com.amazonaws.services.sagemaker.model.CreateHyperParameterTuningJobRequest Maven / Gradle / Ivy
/*
* Copyright 2015-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package com.amazonaws.services.sagemaker.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.AmazonWebServiceRequest;
/**
*
* @see AWS API Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class CreateHyperParameterTuningJobRequest extends com.amazonaws.AmazonWebServiceRequest implements Serializable, Cloneable {
/**
*
* The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job
* launches. The name must be unique within the same AWS account and AWS Region. The name must have 1 to 32
* characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
*
*/
private String hyperParameterTuningJobName;
/**
*
* The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the
* objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the
* tuning job. For more information, see How
* Hyperparameter Tuning Works.
*
*/
private HyperParameterTuningJobConfig hyperParameterTuningJobConfig;
/**
*
* The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job
* launches, including static hyperparameters, input data configuration, output data configuration, resource
* configuration, and stopping condition.
*
*/
private HyperParameterTrainingJobDefinition trainingJobDefinition;
/**
*
* A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
*
*/
private java.util.List trainingJobDefinitions;
/**
*
* Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as
* a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to
* search over in the new tuning job.
*
*
* All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If
* you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start
* configuration, the training job that performs the best in the new tuning job is compared to the best training
* jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the
* objective metric is returned as the overall best training job.
*
*
*
* All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count
* against the limit of training jobs for the tuning job.
*
*
*/
private HyperParameterTuningJobWarmStartConfig warmStartConfig;
/**
*
* 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 Tagging AWS Resources.
*
*
* Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
*
*/
private java.util.List tags;
/**
*
* The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job
* launches. The name must be unique within the same AWS account and AWS Region. The name must have 1 to 32
* characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
*
*
* @param hyperParameterTuningJobName
* The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning
* job launches. The name must be unique within the same AWS account and AWS Region. The name must have 1 to
* 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case
* sensitive.
*/
public void setHyperParameterTuningJobName(String hyperParameterTuningJobName) {
this.hyperParameterTuningJobName = hyperParameterTuningJobName;
}
/**
*
* The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job
* launches. The name must be unique within the same AWS account and AWS Region. The name must have 1 to 32
* characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
*
*
* @return The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning
* job launches. The name must be unique within the same AWS account and AWS Region. The name must have 1 to
* 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case
* sensitive.
*/
public String getHyperParameterTuningJobName() {
return this.hyperParameterTuningJobName;
}
/**
*
* The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job
* launches. The name must be unique within the same AWS account and AWS Region. The name must have 1 to 32
* characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
*
*
* @param hyperParameterTuningJobName
* The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning
* job launches. The name must be unique within the same AWS account and AWS Region. The name must have 1 to
* 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case
* sensitive.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateHyperParameterTuningJobRequest withHyperParameterTuningJobName(String hyperParameterTuningJobName) {
setHyperParameterTuningJobName(hyperParameterTuningJobName);
return this;
}
/**
*
* The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the
* objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the
* tuning job. For more information, see How
* Hyperparameter Tuning Works.
*
*
* @param hyperParameterTuningJobConfig
* The HyperParameterTuningJobConfig object that describes the tuning job, including the search
* strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and
* resource limits for the tuning job. For more information, see How
* Hyperparameter Tuning Works.
*/
public void setHyperParameterTuningJobConfig(HyperParameterTuningJobConfig hyperParameterTuningJobConfig) {
this.hyperParameterTuningJobConfig = hyperParameterTuningJobConfig;
}
/**
*
* The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the
* objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the
* tuning job. For more information, see How
* Hyperparameter Tuning Works.
*
*
* @return The HyperParameterTuningJobConfig object that describes the tuning job, including the search
* strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and
* resource limits for the tuning job. For more information, see How
* Hyperparameter Tuning Works.
*/
public HyperParameterTuningJobConfig getHyperParameterTuningJobConfig() {
return this.hyperParameterTuningJobConfig;
}
/**
*
* The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the
* objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the
* tuning job. For more information, see How
* Hyperparameter Tuning Works.
*
*
* @param hyperParameterTuningJobConfig
* The HyperParameterTuningJobConfig object that describes the tuning job, including the search
* strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and
* resource limits for the tuning job. For more information, see How
* Hyperparameter Tuning Works.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateHyperParameterTuningJobRequest withHyperParameterTuningJobConfig(HyperParameterTuningJobConfig hyperParameterTuningJobConfig) {
setHyperParameterTuningJobConfig(hyperParameterTuningJobConfig);
return this;
}
/**
*
* The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job
* launches, including static hyperparameters, input data configuration, output data configuration, resource
* configuration, and stopping condition.
*
*
* @param trainingJobDefinition
* The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning
* job launches, including static hyperparameters, input data configuration, output data configuration,
* resource configuration, and stopping condition.
*/
public void setTrainingJobDefinition(HyperParameterTrainingJobDefinition trainingJobDefinition) {
this.trainingJobDefinition = trainingJobDefinition;
}
/**
*
* The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job
* launches, including static hyperparameters, input data configuration, output data configuration, resource
* configuration, and stopping condition.
*
*
* @return The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning
* job launches, including static hyperparameters, input data configuration, output data configuration,
* resource configuration, and stopping condition.
*/
public HyperParameterTrainingJobDefinition getTrainingJobDefinition() {
return this.trainingJobDefinition;
}
/**
*
* The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job
* launches, including static hyperparameters, input data configuration, output data configuration, resource
* configuration, and stopping condition.
*
*
* @param trainingJobDefinition
* The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning
* job launches, including static hyperparameters, input data configuration, output data configuration,
* resource configuration, and stopping condition.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateHyperParameterTuningJobRequest withTrainingJobDefinition(HyperParameterTrainingJobDefinition trainingJobDefinition) {
setTrainingJobDefinition(trainingJobDefinition);
return this;
}
/**
*
* A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
*
*
* @return A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
*/
public java.util.List getTrainingJobDefinitions() {
return trainingJobDefinitions;
}
/**
*
* A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
*
*
* @param trainingJobDefinitions
* A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
*/
public void setTrainingJobDefinitions(java.util.Collection trainingJobDefinitions) {
if (trainingJobDefinitions == null) {
this.trainingJobDefinitions = null;
return;
}
this.trainingJobDefinitions = new java.util.ArrayList(trainingJobDefinitions);
}
/**
*
* A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setTrainingJobDefinitions(java.util.Collection)} or
* {@link #withTrainingJobDefinitions(java.util.Collection)} if you want to override the existing values.
*
*
* @param trainingJobDefinitions
* A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateHyperParameterTuningJobRequest withTrainingJobDefinitions(HyperParameterTrainingJobDefinition... trainingJobDefinitions) {
if (this.trainingJobDefinitions == null) {
setTrainingJobDefinitions(new java.util.ArrayList(trainingJobDefinitions.length));
}
for (HyperParameterTrainingJobDefinition ele : trainingJobDefinitions) {
this.trainingJobDefinitions.add(ele);
}
return this;
}
/**
*
* A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
*
*
* @param trainingJobDefinitions
* A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateHyperParameterTuningJobRequest withTrainingJobDefinitions(java.util.Collection trainingJobDefinitions) {
setTrainingJobDefinitions(trainingJobDefinitions);
return this;
}
/**
*
* Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as
* a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to
* search over in the new tuning job.
*
*
* All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If
* you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start
* configuration, the training job that performs the best in the new tuning job is compared to the best training
* jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the
* objective metric is returned as the overall best training job.
*
*
*
* All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count
* against the limit of training jobs for the tuning job.
*
*
*
* @param warmStartConfig
* Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning
* jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of
* hyperparameters to search over in the new tuning job.
*
* All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective
* metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value
* for the warm start configuration, the training job that performs the best in the new tuning job is
* compared to the best training jobs from the parent tuning jobs. From these, the training job that performs
* the best as measured by the objective metric is returned as the overall best training job.
*
*
*
* All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs
* count against the limit of training jobs for the tuning job.
*
*/
public void setWarmStartConfig(HyperParameterTuningJobWarmStartConfig warmStartConfig) {
this.warmStartConfig = warmStartConfig;
}
/**
*
* Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as
* a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to
* search over in the new tuning job.
*
*
* All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If
* you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start
* configuration, the training job that performs the best in the new tuning job is compared to the best training
* jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the
* objective metric is returned as the overall best training job.
*
*
*
* All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count
* against the limit of training jobs for the tuning job.
*
*
*
* @return Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning
* jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of
* hyperparameters to search over in the new tuning job.
*
* All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective
* metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value
* for the warm start configuration, the training job that performs the best in the new tuning job is
* compared to the best training jobs from the parent tuning jobs. From these, the training job that
* performs the best as measured by the objective metric is returned as the overall best training job.
*
*
*
* All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs
* count against the limit of training jobs for the tuning job.
*
*/
public HyperParameterTuningJobWarmStartConfig getWarmStartConfig() {
return this.warmStartConfig;
}
/**
*
* Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as
* a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to
* search over in the new tuning job.
*
*
* All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If
* you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start
* configuration, the training job that performs the best in the new tuning job is compared to the best training
* jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the
* objective metric is returned as the overall best training job.
*
*
*
* All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count
* against the limit of training jobs for the tuning job.
*
*
*
* @param warmStartConfig
* Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning
* jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of
* hyperparameters to search over in the new tuning job.
*
* All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective
* metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value
* for the warm start configuration, the training job that performs the best in the new tuning job is
* compared to the best training jobs from the parent tuning jobs. From these, the training job that performs
* the best as measured by the objective metric is returned as the overall best training job.
*
*
*
* All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs
* count against the limit of training jobs for the tuning job.
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateHyperParameterTuningJobRequest withWarmStartConfig(HyperParameterTuningJobWarmStartConfig warmStartConfig) {
setWarmStartConfig(warmStartConfig);
return this;
}
/**
*
* 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 Tagging AWS Resources.
*
*
* Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
*
*
* @return 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 Tagging AWS Resources.
*
* Tags that you specify for the tuning job are also added to all training jobs that the tuning job
* launches.
*/
public java.util.List getTags() {
return tags;
}
/**
*
* 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 Tagging AWS Resources.
*
*
* Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
*
*
* @param tags
* 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 Tagging AWS Resources.
*
* Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
*/
public void setTags(java.util.Collection tags) {
if (tags == null) {
this.tags = null;
return;
}
this.tags = new java.util.ArrayList(tags);
}
/**
*
* 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 Tagging AWS Resources.
*
*
* Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setTags(java.util.Collection)} or {@link #withTags(java.util.Collection)} if you want to override the
* existing values.
*
*
* @param tags
* 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 Tagging AWS Resources.
*
* Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateHyperParameterTuningJobRequest withTags(Tag... tags) {
if (this.tags == null) {
setTags(new java.util.ArrayList(tags.length));
}
for (Tag ele : tags) {
this.tags.add(ele);
}
return this;
}
/**
*
* 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 Tagging AWS Resources.
*
*
* Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
*
*
* @param tags
* 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 Tagging AWS Resources.
*
* Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public CreateHyperParameterTuningJobRequest withTags(java.util.Collection tags) {
setTags(tags);
return this;
}
/**
* Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be
* redacted from this string using a placeholder value.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getHyperParameterTuningJobName() != null)
sb.append("HyperParameterTuningJobName: ").append(getHyperParameterTuningJobName()).append(",");
if (getHyperParameterTuningJobConfig() != null)
sb.append("HyperParameterTuningJobConfig: ").append(getHyperParameterTuningJobConfig()).append(",");
if (getTrainingJobDefinition() != null)
sb.append("TrainingJobDefinition: ").append(getTrainingJobDefinition()).append(",");
if (getTrainingJobDefinitions() != null)
sb.append("TrainingJobDefinitions: ").append(getTrainingJobDefinitions()).append(",");
if (getWarmStartConfig() != null)
sb.append("WarmStartConfig: ").append(getWarmStartConfig()).append(",");
if (getTags() != null)
sb.append("Tags: ").append(getTags());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof CreateHyperParameterTuningJobRequest == false)
return false;
CreateHyperParameterTuningJobRequest other = (CreateHyperParameterTuningJobRequest) obj;
if (other.getHyperParameterTuningJobName() == null ^ this.getHyperParameterTuningJobName() == null)
return false;
if (other.getHyperParameterTuningJobName() != null && other.getHyperParameterTuningJobName().equals(this.getHyperParameterTuningJobName()) == false)
return false;
if (other.getHyperParameterTuningJobConfig() == null ^ this.getHyperParameterTuningJobConfig() == null)
return false;
if (other.getHyperParameterTuningJobConfig() != null
&& other.getHyperParameterTuningJobConfig().equals(this.getHyperParameterTuningJobConfig()) == false)
return false;
if (other.getTrainingJobDefinition() == null ^ this.getTrainingJobDefinition() == null)
return false;
if (other.getTrainingJobDefinition() != null && other.getTrainingJobDefinition().equals(this.getTrainingJobDefinition()) == false)
return false;
if (other.getTrainingJobDefinitions() == null ^ this.getTrainingJobDefinitions() == null)
return false;
if (other.getTrainingJobDefinitions() != null && other.getTrainingJobDefinitions().equals(this.getTrainingJobDefinitions()) == false)
return false;
if (other.getWarmStartConfig() == null ^ this.getWarmStartConfig() == null)
return false;
if (other.getWarmStartConfig() != null && other.getWarmStartConfig().equals(this.getWarmStartConfig()) == false)
return false;
if (other.getTags() == null ^ this.getTags() == null)
return false;
if (other.getTags() != null && other.getTags().equals(this.getTags()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getHyperParameterTuningJobName() == null) ? 0 : getHyperParameterTuningJobName().hashCode());
hashCode = prime * hashCode + ((getHyperParameterTuningJobConfig() == null) ? 0 : getHyperParameterTuningJobConfig().hashCode());
hashCode = prime * hashCode + ((getTrainingJobDefinition() == null) ? 0 : getTrainingJobDefinition().hashCode());
hashCode = prime * hashCode + ((getTrainingJobDefinitions() == null) ? 0 : getTrainingJobDefinitions().hashCode());
hashCode = prime * hashCode + ((getWarmStartConfig() == null) ? 0 : getWarmStartConfig().hashCode());
hashCode = prime * hashCode + ((getTags() == null) ? 0 : getTags().hashCode());
return hashCode;
}
@Override
public CreateHyperParameterTuningJobRequest clone() {
return (CreateHyperParameterTuningJobRequest) super.clone();
}
}