SageMaker / Paginator / ListCandidatesForAutoMLJob

ListCandidatesForAutoMLJob#

class SageMaker.Paginator.ListCandidatesForAutoMLJob#

paginator = client.get_paginator('list_candidates_for_auto_ml_job')

paginate(**kwargs)#

Creates an iterator that will paginate through responses from SageMaker.Client.list_candidates_for_auto_ml_job().

Request Syntax

response_iterator = paginator.paginate(
    AutoMLJobName='string',
    StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping',
    CandidateNameEquals='string',
    SortOrder='Ascending'|'Descending',
    SortBy='CreationTime'|'Status'|'FinalObjectiveMetricValue',
    PaginationConfig={
        'MaxItems': 123,
        'PageSize': 123,
        'StartingToken': 'string'
    }
)

Parameters:

AutoMLJobName (string) –
[REQUIRED]

List the candidates created for the job by providing the job’s name.
StatusEquals (string) – List the candidates for the job and filter by status.
CandidateNameEquals (string) – List the candidates for the job and filter by candidate name.
SortOrder (string) – The sort order for the results. The default is Ascending.
SortBy (string) – The parameter by which to sort the results. The default is Descending.
PaginationConfig (dict) –
A dictionary that provides parameters to control pagination.
- MaxItems (integer) –
  
  The total number of items to return. If the total number of items available is more than the value specified in max-items then a NextToken will be provided in the output that you can use to resume pagination.
- PageSize (integer) –
  
  The size of each page.
- StartingToken (string) –
  
  A token to specify where to start paginating. This is the NextToken from a previous response.

Return type:

dict

Returns:

Response Syntax

{
    'Candidates': [
        {
            'CandidateName': 'string',
            'FinalAutoMLJobObjectiveMetric': {
                'Type': 'Maximize'|'Minimize',
                'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro',
                'Value': ...,
                'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'
            },
            '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'
                },
                'CandidateMetrics': [
                    {
                        'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro',
                        'Value': ...,
                        'Set': 'Train'|'Validation'|'Test',
                        'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'LogLoss'|'InferenceLatency'
                    },
                ]
            },
            'InferenceContainerDefinitions': {
                'string': [
                    {
                        'Image': 'string',
                        'ModelDataUrl': 'string',
                        'Environment': {
                            'string': 'string'
                        }
                    },
                ]
            }
        },
    ],

}

Response Structure

(dict) –
- Candidates (list) –
  
  Summaries about the AutoMLCandidates.
  - (dict) –
    
    Information about a candidate produced by an AutoML training job, including its status, steps, and other properties.
    - 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.
      - 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 V2 API only (for example, for jobs created by calling CreateAutoMLJobV2).
      - (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) –