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()
.
See also: AWS API Documentation
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'
}
)
[REQUIRED]
List the candidates created for the job by providing the job's name.
Ascending
.Descending
.A dictionary that provides parameters to control pagination.
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.
The size of each page.
A token to specify where to start paginating. This is the NextToken
from a previous response.
dict
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'
},
]
}
},
],
}
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 inference container definitions.
(dict) --
A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see .
Image (string) --
The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see .
ModelDataUrl (string) --
The location of the model artifacts. For more information, see .
Environment (dict) --
The environment variables to set in the container. For more information, see .
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.