com.amazonaws.services.sagemaker.model.HyperParameterTrainingJobDefinition 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.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* Defines the training jobs launched by a hyperparameter tuning job.
*
*
* @see AWS API Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class HyperParameterTrainingJobDefinition implements Serializable, Cloneable, StructuredPojo {
/**
*
* The job definition name.
*
*/
private String definitionName;
private HyperParameterTuningJobObjective tuningObjective;
private ParameterRanges hyperParameterRanges;
/**
*
* Specifies the values of hyperparameters that do not change for the tuning job.
*
*/
private java.util.Map staticHyperParameters;
/**
*
* The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the
* training jobs that the tuning job launches.
*
*/
private HyperParameterAlgorithmSpecification algorithmSpecification;
/**
*
* The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
*
*/
private String roleArn;
/**
*
* An array of Channel objects that specify the input for the training jobs that the tuning job launches.
*
*/
private java.util.List inputDataConfig;
/**
*
* The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter
* tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For
* more information, see Protect Training
* Jobs by Using an Amazon Virtual Private Cloud.
*
*/
private VpcConfig vpcConfig;
/**
*
* Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning
* job launches.
*
*/
private OutputDataConfig outputDataConfig;
/**
*
* The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning
* job launches.
*
*
* Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes
* for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose
* File as the TrainingInputMode in the algorithm specification. For distributed training
* algorithms, specify an instance count greater than 1.
*
*/
private ResourceConfig resourceConfig;
/**
*
* Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long you are
* willing to wait for a managed spot training job to complete. When the job reaches the a limit, Amazon SageMaker
* ends the training job. Use this API to cap model training costs.
*
*/
private StoppingCondition stoppingCondition;
/**
*
* Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers
* within a training cluster for distributed training. If network isolation is used for training jobs that are
* configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the
* specified VPC, but the training container does not have network access.
*
*/
private Boolean enableNetworkIsolation;
/**
*
* To encrypt all communications between ML compute instances in distributed training, choose True.
* Encryption provides greater security for distributed training, but training might take longer. How long it takes
* depends on the amount of communication between compute instances, especially if you use a deep learning algorithm
* in distributed training.
*
*/
private Boolean enableInterContainerTrafficEncryption;
/**
*
* A Boolean indicating whether managed spot training is enabled (True) or not (False).
*
*/
private Boolean enableManagedSpotTraining;
private CheckpointConfig checkpointConfig;
/**
*
* The job definition name.
*
*
* @param definitionName
* The job definition name.
*/
public void setDefinitionName(String definitionName) {
this.definitionName = definitionName;
}
/**
*
* The job definition name.
*
*
* @return The job definition name.
*/
public String getDefinitionName() {
return this.definitionName;
}
/**
*
* The job definition name.
*
*
* @param definitionName
* The job definition name.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withDefinitionName(String definitionName) {
setDefinitionName(definitionName);
return this;
}
/**
* @param tuningObjective
*/
public void setTuningObjective(HyperParameterTuningJobObjective tuningObjective) {
this.tuningObjective = tuningObjective;
}
/**
* @return
*/
public HyperParameterTuningJobObjective getTuningObjective() {
return this.tuningObjective;
}
/**
* @param tuningObjective
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withTuningObjective(HyperParameterTuningJobObjective tuningObjective) {
setTuningObjective(tuningObjective);
return this;
}
/**
* @param hyperParameterRanges
*/
public void setHyperParameterRanges(ParameterRanges hyperParameterRanges) {
this.hyperParameterRanges = hyperParameterRanges;
}
/**
* @return
*/
public ParameterRanges getHyperParameterRanges() {
return this.hyperParameterRanges;
}
/**
* @param hyperParameterRanges
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withHyperParameterRanges(ParameterRanges hyperParameterRanges) {
setHyperParameterRanges(hyperParameterRanges);
return this;
}
/**
*
* Specifies the values of hyperparameters that do not change for the tuning job.
*
*
* @return Specifies the values of hyperparameters that do not change for the tuning job.
*/
public java.util.Map getStaticHyperParameters() {
return staticHyperParameters;
}
/**
*
* Specifies the values of hyperparameters that do not change for the tuning job.
*
*
* @param staticHyperParameters
* Specifies the values of hyperparameters that do not change for the tuning job.
*/
public void setStaticHyperParameters(java.util.Map staticHyperParameters) {
this.staticHyperParameters = staticHyperParameters;
}
/**
*
* Specifies the values of hyperparameters that do not change for the tuning job.
*
*
* @param staticHyperParameters
* Specifies the values of hyperparameters that do not change for the tuning job.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withStaticHyperParameters(java.util.Map staticHyperParameters) {
setStaticHyperParameters(staticHyperParameters);
return this;
}
/**
* Add a single StaticHyperParameters entry
*
* @see HyperParameterTrainingJobDefinition#withStaticHyperParameters
* @returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition addStaticHyperParametersEntry(String key, String value) {
if (null == this.staticHyperParameters) {
this.staticHyperParameters = new java.util.HashMap();
}
if (this.staticHyperParameters.containsKey(key))
throw new IllegalArgumentException("Duplicated keys (" + key.toString() + ") are provided.");
this.staticHyperParameters.put(key, value);
return this;
}
/**
* Removes all the entries added into StaticHyperParameters.
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition clearStaticHyperParametersEntries() {
this.staticHyperParameters = null;
return this;
}
/**
*
* The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the
* training jobs that the tuning job launches.
*
*
* @param algorithmSpecification
* The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for
* the training jobs that the tuning job launches.
*/
public void setAlgorithmSpecification(HyperParameterAlgorithmSpecification algorithmSpecification) {
this.algorithmSpecification = algorithmSpecification;
}
/**
*
* The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the
* training jobs that the tuning job launches.
*
*
* @return The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for
* the training jobs that the tuning job launches.
*/
public HyperParameterAlgorithmSpecification getAlgorithmSpecification() {
return this.algorithmSpecification;
}
/**
*
* The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the
* training jobs that the tuning job launches.
*
*
* @param algorithmSpecification
* The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for
* the training jobs that the tuning job launches.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withAlgorithmSpecification(HyperParameterAlgorithmSpecification algorithmSpecification) {
setAlgorithmSpecification(algorithmSpecification);
return this;
}
/**
*
* The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
*
*
* @param roleArn
* The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job
* launches.
*/
public void setRoleArn(String roleArn) {
this.roleArn = roleArn;
}
/**
*
* The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
*
*
* @return The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job
* launches.
*/
public String getRoleArn() {
return this.roleArn;
}
/**
*
* The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
*
*
* @param roleArn
* The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job
* launches.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withRoleArn(String roleArn) {
setRoleArn(roleArn);
return this;
}
/**
*
* An array of Channel objects that specify the input for the training jobs that the tuning job launches.
*
*
* @return An array of Channel objects that specify the input for the training jobs that the tuning job
* launches.
*/
public java.util.List getInputDataConfig() {
return inputDataConfig;
}
/**
*
* An array of Channel objects that specify the input for the training jobs that the tuning job launches.
*
*
* @param inputDataConfig
* An array of Channel objects that specify the input for the training jobs that the tuning job
* launches.
*/
public void setInputDataConfig(java.util.Collection inputDataConfig) {
if (inputDataConfig == null) {
this.inputDataConfig = null;
return;
}
this.inputDataConfig = new java.util.ArrayList(inputDataConfig);
}
/**
*
* An array of Channel objects that specify the input for the training jobs that the tuning job launches.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setInputDataConfig(java.util.Collection)} or {@link #withInputDataConfig(java.util.Collection)} if you
* want to override the existing values.
*
*
* @param inputDataConfig
* An array of Channel objects that specify the input for the training jobs that the tuning job
* launches.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withInputDataConfig(Channel... inputDataConfig) {
if (this.inputDataConfig == null) {
setInputDataConfig(new java.util.ArrayList(inputDataConfig.length));
}
for (Channel ele : inputDataConfig) {
this.inputDataConfig.add(ele);
}
return this;
}
/**
*
* An array of Channel objects that specify the input for the training jobs that the tuning job launches.
*
*
* @param inputDataConfig
* An array of Channel objects that specify the input for the training jobs that the tuning job
* launches.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withInputDataConfig(java.util.Collection inputDataConfig) {
setInputDataConfig(inputDataConfig);
return this;
}
/**
*
* The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter
* tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For
* more information, see Protect Training
* Jobs by Using an Amazon Virtual Private Cloud.
*
*
* @param vpcConfig
* The VpcConfig object that specifies the VPC that you want the training jobs that this
* hyperparameter tuning job launches to connect to. Control access to and from your training container by
* configuring the VPC. For more information, see Protect Training Jobs by Using an
* Amazon Virtual Private Cloud.
*/
public void setVpcConfig(VpcConfig vpcConfig) {
this.vpcConfig = vpcConfig;
}
/**
*
* The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter
* tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For
* more information, see Protect Training
* Jobs by Using an Amazon Virtual Private Cloud.
*
*
* @return The VpcConfig object that specifies the VPC that you want the training jobs that this
* hyperparameter tuning job launches to connect to. Control access to and from your training container by
* configuring the VPC. For more information, see Protect Training Jobs by Using an
* Amazon Virtual Private Cloud.
*/
public VpcConfig getVpcConfig() {
return this.vpcConfig;
}
/**
*
* The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter
* tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For
* more information, see Protect Training
* Jobs by Using an Amazon Virtual Private Cloud.
*
*
* @param vpcConfig
* The VpcConfig object that specifies the VPC that you want the training jobs that this
* hyperparameter tuning job launches to connect to. Control access to and from your training container by
* configuring the VPC. For more information, see Protect Training Jobs by Using an
* Amazon Virtual Private Cloud.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withVpcConfig(VpcConfig vpcConfig) {
setVpcConfig(vpcConfig);
return this;
}
/**
*
* Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning
* job launches.
*
*
* @param outputDataConfig
* Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the
* tuning job launches.
*/
public void setOutputDataConfig(OutputDataConfig outputDataConfig) {
this.outputDataConfig = outputDataConfig;
}
/**
*
* Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning
* job launches.
*
*
* @return Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that
* the tuning job launches.
*/
public OutputDataConfig getOutputDataConfig() {
return this.outputDataConfig;
}
/**
*
* Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning
* job launches.
*
*
* @param outputDataConfig
* Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the
* tuning job launches.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withOutputDataConfig(OutputDataConfig outputDataConfig) {
setOutputDataConfig(outputDataConfig);
return this;
}
/**
*
* The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning
* job launches.
*
*
* Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes
* for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose
* File as the TrainingInputMode in the algorithm specification. For distributed training
* algorithms, specify an instance count greater than 1.
*
*
* @param resourceConfig
* The resources, including the compute instances and storage volumes, to use for the training jobs that the
* tuning job launches.
*
* Storage volumes store model artifacts and incremental states. Training algorithms might also use storage
* volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training
* data, choose File as the TrainingInputMode in the algorithm specification. For
* distributed training algorithms, specify an instance count greater than 1.
*/
public void setResourceConfig(ResourceConfig resourceConfig) {
this.resourceConfig = resourceConfig;
}
/**
*
* The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning
* job launches.
*
*
* Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes
* for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose
* File as the TrainingInputMode in the algorithm specification. For distributed training
* algorithms, specify an instance count greater than 1.
*
*
* @return The resources, including the compute instances and storage volumes, to use for the training jobs that the
* tuning job launches.
*
* Storage volumes store model artifacts and incremental states. Training algorithms might also use storage
* volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training
* data, choose File as the TrainingInputMode in the algorithm specification. For
* distributed training algorithms, specify an instance count greater than 1.
*/
public ResourceConfig getResourceConfig() {
return this.resourceConfig;
}
/**
*
* The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning
* job launches.
*
*
* Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes
* for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose
* File as the TrainingInputMode in the algorithm specification. For distributed training
* algorithms, specify an instance count greater than 1.
*
*
* @param resourceConfig
* The resources, including the compute instances and storage volumes, to use for the training jobs that the
* tuning job launches.
*
* Storage volumes store model artifacts and incremental states. Training algorithms might also use storage
* volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training
* data, choose File as the TrainingInputMode in the algorithm specification. For
* distributed training algorithms, specify an instance count greater than 1.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withResourceConfig(ResourceConfig resourceConfig) {
setResourceConfig(resourceConfig);
return this;
}
/**
*
* Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long you are
* willing to wait for a managed spot training job to complete. When the job reaches the a limit, Amazon SageMaker
* ends the training job. Use this API to cap model training costs.
*
*
* @param stoppingCondition
* Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long you
* are willing to wait for a managed spot training job to complete. When the job reaches the a limit, Amazon
* SageMaker ends the training job. Use this API to cap model training costs.
*/
public void setStoppingCondition(StoppingCondition stoppingCondition) {
this.stoppingCondition = stoppingCondition;
}
/**
*
* Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long you are
* willing to wait for a managed spot training job to complete. When the job reaches the a limit, Amazon SageMaker
* ends the training job. Use this API to cap model training costs.
*
*
* @return Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long you
* are willing to wait for a managed spot training job to complete. When the job reaches the a limit, Amazon
* SageMaker ends the training job. Use this API to cap model training costs.
*/
public StoppingCondition getStoppingCondition() {
return this.stoppingCondition;
}
/**
*
* Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long you are
* willing to wait for a managed spot training job to complete. When the job reaches the a limit, Amazon SageMaker
* ends the training job. Use this API to cap model training costs.
*
*
* @param stoppingCondition
* Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long you
* are willing to wait for a managed spot training job to complete. When the job reaches the a limit, Amazon
* SageMaker ends the training job. Use this API to cap model training costs.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withStoppingCondition(StoppingCondition stoppingCondition) {
setStoppingCondition(stoppingCondition);
return this;
}
/**
*
* Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers
* within a training cluster for distributed training. If network isolation is used for training jobs that are
* configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the
* specified VPC, but the training container does not have network access.
*
*
* @param enableNetworkIsolation
* Isolates the training container. No inbound or outbound network calls can be made, except for calls
* between peers within a training cluster for distributed training. If network isolation is used for
* training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and
* model artifacts through the specified VPC, but the training container does not have network access.
*/
public void setEnableNetworkIsolation(Boolean enableNetworkIsolation) {
this.enableNetworkIsolation = enableNetworkIsolation;
}
/**
*
* Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers
* within a training cluster for distributed training. If network isolation is used for training jobs that are
* configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the
* specified VPC, but the training container does not have network access.
*
*
* @return Isolates the training container. No inbound or outbound network calls can be made, except for calls
* between peers within a training cluster for distributed training. If network isolation is used for
* training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and
* model artifacts through the specified VPC, but the training container does not have network access.
*/
public Boolean getEnableNetworkIsolation() {
return this.enableNetworkIsolation;
}
/**
*
* Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers
* within a training cluster for distributed training. If network isolation is used for training jobs that are
* configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the
* specified VPC, but the training container does not have network access.
*
*
* @param enableNetworkIsolation
* Isolates the training container. No inbound or outbound network calls can be made, except for calls
* between peers within a training cluster for distributed training. If network isolation is used for
* training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and
* model artifacts through the specified VPC, but the training container does not have network access.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withEnableNetworkIsolation(Boolean enableNetworkIsolation) {
setEnableNetworkIsolation(enableNetworkIsolation);
return this;
}
/**
*
* Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers
* within a training cluster for distributed training. If network isolation is used for training jobs that are
* configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the
* specified VPC, but the training container does not have network access.
*
*
* @return Isolates the training container. No inbound or outbound network calls can be made, except for calls
* between peers within a training cluster for distributed training. If network isolation is used for
* training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and
* model artifacts through the specified VPC, but the training container does not have network access.
*/
public Boolean isEnableNetworkIsolation() {
return this.enableNetworkIsolation;
}
/**
*
* To encrypt all communications between ML compute instances in distributed training, choose True.
* Encryption provides greater security for distributed training, but training might take longer. How long it takes
* depends on the amount of communication between compute instances, especially if you use a deep learning algorithm
* in distributed training.
*
*
* @param enableInterContainerTrafficEncryption
* To encrypt all communications between ML compute instances in distributed training, choose
* True. Encryption provides greater security for distributed training, but training might take
* longer. How long it takes depends on the amount of communication between compute instances, especially if
* you use a deep learning algorithm in distributed training.
*/
public void setEnableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption) {
this.enableInterContainerTrafficEncryption = enableInterContainerTrafficEncryption;
}
/**
*
* To encrypt all communications between ML compute instances in distributed training, choose True.
* Encryption provides greater security for distributed training, but training might take longer. How long it takes
* depends on the amount of communication between compute instances, especially if you use a deep learning algorithm
* in distributed training.
*
*
* @return To encrypt all communications between ML compute instances in distributed training, choose
* True. Encryption provides greater security for distributed training, but training might take
* longer. How long it takes depends on the amount of communication between compute instances, especially if
* you use a deep learning algorithm in distributed training.
*/
public Boolean getEnableInterContainerTrafficEncryption() {
return this.enableInterContainerTrafficEncryption;
}
/**
*
* To encrypt all communications between ML compute instances in distributed training, choose True.
* Encryption provides greater security for distributed training, but training might take longer. How long it takes
* depends on the amount of communication between compute instances, especially if you use a deep learning algorithm
* in distributed training.
*
*
* @param enableInterContainerTrafficEncryption
* To encrypt all communications between ML compute instances in distributed training, choose
* True. Encryption provides greater security for distributed training, but training might take
* longer. How long it takes depends on the amount of communication between compute instances, especially if
* you use a deep learning algorithm in distributed training.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withEnableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption) {
setEnableInterContainerTrafficEncryption(enableInterContainerTrafficEncryption);
return this;
}
/**
*
* To encrypt all communications between ML compute instances in distributed training, choose True.
* Encryption provides greater security for distributed training, but training might take longer. How long it takes
* depends on the amount of communication between compute instances, especially if you use a deep learning algorithm
* in distributed training.
*
*
* @return To encrypt all communications between ML compute instances in distributed training, choose
* True. Encryption provides greater security for distributed training, but training might take
* longer. How long it takes depends on the amount of communication between compute instances, especially if
* you use a deep learning algorithm in distributed training.
*/
public Boolean isEnableInterContainerTrafficEncryption() {
return this.enableInterContainerTrafficEncryption;
}
/**
*
* A Boolean indicating whether managed spot training is enabled (True) or not (False).
*
*
* @param enableManagedSpotTraining
* A Boolean indicating whether managed spot training is enabled (True) or not (
* False).
*/
public void setEnableManagedSpotTraining(Boolean enableManagedSpotTraining) {
this.enableManagedSpotTraining = enableManagedSpotTraining;
}
/**
*
* A Boolean indicating whether managed spot training is enabled (True) or not (False).
*
*
* @return A Boolean indicating whether managed spot training is enabled (True) or not (
* False).
*/
public Boolean getEnableManagedSpotTraining() {
return this.enableManagedSpotTraining;
}
/**
*
* A Boolean indicating whether managed spot training is enabled (True) or not (False).
*
*
* @param enableManagedSpotTraining
* A Boolean indicating whether managed spot training is enabled (True) or not (
* False).
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withEnableManagedSpotTraining(Boolean enableManagedSpotTraining) {
setEnableManagedSpotTraining(enableManagedSpotTraining);
return this;
}
/**
*
* A Boolean indicating whether managed spot training is enabled (True) or not (False).
*
*
* @return A Boolean indicating whether managed spot training is enabled (True) or not (
* False).
*/
public Boolean isEnableManagedSpotTraining() {
return this.enableManagedSpotTraining;
}
/**
* @param checkpointConfig
*/
public void setCheckpointConfig(CheckpointConfig checkpointConfig) {
this.checkpointConfig = checkpointConfig;
}
/**
* @return
*/
public CheckpointConfig getCheckpointConfig() {
return this.checkpointConfig;
}
/**
* @param checkpointConfig
* @return Returns a reference to this object so that method calls can be chained together.
*/
public HyperParameterTrainingJobDefinition withCheckpointConfig(CheckpointConfig checkpointConfig) {
setCheckpointConfig(checkpointConfig);
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 (getDefinitionName() != null)
sb.append("DefinitionName: ").append(getDefinitionName()).append(",");
if (getTuningObjective() != null)
sb.append("TuningObjective: ").append(getTuningObjective()).append(",");
if (getHyperParameterRanges() != null)
sb.append("HyperParameterRanges: ").append(getHyperParameterRanges()).append(",");
if (getStaticHyperParameters() != null)
sb.append("StaticHyperParameters: ").append(getStaticHyperParameters()).append(",");
if (getAlgorithmSpecification() != null)
sb.append("AlgorithmSpecification: ").append(getAlgorithmSpecification()).append(",");
if (getRoleArn() != null)
sb.append("RoleArn: ").append(getRoleArn()).append(",");
if (getInputDataConfig() != null)
sb.append("InputDataConfig: ").append(getInputDataConfig()).append(",");
if (getVpcConfig() != null)
sb.append("VpcConfig: ").append(getVpcConfig()).append(",");
if (getOutputDataConfig() != null)
sb.append("OutputDataConfig: ").append(getOutputDataConfig()).append(",");
if (getResourceConfig() != null)
sb.append("ResourceConfig: ").append(getResourceConfig()).append(",");
if (getStoppingCondition() != null)
sb.append("StoppingCondition: ").append(getStoppingCondition()).append(",");
if (getEnableNetworkIsolation() != null)
sb.append("EnableNetworkIsolation: ").append(getEnableNetworkIsolation()).append(",");
if (getEnableInterContainerTrafficEncryption() != null)
sb.append("EnableInterContainerTrafficEncryption: ").append(getEnableInterContainerTrafficEncryption()).append(",");
if (getEnableManagedSpotTraining() != null)
sb.append("EnableManagedSpotTraining: ").append(getEnableManagedSpotTraining()).append(",");
if (getCheckpointConfig() != null)
sb.append("CheckpointConfig: ").append(getCheckpointConfig());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof HyperParameterTrainingJobDefinition == false)
return false;
HyperParameterTrainingJobDefinition other = (HyperParameterTrainingJobDefinition) obj;
if (other.getDefinitionName() == null ^ this.getDefinitionName() == null)
return false;
if (other.getDefinitionName() != null && other.getDefinitionName().equals(this.getDefinitionName()) == false)
return false;
if (other.getTuningObjective() == null ^ this.getTuningObjective() == null)
return false;
if (other.getTuningObjective() != null && other.getTuningObjective().equals(this.getTuningObjective()) == false)
return false;
if (other.getHyperParameterRanges() == null ^ this.getHyperParameterRanges() == null)
return false;
if (other.getHyperParameterRanges() != null && other.getHyperParameterRanges().equals(this.getHyperParameterRanges()) == false)
return false;
if (other.getStaticHyperParameters() == null ^ this.getStaticHyperParameters() == null)
return false;
if (other.getStaticHyperParameters() != null && other.getStaticHyperParameters().equals(this.getStaticHyperParameters()) == false)
return false;
if (other.getAlgorithmSpecification() == null ^ this.getAlgorithmSpecification() == null)
return false;
if (other.getAlgorithmSpecification() != null && other.getAlgorithmSpecification().equals(this.getAlgorithmSpecification()) == false)
return false;
if (other.getRoleArn() == null ^ this.getRoleArn() == null)
return false;
if (other.getRoleArn() != null && other.getRoleArn().equals(this.getRoleArn()) == false)
return false;
if (other.getInputDataConfig() == null ^ this.getInputDataConfig() == null)
return false;
if (other.getInputDataConfig() != null && other.getInputDataConfig().equals(this.getInputDataConfig()) == false)
return false;
if (other.getVpcConfig() == null ^ this.getVpcConfig() == null)
return false;
if (other.getVpcConfig() != null && other.getVpcConfig().equals(this.getVpcConfig()) == false)
return false;
if (other.getOutputDataConfig() == null ^ this.getOutputDataConfig() == null)
return false;
if (other.getOutputDataConfig() != null && other.getOutputDataConfig().equals(this.getOutputDataConfig()) == false)
return false;
if (other.getResourceConfig() == null ^ this.getResourceConfig() == null)
return false;
if (other.getResourceConfig() != null && other.getResourceConfig().equals(this.getResourceConfig()) == false)
return false;
if (other.getStoppingCondition() == null ^ this.getStoppingCondition() == null)
return false;
if (other.getStoppingCondition() != null && other.getStoppingCondition().equals(this.getStoppingCondition()) == false)
return false;
if (other.getEnableNetworkIsolation() == null ^ this.getEnableNetworkIsolation() == null)
return false;
if (other.getEnableNetworkIsolation() != null && other.getEnableNetworkIsolation().equals(this.getEnableNetworkIsolation()) == false)
return false;
if (other.getEnableInterContainerTrafficEncryption() == null ^ this.getEnableInterContainerTrafficEncryption() == null)
return false;
if (other.getEnableInterContainerTrafficEncryption() != null
&& other.getEnableInterContainerTrafficEncryption().equals(this.getEnableInterContainerTrafficEncryption()) == false)
return false;
if (other.getEnableManagedSpotTraining() == null ^ this.getEnableManagedSpotTraining() == null)
return false;
if (other.getEnableManagedSpotTraining() != null && other.getEnableManagedSpotTraining().equals(this.getEnableManagedSpotTraining()) == false)
return false;
if (other.getCheckpointConfig() == null ^ this.getCheckpointConfig() == null)
return false;
if (other.getCheckpointConfig() != null && other.getCheckpointConfig().equals(this.getCheckpointConfig()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getDefinitionName() == null) ? 0 : getDefinitionName().hashCode());
hashCode = prime * hashCode + ((getTuningObjective() == null) ? 0 : getTuningObjective().hashCode());
hashCode = prime * hashCode + ((getHyperParameterRanges() == null) ? 0 : getHyperParameterRanges().hashCode());
hashCode = prime * hashCode + ((getStaticHyperParameters() == null) ? 0 : getStaticHyperParameters().hashCode());
hashCode = prime * hashCode + ((getAlgorithmSpecification() == null) ? 0 : getAlgorithmSpecification().hashCode());
hashCode = prime * hashCode + ((getRoleArn() == null) ? 0 : getRoleArn().hashCode());
hashCode = prime * hashCode + ((getInputDataConfig() == null) ? 0 : getInputDataConfig().hashCode());
hashCode = prime * hashCode + ((getVpcConfig() == null) ? 0 : getVpcConfig().hashCode());
hashCode = prime * hashCode + ((getOutputDataConfig() == null) ? 0 : getOutputDataConfig().hashCode());
hashCode = prime * hashCode + ((getResourceConfig() == null) ? 0 : getResourceConfig().hashCode());
hashCode = prime * hashCode + ((getStoppingCondition() == null) ? 0 : getStoppingCondition().hashCode());
hashCode = prime * hashCode + ((getEnableNetworkIsolation() == null) ? 0 : getEnableNetworkIsolation().hashCode());
hashCode = prime * hashCode + ((getEnableInterContainerTrafficEncryption() == null) ? 0 : getEnableInterContainerTrafficEncryption().hashCode());
hashCode = prime * hashCode + ((getEnableManagedSpotTraining() == null) ? 0 : getEnableManagedSpotTraining().hashCode());
hashCode = prime * hashCode + ((getCheckpointConfig() == null) ? 0 : getCheckpointConfig().hashCode());
return hashCode;
}
@Override
public HyperParameterTrainingJobDefinition clone() {
try {
return (HyperParameterTrainingJobDefinition) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.sagemaker.model.transform.HyperParameterTrainingJobDefinitionMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}