/* * Copyright 2018-2023 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 Amazon Web Services account and Amazon Web Services 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 Amazon Web Services resources in different ways, * for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services 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; /** *

* Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following * fields: *

* */ private Autotune autotune; /** *

* 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 Amazon Web Services account and Amazon Web Services 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 Amazon Web Services account and Amazon Web Services * 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 Amazon Web Services account and Amazon Web Services 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 Amazon Web Services account and Amazon Web Services * 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 Amazon Web Services account and Amazon Web Services 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 Amazon Web Services account and Amazon Web Services * 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 Amazon Web Services resources in different ways, * for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services 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 Amazon Web Services resources in * different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services * 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 Amazon Web Services resources in different ways, * for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services 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 Amazon Web Services resources in * different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services * 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 Amazon Web Services resources in different ways, * for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services 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 Amazon Web Services resources in * different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services * 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 Amazon Web Services resources in different ways, * for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services 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 Amazon Web Services resources in * different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services * 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; } /** *

* Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following * fields: *

*
    *
  • *

    * ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize. *

    *
    • *
  • *

    * ResourceLimits: * The maximum resources that can be used for a training job. These resources include the maximum number of training * jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time. *

    *
    • *
  • *

    * TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs * launched by a hyperparameter tuning job. *

    *
    • *
  • *

    * RetryStrategy: The number of times to retry a training job. *

    *
    • *
  • *

    * * Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for * the training jobs that it launches. *

    *
    • *
  • *

    * * ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence. *

    *
    • *
* * @param autotune * Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the * following fields:

*
    *
  • *

    * ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize. *

    *
    • *
  • *

    * ResourceLimits< * /a>: The maximum resources that can be used for a training job. These resources include the maximum number * of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at * the same time. *

    *
    • *
  • *

    * TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training * jobs launched by a hyperparameter tuning job. *

    *
    • *
  • *

    * RetryStrategy: The number of times to retry a training job. *

    *
    • *
  • *

    * * Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use * for the training jobs that it launches. *

    *
    • *
  • *

    * * ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model * convergence. *

    *
    • */ public void setAutotune(Autotune autotune) { this.autotune = autotune; } /** *

    * Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following * fields: *

    *
      *
    • *

      * ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize. *

      *
      • *
    • *

      * ResourceLimits: * The maximum resources that can be used for a training job. These resources include the maximum number of training * jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time. *

      *
      • *
    • *

      * TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs * launched by a hyperparameter tuning job. *

      *
      • *
    • *

      * RetryStrategy: The number of times to retry a training job. *

      *
      • *
    • *

      * * Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for * the training jobs that it launches. *

      *
      • *
    • *

      * * ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence. *

      *
      • *
    * * @return Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the * following fields:

    *
      *
    • *

      * ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize. *

      *
      • *
    • *

      * ResourceLimits * : The maximum resources that can be used for a training job. These resources include the maximum * number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to * run at the same time. *

      *
      • *
    • *

      * TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for * training jobs launched by a hyperparameter tuning job. *

      *
      • *
    • *

      * RetryStrategy: The number of times to retry a training job. *

      *
      • *
    • *

      * Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to * use for the training jobs that it launches. *

      *
      • *
    • *

      * * ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model * convergence. *

      *
      • */ public Autotune getAutotune() { return this.autotune; } /** *

      * Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following * fields: *

      *
        *
      • *

        * ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize. *

        *
        • *
      • *

        * ResourceLimits: * The maximum resources that can be used for a training job. These resources include the maximum number of training * jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time. *

        *
        • *
      • *

        * TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs * launched by a hyperparameter tuning job. *

        *
        • *
      • *

        * RetryStrategy: The number of times to retry a training job. *

        *
        • *
      • *

        * * Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for * the training jobs that it launches. *

        *
        • *
      • *

        * * ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence. *

        *
        • *
      * * @param autotune * Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the * following fields:

      *
        *
      • *

        * ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize. *

        *
        • *
      • *

        * ResourceLimits< * /a>: The maximum resources that can be used for a training job. These resources include the maximum number * of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at * the same time. *

        *
        • *
      • *

        * TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training * jobs launched by a hyperparameter tuning job. *

        *
        • *
      • *

        * RetryStrategy: The number of times to retry a training job. *

        *
        • *
      • *

        * * Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use * for the training jobs that it launches. *

        *
        • *
      • *

        * * ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model * convergence. *

        *
        • * @return Returns a reference to this object so that method calls can be chained together. */ public CreateHyperParameterTuningJobRequest withAutotune(Autotune autotune) { setAutotune(autotune); 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()).append(","); if (getAutotune() != null) sb.append("Autotune: ").append(getAutotune()); 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; if (other.getAutotune() == null ^ this.getAutotune() == null) return false; if (other.getAutotune() != null && other.getAutotune().equals(this.getAutotune()) == 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()); hashCode = prime * hashCode + ((getAutotune() == null) ? 0 : getAutotune().hashCode()); return hashCode; } @Override public CreateHyperParameterTuningJobRequest clone() { return (CreateHyperParameterTuningJobRequest) super.clone(); } }