481 lines
27 KiB
C++
481 lines
27 KiB
C++
/**
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* Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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* SPDX-License-Identifier: Apache-2.0.
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*/
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#pragma once
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#include <aws/sagemaker/SageMaker_EXPORTS.h>
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#include <aws/sagemaker/SageMakerRequest.h>
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#include <aws/core/utils/memory/stl/AWSString.h>
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#include <aws/sagemaker/model/HyperParameterTuningJobConfig.h>
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#include <aws/sagemaker/model/HyperParameterTrainingJobDefinition.h>
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#include <aws/core/utils/memory/stl/AWSVector.h>
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#include <aws/sagemaker/model/HyperParameterTuningJobWarmStartConfig.h>
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#include <aws/sagemaker/model/Tag.h>
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#include <utility>
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namespace Aws
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{
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namespace SageMaker
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{
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namespace Model
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{
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/**
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*/
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class AWS_SAGEMAKER_API CreateHyperParameterTuningJobRequest : public SageMakerRequest
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{
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public:
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CreateHyperParameterTuningJobRequest();
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// Service request name is the Operation name which will send this request out,
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// each operation should has unique request name, so that we can get operation's name from this request.
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// Note: this is not true for response, multiple operations may have the same response name,
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// so we can not get operation's name from response.
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inline virtual const char* GetServiceRequestName() const override { return "CreateHyperParameterTuningJob"; }
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Aws::String SerializePayload() const override;
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Aws::Http::HeaderValueCollection GetRequestSpecificHeaders() const override;
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/**
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* <p>The name of the tuning job. This name is the prefix for the names of all
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* training jobs that this tuning job launches. The name must be unique within the
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* same AWS account and AWS Region. The name must have { } to { } characters. Valid
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* characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case
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* sensitive.</p>
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*/
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inline const Aws::String& GetHyperParameterTuningJobName() const{ return m_hyperParameterTuningJobName; }
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/**
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* <p>The name of the tuning job. This name is the prefix for the names of all
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* training jobs that this tuning job launches. The name must be unique within the
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* same AWS account and AWS Region. The name must have { } to { } characters. Valid
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* characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case
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* sensitive.</p>
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*/
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inline bool HyperParameterTuningJobNameHasBeenSet() const { return m_hyperParameterTuningJobNameHasBeenSet; }
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/**
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* <p>The name of the tuning job. This name is the prefix for the names of all
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* training jobs that this tuning job launches. The name must be unique within the
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* same AWS account and AWS Region. The name must have { } to { } characters. Valid
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* characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case
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* sensitive.</p>
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*/
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inline void SetHyperParameterTuningJobName(const Aws::String& value) { m_hyperParameterTuningJobNameHasBeenSet = true; m_hyperParameterTuningJobName = value; }
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/**
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* <p>The name of the tuning job. This name is the prefix for the names of all
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* training jobs that this tuning job launches. The name must be unique within the
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* same AWS account and AWS Region. The name must have { } to { } characters. Valid
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* characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case
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* sensitive.</p>
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*/
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inline void SetHyperParameterTuningJobName(Aws::String&& value) { m_hyperParameterTuningJobNameHasBeenSet = true; m_hyperParameterTuningJobName = std::move(value); }
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/**
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* <p>The name of the tuning job. This name is the prefix for the names of all
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* training jobs that this tuning job launches. The name must be unique within the
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* same AWS account and AWS Region. The name must have { } to { } characters. Valid
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* characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case
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* sensitive.</p>
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*/
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inline void SetHyperParameterTuningJobName(const char* value) { m_hyperParameterTuningJobNameHasBeenSet = true; m_hyperParameterTuningJobName.assign(value); }
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/**
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* <p>The name of the tuning job. This name is the prefix for the names of all
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* training jobs that this tuning job launches. The name must be unique within the
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* same AWS account and AWS Region. The name must have { } to { } characters. Valid
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* characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case
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* sensitive.</p>
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*/
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inline CreateHyperParameterTuningJobRequest& WithHyperParameterTuningJobName(const Aws::String& value) { SetHyperParameterTuningJobName(value); return *this;}
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/**
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* <p>The name of the tuning job. This name is the prefix for the names of all
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* training jobs that this tuning job launches. The name must be unique within the
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* same AWS account and AWS Region. The name must have { } to { } characters. Valid
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* characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case
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* sensitive.</p>
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*/
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inline CreateHyperParameterTuningJobRequest& WithHyperParameterTuningJobName(Aws::String&& value) { SetHyperParameterTuningJobName(std::move(value)); return *this;}
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/**
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* <p>The name of the tuning job. This name is the prefix for the names of all
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* training jobs that this tuning job launches. The name must be unique within the
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* same AWS account and AWS Region. The name must have { } to { } characters. Valid
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* characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case
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* sensitive.</p>
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*/
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inline CreateHyperParameterTuningJobRequest& WithHyperParameterTuningJobName(const char* value) { SetHyperParameterTuningJobName(value); return *this;}
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/**
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* <p>The <a>HyperParameterTuningJobConfig</a> object that describes the tuning
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* job, including the search strategy, the objective metric used to evaluate
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* training jobs, ranges of parameters to search, and resource limits for the
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* tuning job. For more information, see <a
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* href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html">How
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* Hyperparameter Tuning Works</a>.</p>
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*/
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inline const HyperParameterTuningJobConfig& GetHyperParameterTuningJobConfig() const{ return m_hyperParameterTuningJobConfig; }
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/**
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* <p>The <a>HyperParameterTuningJobConfig</a> object that describes the tuning
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* job, including the search strategy, the objective metric used to evaluate
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* training jobs, ranges of parameters to search, and resource limits for the
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* tuning job. For more information, see <a
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* href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html">How
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* Hyperparameter Tuning Works</a>.</p>
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*/
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inline bool HyperParameterTuningJobConfigHasBeenSet() const { return m_hyperParameterTuningJobConfigHasBeenSet; }
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/**
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* <p>The <a>HyperParameterTuningJobConfig</a> object that describes the tuning
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* job, including the search strategy, the objective metric used to evaluate
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* training jobs, ranges of parameters to search, and resource limits for the
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* tuning job. For more information, see <a
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* href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html">How
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* Hyperparameter Tuning Works</a>.</p>
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*/
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inline void SetHyperParameterTuningJobConfig(const HyperParameterTuningJobConfig& value) { m_hyperParameterTuningJobConfigHasBeenSet = true; m_hyperParameterTuningJobConfig = value; }
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/**
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* <p>The <a>HyperParameterTuningJobConfig</a> object that describes the tuning
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* job, including the search strategy, the objective metric used to evaluate
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* training jobs, ranges of parameters to search, and resource limits for the
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* tuning job. For more information, see <a
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* href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html">How
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* Hyperparameter Tuning Works</a>.</p>
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*/
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inline void SetHyperParameterTuningJobConfig(HyperParameterTuningJobConfig&& value) { m_hyperParameterTuningJobConfigHasBeenSet = true; m_hyperParameterTuningJobConfig = std::move(value); }
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/**
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* <p>The <a>HyperParameterTuningJobConfig</a> object that describes the tuning
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* job, including the search strategy, the objective metric used to evaluate
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* training jobs, ranges of parameters to search, and resource limits for the
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* tuning job. For more information, see <a
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* href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html">How
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* Hyperparameter Tuning Works</a>.</p>
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*/
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inline CreateHyperParameterTuningJobRequest& WithHyperParameterTuningJobConfig(const HyperParameterTuningJobConfig& value) { SetHyperParameterTuningJobConfig(value); return *this;}
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/**
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* <p>The <a>HyperParameterTuningJobConfig</a> object that describes the tuning
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* job, including the search strategy, the objective metric used to evaluate
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* training jobs, ranges of parameters to search, and resource limits for the
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* tuning job. For more information, see <a
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* href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html">How
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* Hyperparameter Tuning Works</a>.</p>
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*/
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inline CreateHyperParameterTuningJobRequest& WithHyperParameterTuningJobConfig(HyperParameterTuningJobConfig&& value) { SetHyperParameterTuningJobConfig(std::move(value)); return *this;}
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/**
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* <p>The <a>HyperParameterTrainingJobDefinition</a> object that describes the
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* training jobs that this tuning job launches, including static hyperparameters,
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* input data configuration, output data configuration, resource configuration, and
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* stopping condition.</p>
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*/
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inline const HyperParameterTrainingJobDefinition& GetTrainingJobDefinition() const{ return m_trainingJobDefinition; }
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/**
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* <p>The <a>HyperParameterTrainingJobDefinition</a> object that describes the
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* training jobs that this tuning job launches, including static hyperparameters,
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* input data configuration, output data configuration, resource configuration, and
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* stopping condition.</p>
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*/
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inline bool TrainingJobDefinitionHasBeenSet() const { return m_trainingJobDefinitionHasBeenSet; }
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/**
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* <p>The <a>HyperParameterTrainingJobDefinition</a> object that describes the
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* training jobs that this tuning job launches, including static hyperparameters,
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* input data configuration, output data configuration, resource configuration, and
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* stopping condition.</p>
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*/
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inline void SetTrainingJobDefinition(const HyperParameterTrainingJobDefinition& value) { m_trainingJobDefinitionHasBeenSet = true; m_trainingJobDefinition = value; }
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/**
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* <p>The <a>HyperParameterTrainingJobDefinition</a> object that describes the
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* training jobs that this tuning job launches, including static hyperparameters,
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* input data configuration, output data configuration, resource configuration, and
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* stopping condition.</p>
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*/
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inline void SetTrainingJobDefinition(HyperParameterTrainingJobDefinition&& value) { m_trainingJobDefinitionHasBeenSet = true; m_trainingJobDefinition = std::move(value); }
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/**
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* <p>The <a>HyperParameterTrainingJobDefinition</a> object that describes the
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* training jobs that this tuning job launches, including static hyperparameters,
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* input data configuration, output data configuration, resource configuration, and
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* stopping condition.</p>
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*/
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inline CreateHyperParameterTuningJobRequest& WithTrainingJobDefinition(const HyperParameterTrainingJobDefinition& value) { SetTrainingJobDefinition(value); return *this;}
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/**
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* <p>The <a>HyperParameterTrainingJobDefinition</a> object that describes the
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* training jobs that this tuning job launches, including static hyperparameters,
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* input data configuration, output data configuration, resource configuration, and
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* stopping condition.</p>
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*/
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inline CreateHyperParameterTuningJobRequest& WithTrainingJobDefinition(HyperParameterTrainingJobDefinition&& value) { SetTrainingJobDefinition(std::move(value)); return *this;}
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/**
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* <p>A list of the <a>HyperParameterTrainingJobDefinition</a> objects launched for
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* this tuning job.</p>
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*/
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inline const Aws::Vector<HyperParameterTrainingJobDefinition>& GetTrainingJobDefinitions() const{ return m_trainingJobDefinitions; }
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/**
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* <p>A list of the <a>HyperParameterTrainingJobDefinition</a> objects launched for
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* this tuning job.</p>
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*/
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inline bool TrainingJobDefinitionsHasBeenSet() const { return m_trainingJobDefinitionsHasBeenSet; }
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/**
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* <p>A list of the <a>HyperParameterTrainingJobDefinition</a> objects launched for
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* this tuning job.</p>
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*/
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inline void SetTrainingJobDefinitions(const Aws::Vector<HyperParameterTrainingJobDefinition>& value) { m_trainingJobDefinitionsHasBeenSet = true; m_trainingJobDefinitions = value; }
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/**
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* <p>A list of the <a>HyperParameterTrainingJobDefinition</a> objects launched for
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* this tuning job.</p>
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*/
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inline void SetTrainingJobDefinitions(Aws::Vector<HyperParameterTrainingJobDefinition>&& value) { m_trainingJobDefinitionsHasBeenSet = true; m_trainingJobDefinitions = std::move(value); }
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/**
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* <p>A list of the <a>HyperParameterTrainingJobDefinition</a> objects launched for
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* this tuning job.</p>
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*/
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inline CreateHyperParameterTuningJobRequest& WithTrainingJobDefinitions(const Aws::Vector<HyperParameterTrainingJobDefinition>& value) { SetTrainingJobDefinitions(value); return *this;}
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/**
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* <p>A list of the <a>HyperParameterTrainingJobDefinition</a> objects launched for
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* this tuning job.</p>
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*/
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inline CreateHyperParameterTuningJobRequest& WithTrainingJobDefinitions(Aws::Vector<HyperParameterTrainingJobDefinition>&& value) { SetTrainingJobDefinitions(std::move(value)); return *this;}
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/**
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* <p>A list of the <a>HyperParameterTrainingJobDefinition</a> objects launched for
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* this tuning job.</p>
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*/
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inline CreateHyperParameterTuningJobRequest& AddTrainingJobDefinitions(const HyperParameterTrainingJobDefinition& value) { m_trainingJobDefinitionsHasBeenSet = true; m_trainingJobDefinitions.push_back(value); return *this; }
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/**
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* <p>A list of the <a>HyperParameterTrainingJobDefinition</a> objects launched for
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* this tuning job.</p>
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*/
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inline CreateHyperParameterTuningJobRequest& AddTrainingJobDefinitions(HyperParameterTrainingJobDefinition&& value) { m_trainingJobDefinitionsHasBeenSet = true; m_trainingJobDefinitions.push_back(std::move(value)); return *this; }
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/**
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* <p>Specifies the configuration for starting the hyperparameter tuning job using
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* one or more previous tuning jobs as a starting point. The results of previous
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* tuning jobs are used to inform which combinations of hyperparameters to search
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* over in the new tuning job.</p> <p>All training jobs launched by the new
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* hyperparameter tuning job are evaluated by using the objective metric. If you
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* specify <code>IDENTICAL_DATA_AND_ALGORITHM</code> as the
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* <code>WarmStartType</code> value for the warm start configuration, the training
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* job that performs the best in the new tuning job is compared to the best
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* training jobs from the parent tuning jobs. From these, the training job that
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* performs the best as measured by the objective metric is returned as the overall
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* best training job.</p> <p>All training jobs launched by parent
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* hyperparameter tuning jobs and the new hyperparameter tuning jobs count against
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* the limit of training jobs for the tuning job.</p>
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*/
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inline const HyperParameterTuningJobWarmStartConfig& GetWarmStartConfig() const{ return m_warmStartConfig; }
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/**
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* <p>Specifies the configuration for starting the hyperparameter tuning job using
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* one or more previous tuning jobs as a starting point. The results of previous
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* tuning jobs are used to inform which combinations of hyperparameters to search
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* over in the new tuning job.</p> <p>All training jobs launched by the new
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* hyperparameter tuning job are evaluated by using the objective metric. If you
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* specify <code>IDENTICAL_DATA_AND_ALGORITHM</code> as the
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* <code>WarmStartType</code> value for the warm start configuration, the training
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* job that performs the best in the new tuning job is compared to the best
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* training jobs from the parent tuning jobs. From these, the training job that
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* performs the best as measured by the objective metric is returned as the overall
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* best training job.</p> <p>All training jobs launched by parent
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* hyperparameter tuning jobs and the new hyperparameter tuning jobs count against
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* the limit of training jobs for the tuning job.</p>
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*/
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inline bool WarmStartConfigHasBeenSet() const { return m_warmStartConfigHasBeenSet; }
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/**
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* <p>Specifies the configuration for starting the hyperparameter tuning job using
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* one or more previous tuning jobs as a starting point. The results of previous
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* tuning jobs are used to inform which combinations of hyperparameters to search
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* over in the new tuning job.</p> <p>All training jobs launched by the new
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* hyperparameter tuning job are evaluated by using the objective metric. If you
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* specify <code>IDENTICAL_DATA_AND_ALGORITHM</code> as the
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* <code>WarmStartType</code> value for the warm start configuration, the training
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* job that performs the best in the new tuning job is compared to the best
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* training jobs from the parent tuning jobs. From these, the training job that
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* performs the best as measured by the objective metric is returned as the overall
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* best training job.</p> <p>All training jobs launched by parent
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* hyperparameter tuning jobs and the new hyperparameter tuning jobs count against
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* the limit of training jobs for the tuning job.</p>
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*/
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inline void SetWarmStartConfig(const HyperParameterTuningJobWarmStartConfig& value) { m_warmStartConfigHasBeenSet = true; m_warmStartConfig = value; }
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/**
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* <p>Specifies the configuration for starting the hyperparameter tuning job using
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* one or more previous tuning jobs as a starting point. The results of previous
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* tuning jobs are used to inform which combinations of hyperparameters to search
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* over in the new tuning job.</p> <p>All training jobs launched by the new
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* hyperparameter tuning job are evaluated by using the objective metric. If you
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* specify <code>IDENTICAL_DATA_AND_ALGORITHM</code> as the
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* <code>WarmStartType</code> value for the warm start configuration, the training
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* job that performs the best in the new tuning job is compared to the best
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* training jobs from the parent tuning jobs. From these, the training job that
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* performs the best as measured by the objective metric is returned as the overall
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* best training job.</p> <p>All training jobs launched by parent
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* hyperparameter tuning jobs and the new hyperparameter tuning jobs count against
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* the limit of training jobs for the tuning job.</p>
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*/
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inline void SetWarmStartConfig(HyperParameterTuningJobWarmStartConfig&& value) { m_warmStartConfigHasBeenSet = true; m_warmStartConfig = std::move(value); }
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/**
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* <p>Specifies the configuration for starting the hyperparameter tuning job using
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* one or more previous tuning jobs as a starting point. The results of previous
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* tuning jobs are used to inform which combinations of hyperparameters to search
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* over in the new tuning job.</p> <p>All training jobs launched by the new
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* hyperparameter tuning job are evaluated by using the objective metric. If you
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* specify <code>IDENTICAL_DATA_AND_ALGORITHM</code> as the
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* <code>WarmStartType</code> value for the warm start configuration, the training
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* job that performs the best in the new tuning job is compared to the best
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* training jobs from the parent tuning jobs. From these, the training job that
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* performs the best as measured by the objective metric is returned as the overall
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* best training job.</p> <p>All training jobs launched by parent
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* hyperparameter tuning jobs and the new hyperparameter tuning jobs count against
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* the limit of training jobs for the tuning job.</p>
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*/
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inline CreateHyperParameterTuningJobRequest& WithWarmStartConfig(const HyperParameterTuningJobWarmStartConfig& value) { SetWarmStartConfig(value); return *this;}
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/**
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* <p>Specifies the configuration for starting the hyperparameter tuning job using
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* one or more previous tuning jobs as a starting point. The results of previous
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* tuning jobs are used to inform which combinations of hyperparameters to search
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* over in the new tuning job.</p> <p>All training jobs launched by the new
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* hyperparameter tuning job are evaluated by using the objective metric. If you
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* specify <code>IDENTICAL_DATA_AND_ALGORITHM</code> as the
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* <code>WarmStartType</code> value for the warm start configuration, the training
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* job that performs the best in the new tuning job is compared to the best
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* training jobs from the parent tuning jobs. From these, the training job that
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* performs the best as measured by the objective metric is returned as the overall
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* best training job.</p> <p>All training jobs launched by parent
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* hyperparameter tuning jobs and the new hyperparameter tuning jobs count against
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* the limit of training jobs for the tuning job.</p>
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*/
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inline CreateHyperParameterTuningJobRequest& WithWarmStartConfig(HyperParameterTuningJobWarmStartConfig&& value) { SetWarmStartConfig(std::move(value)); return *this;}
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/**
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* <p>An array of key-value pairs. You can use tags to categorize your AWS
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* resources in different ways, for example, by purpose, owner, or environment. For
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* more information, see <a
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* href="https://aws.amazon.com/answers/account-management/aws-tagging-strategies/">AWS
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* Tagging Strategies</a>.</p> <p>Tags that you specify for the tuning job are also
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* added to all training jobs that the tuning job launches.</p>
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*/
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inline const Aws::Vector<Tag>& GetTags() const{ return m_tags; }
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/**
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* <p>An array of key-value pairs. You can use tags to categorize your AWS
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* resources in different ways, for example, by purpose, owner, or environment. For
|
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* more information, see <a
|
|
* href="https://aws.amazon.com/answers/account-management/aws-tagging-strategies/">AWS
|
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* Tagging Strategies</a>.</p> <p>Tags that you specify for the tuning job are also
|
|
* added to all training jobs that the tuning job launches.</p>
|
|
*/
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inline bool TagsHasBeenSet() const { return m_tagsHasBeenSet; }
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|
|
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/**
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|
* <p>An array of key-value pairs. You can use tags to categorize your AWS
|
|
* resources in different ways, for example, by purpose, owner, or environment. For
|
|
* more information, see <a
|
|
* href="https://aws.amazon.com/answers/account-management/aws-tagging-strategies/">AWS
|
|
* Tagging Strategies</a>.</p> <p>Tags that you specify for the tuning job are also
|
|
* added to all training jobs that the tuning job launches.</p>
|
|
*/
|
|
inline void SetTags(const Aws::Vector<Tag>& value) { m_tagsHasBeenSet = true; m_tags = value; }
|
|
|
|
/**
|
|
* <p>An array of key-value pairs. You can use tags to categorize your AWS
|
|
* resources in different ways, for example, by purpose, owner, or environment. For
|
|
* more information, see <a
|
|
* href="https://aws.amazon.com/answers/account-management/aws-tagging-strategies/">AWS
|
|
* Tagging Strategies</a>.</p> <p>Tags that you specify for the tuning job are also
|
|
* added to all training jobs that the tuning job launches.</p>
|
|
*/
|
|
inline void SetTags(Aws::Vector<Tag>&& value) { m_tagsHasBeenSet = true; m_tags = std::move(value); }
|
|
|
|
/**
|
|
* <p>An array of key-value pairs. You can use tags to categorize your AWS
|
|
* resources in different ways, for example, by purpose, owner, or environment. For
|
|
* more information, see <a
|
|
* href="https://aws.amazon.com/answers/account-management/aws-tagging-strategies/">AWS
|
|
* Tagging Strategies</a>.</p> <p>Tags that you specify for the tuning job are also
|
|
* added to all training jobs that the tuning job launches.</p>
|
|
*/
|
|
inline CreateHyperParameterTuningJobRequest& WithTags(const Aws::Vector<Tag>& value) { SetTags(value); return *this;}
|
|
|
|
/**
|
|
* <p>An array of key-value pairs. You can use tags to categorize your AWS
|
|
* resources in different ways, for example, by purpose, owner, or environment. For
|
|
* more information, see <a
|
|
* href="https://aws.amazon.com/answers/account-management/aws-tagging-strategies/">AWS
|
|
* Tagging Strategies</a>.</p> <p>Tags that you specify for the tuning job are also
|
|
* added to all training jobs that the tuning job launches.</p>
|
|
*/
|
|
inline CreateHyperParameterTuningJobRequest& WithTags(Aws::Vector<Tag>&& value) { SetTags(std::move(value)); return *this;}
|
|
|
|
/**
|
|
* <p>An array of key-value pairs. You can use tags to categorize your AWS
|
|
* resources in different ways, for example, by purpose, owner, or environment. For
|
|
* more information, see <a
|
|
* href="https://aws.amazon.com/answers/account-management/aws-tagging-strategies/">AWS
|
|
* Tagging Strategies</a>.</p> <p>Tags that you specify for the tuning job are also
|
|
* added to all training jobs that the tuning job launches.</p>
|
|
*/
|
|
inline CreateHyperParameterTuningJobRequest& AddTags(const Tag& value) { m_tagsHasBeenSet = true; m_tags.push_back(value); return *this; }
|
|
|
|
/**
|
|
* <p>An array of key-value pairs. You can use tags to categorize your AWS
|
|
* resources in different ways, for example, by purpose, owner, or environment. For
|
|
* more information, see <a
|
|
* href="https://aws.amazon.com/answers/account-management/aws-tagging-strategies/">AWS
|
|
* Tagging Strategies</a>.</p> <p>Tags that you specify for the tuning job are also
|
|
* added to all training jobs that the tuning job launches.</p>
|
|
*/
|
|
inline CreateHyperParameterTuningJobRequest& AddTags(Tag&& value) { m_tagsHasBeenSet = true; m_tags.push_back(std::move(value)); return *this; }
|
|
|
|
private:
|
|
|
|
Aws::String m_hyperParameterTuningJobName;
|
|
bool m_hyperParameterTuningJobNameHasBeenSet;
|
|
|
|
HyperParameterTuningJobConfig m_hyperParameterTuningJobConfig;
|
|
bool m_hyperParameterTuningJobConfigHasBeenSet;
|
|
|
|
HyperParameterTrainingJobDefinition m_trainingJobDefinition;
|
|
bool m_trainingJobDefinitionHasBeenSet;
|
|
|
|
Aws::Vector<HyperParameterTrainingJobDefinition> m_trainingJobDefinitions;
|
|
bool m_trainingJobDefinitionsHasBeenSet;
|
|
|
|
HyperParameterTuningJobWarmStartConfig m_warmStartConfig;
|
|
bool m_warmStartConfigHasBeenSet;
|
|
|
|
Aws::Vector<Tag> m_tags;
|
|
bool m_tagsHasBeenSet;
|
|
};
|
|
|
|
} // namespace Model
|
|
} // namespace SageMaker
|
|
} // namespace Aws
|