com.amazonaws.services.sagemaker.model.TrainingJobDefinition 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 input needed to run a training job using the algorithm.
*
*
* @see AWS
* API Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class TrainingJobDefinition implements Serializable, Cloneable, StructuredPojo {
/**
*
* The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms
* support, see Algorithms.
*
*
* If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3
* to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an
* algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the
* container.
*
*/
private String trainingInputMode;
/**
*
* The hyperparameters used for the training job.
*
*/
private java.util.Map hyperParameters;
/**
*
* An array of Channel objects, each of which specifies an input source.
*
*/
private java.util.List inputDataConfig;
/**
*
* the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the
* artifacts.
*
*/
private OutputDataConfig outputDataConfig;
/**
*
* The resources, including the ML compute instances and ML storage volumes, to use for model training.
*
*/
private ResourceConfig resourceConfig;
/**
*
* Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker
* ends the training job. Use this API to cap model training costs.
*
*
* To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120
* seconds. Algorithms can use this 120-second window to save the model artifacts.
*
*/
private StoppingCondition stoppingCondition;
/**
*
* The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms
* support, see Algorithms.
*
*
* If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3
* to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an
* algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the
* container.
*
*
* @param trainingInputMode
* The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker
* algorithms support, see Algorithms.
*
* If an algorithm supports the File input mode, Amazon SageMaker downloads the training data
* from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training
* container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data
* directly from S3 to the container.
* @see TrainingInputMode
*/
public void setTrainingInputMode(String trainingInputMode) {
this.trainingInputMode = trainingInputMode;
}
/**
*
* The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms
* support, see Algorithms.
*
*
* If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3
* to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an
* algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the
* container.
*
*
* @return The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker
* algorithms support, see Algorithms.
*
* If an algorithm supports the File input mode, Amazon SageMaker downloads the training data
* from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training
* container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data
* directly from S3 to the container.
* @see TrainingInputMode
*/
public String getTrainingInputMode() {
return this.trainingInputMode;
}
/**
*
* The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms
* support, see Algorithms.
*
*
* If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3
* to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an
* algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the
* container.
*
*
* @param trainingInputMode
* The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker
* algorithms support, see Algorithms.
*
* If an algorithm supports the File input mode, Amazon SageMaker downloads the training data
* from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training
* container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data
* directly from S3 to the container.
* @return Returns a reference to this object so that method calls can be chained together.
* @see TrainingInputMode
*/
public TrainingJobDefinition withTrainingInputMode(String trainingInputMode) {
setTrainingInputMode(trainingInputMode);
return this;
}
/**
*
* The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker algorithms
* support, see Algorithms.
*
*
* If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3
* to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an
* algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the
* container.
*
*
* @param trainingInputMode
* The input mode used by the algorithm for the training job. For the input modes that Amazon SageMaker
* algorithms support, see Algorithms.
*
* If an algorithm supports the File input mode, Amazon SageMaker downloads the training data
* from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training
* container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data
* directly from S3 to the container.
* @return Returns a reference to this object so that method calls can be chained together.
* @see TrainingInputMode
*/
public TrainingJobDefinition withTrainingInputMode(TrainingInputMode trainingInputMode) {
this.trainingInputMode = trainingInputMode.toString();
return this;
}
/**
*
* The hyperparameters used for the training job.
*
*
* @return The hyperparameters used for the training job.
*/
public java.util.Map getHyperParameters() {
return hyperParameters;
}
/**
*
* The hyperparameters used for the training job.
*
*
* @param hyperParameters
* The hyperparameters used for the training job.
*/
public void setHyperParameters(java.util.Map hyperParameters) {
this.hyperParameters = hyperParameters;
}
/**
*
* The hyperparameters used for the training job.
*
*
* @param hyperParameters
* The hyperparameters used for the training job.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TrainingJobDefinition withHyperParameters(java.util.Map hyperParameters) {
setHyperParameters(hyperParameters);
return this;
}
/**
* Add a single HyperParameters entry
*
* @see TrainingJobDefinition#withHyperParameters
* @returns a reference to this object so that method calls can be chained together.
*/
public TrainingJobDefinition addHyperParametersEntry(String key, String value) {
if (null == this.hyperParameters) {
this.hyperParameters = new java.util.HashMap();
}
if (this.hyperParameters.containsKey(key))
throw new IllegalArgumentException("Duplicated keys (" + key.toString() + ") are provided.");
this.hyperParameters.put(key, value);
return this;
}
/**
* Removes all the entries added into HyperParameters.
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TrainingJobDefinition clearHyperParametersEntries() {
this.hyperParameters = null;
return this;
}
/**
*
* An array of Channel objects, each of which specifies an input source.
*
*
* @return An array of Channel objects, each of which specifies an input source.
*/
public java.util.List getInputDataConfig() {
return inputDataConfig;
}
/**
*
* An array of Channel objects, each of which specifies an input source.
*
*
* @param inputDataConfig
* An array of Channel objects, each of which specifies an input source.
*/
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, each of which specifies an input source.
*
*
* 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, each of which specifies an input source.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TrainingJobDefinition 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, each of which specifies an input source.
*
*
* @param inputDataConfig
* An array of Channel objects, each of which specifies an input source.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TrainingJobDefinition withInputDataConfig(java.util.Collection inputDataConfig) {
setInputDataConfig(inputDataConfig);
return this;
}
/**
*
* the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the
* artifacts.
*
*
* @param outputDataConfig
* the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for
* the artifacts.
*/
public void setOutputDataConfig(OutputDataConfig outputDataConfig) {
this.outputDataConfig = outputDataConfig;
}
/**
*
* the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the
* artifacts.
*
*
* @return the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders
* for the artifacts.
*/
public OutputDataConfig getOutputDataConfig() {
return this.outputDataConfig;
}
/**
*
* the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the
* artifacts.
*
*
* @param outputDataConfig
* the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for
* the artifacts.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TrainingJobDefinition withOutputDataConfig(OutputDataConfig outputDataConfig) {
setOutputDataConfig(outputDataConfig);
return this;
}
/**
*
* The resources, including the ML compute instances and ML storage volumes, to use for model training.
*
*
* @param resourceConfig
* The resources, including the ML compute instances and ML storage volumes, to use for model training.
*/
public void setResourceConfig(ResourceConfig resourceConfig) {
this.resourceConfig = resourceConfig;
}
/**
*
* The resources, including the ML compute instances and ML storage volumes, to use for model training.
*
*
* @return The resources, including the ML compute instances and ML storage volumes, to use for model training.
*/
public ResourceConfig getResourceConfig() {
return this.resourceConfig;
}
/**
*
* The resources, including the ML compute instances and ML storage volumes, to use for model training.
*
*
* @param resourceConfig
* The resources, including the ML compute instances and ML storage volumes, to use for model training.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TrainingJobDefinition withResourceConfig(ResourceConfig resourceConfig) {
setResourceConfig(resourceConfig);
return this;
}
/**
*
* Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker
* ends the training job. Use this API to cap model training costs.
*
*
* To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120
* seconds. Algorithms can use this 120-second window to save the model artifacts.
*
*
* @param stoppingCondition
* Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon
* SageMaker ends the training job. Use this API to cap model training costs.
*
* To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for
* 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
*/
public void setStoppingCondition(StoppingCondition stoppingCondition) {
this.stoppingCondition = stoppingCondition;
}
/**
*
* Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker
* ends the training job. Use this API to cap model training costs.
*
*
* To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120
* seconds. Algorithms can use this 120-second window to save the model artifacts.
*
*
* @return Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon
* SageMaker ends the training job. Use this API to cap model training costs.
*
* To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for
* 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
*/
public StoppingCondition getStoppingCondition() {
return this.stoppingCondition;
}
/**
*
* Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker
* ends the training job. Use this API to cap model training costs.
*
*
* To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120
* seconds. Algorithms can use this 120-second window to save the model artifacts.
*
*
* @param stoppingCondition
* Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon
* SageMaker ends the training job. Use this API to cap model training costs.
*
* To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for
* 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TrainingJobDefinition withStoppingCondition(StoppingCondition stoppingCondition) {
setStoppingCondition(stoppingCondition);
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 (getTrainingInputMode() != null)
sb.append("TrainingInputMode: ").append(getTrainingInputMode()).append(",");
if (getHyperParameters() != null)
sb.append("HyperParameters: ").append(getHyperParameters()).append(",");
if (getInputDataConfig() != null)
sb.append("InputDataConfig: ").append(getInputDataConfig()).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());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof TrainingJobDefinition == false)
return false;
TrainingJobDefinition other = (TrainingJobDefinition) obj;
if (other.getTrainingInputMode() == null ^ this.getTrainingInputMode() == null)
return false;
if (other.getTrainingInputMode() != null && other.getTrainingInputMode().equals(this.getTrainingInputMode()) == false)
return false;
if (other.getHyperParameters() == null ^ this.getHyperParameters() == null)
return false;
if (other.getHyperParameters() != null && other.getHyperParameters().equals(this.getHyperParameters()) == 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.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;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getTrainingInputMode() == null) ? 0 : getTrainingInputMode().hashCode());
hashCode = prime * hashCode + ((getHyperParameters() == null) ? 0 : getHyperParameters().hashCode());
hashCode = prime * hashCode + ((getInputDataConfig() == null) ? 0 : getInputDataConfig().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());
return hashCode;
}
@Override
public TrainingJobDefinition clone() {
try {
return (TrainingJobDefinition) 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.TrainingJobDefinitionMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}