type: UNORDERED_LIST_STRUCT
Provider name: PartialFailureReasons
Description: Returns a list of reasons for partial failures within an AutoML job.

• partial_failure_message
  Type: STRING
  Provider name: PartialFailureMessage
  Description: The message containing the reason for a partial failure of an AutoML job.
  

problem_type

Type: STRING
Provider name: ProblemType
Description: Returns the job’s problem type.

resolved_attributes

Type: STRUCT
Provider name: ResolvedAttributes
Description: Contains ProblemType, AutoMLJobObjective, and CompletionCriteria. If you do not provide these values, they are inferred.

• auto_ml_job_objective
  Type: STRUCT
  Provider name: AutoMLJobObjective
  
  • metric_name
    Type: STRING
    Provider name: MetricName
    Description: 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:
      • List of available metrics: Accuracy For a description of each metric, see Autopilot metrics for text and image classification.
      • Default objective metrics: Accuracy
    • For time-series forecasting problem types:
      • List of available metrics: RMSE, wQL, Average wQL, MASE, MAPE, WAPE For a description of each metric, see Autopilot metrics for time-series forecasting.
      • Default objective metrics: AverageWeightedQuantileLoss
    • 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.
• completion_criteria
  Type: STRUCT
  Provider name: CompletionCriteria
  
  • max_auto_ml_job_runtime_in_seconds
    Type: INT32
    Provider name: MaxAutoMLJobRuntimeInSeconds
    Description: 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.
    
  • max_candidates
    Type: INT32
    Provider name: MaxCandidates
    Description: 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.
    
  • max_runtime_per_training_job_in_seconds
    Type: INT32
    Provider name: MaxRuntimePerTrainingJobInSeconds
    Description: 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).
    
• problem_type
  Type: STRING
  Provider name: ProblemType
  Description: The problem type.
  

role_arn

Type: STRING
Provider name: RoleArn
Description: 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.

tags

Type: UNORDERED_LIST_STRING