com.amazonaws.services.sagemaker.model.AlgorithmSpecification 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;
/**
*
* Specifies the training algorithm to use in a CreateTrainingJob request.
*
*
* For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about using your
* own algorithms, see Using Your Own
* Algorithms with Amazon SageMaker.
*
*
* @see AWS
* API Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class AlgorithmSpecification implements Serializable, Cloneable, StructuredPojo {
/**
*
* The registry path of the Docker image that contains the training algorithm. For information about docker registry
* paths for built-in algorithms, see Algorithms
* Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both
* registry/repository[:tag] and registry/repository[@digest] image path formats. For more
* information, see Using Your Own
* Algorithms with Amazon SageMaker.
*
*/
private String trainingImage;
/**
*
* The name of the algorithm resource to use for the training job. This must be an algorithm resource that you
* created or subscribe to on AWS Marketplace. If you specify a value for this parameter, you can't specify a value
* for TrainingImage.
*
*/
private String algorithmName;
/**
*
* The input mode that the algorithm supports. 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.
*
*
* In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download
* from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm
* container use ML storage volume to also store intermediate information, if any.
*
*
* For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is
* predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any
* further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is
* also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
*
*/
private String trainingInputMode;
/**
*
* A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse
* algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
*
*/
private java.util.List metricDefinitions;
/**
*
* To generate and save time-series metrics during training, set to true. The default is
* false and time-series metrics aren't generated except in the following cases:
*
*
* -
*
* You use one of the Amazon SageMaker built-in algorithms
*
*
* -
*
* You use one of the following Prebuilt Amazon SageMaker Docker Images:
*
*
* -
*
* Tensorflow (version >= 1.15)
*
*
* -
*
* MXNet (version >= 1.6)
*
*
* -
*
* PyTorch (version >= 1.3)
*
*
*
*
* -
*
* You specify at least one MetricDefinition
*
*
*
*/
private Boolean enableSageMakerMetricsTimeSeries;
/**
*
* The registry path of the Docker image that contains the training algorithm. For information about docker registry
* paths for built-in algorithms, see Algorithms
* Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both
* registry/repository[:tag] and registry/repository[@digest] image path formats. For more
* information, see Using Your Own
* Algorithms with Amazon SageMaker.
*
*
* @param trainingImage
* The registry path of the Docker image that contains the training algorithm. For information about docker
* registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both
* registry/repository[:tag] and registry/repository[@digest] image path formats.
* For more information, see Using Your Own Algorithms with
* Amazon SageMaker.
*/
public void setTrainingImage(String trainingImage) {
this.trainingImage = trainingImage;
}
/**
*
* The registry path of the Docker image that contains the training algorithm. For information about docker registry
* paths for built-in algorithms, see Algorithms
* Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both
* registry/repository[:tag] and registry/repository[@digest] image path formats. For more
* information, see Using Your Own
* Algorithms with Amazon SageMaker.
*
*
* @return The registry path of the Docker image that contains the training algorithm. For information about docker
* registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both
* registry/repository[:tag] and registry/repository[@digest] image path formats.
* For more information, see Using Your Own Algorithms
* with Amazon SageMaker.
*/
public String getTrainingImage() {
return this.trainingImage;
}
/**
*
* The registry path of the Docker image that contains the training algorithm. For information about docker registry
* paths for built-in algorithms, see Algorithms
* Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both
* registry/repository[:tag] and registry/repository[@digest] image path formats. For more
* information, see Using Your Own
* Algorithms with Amazon SageMaker.
*
*
* @param trainingImage
* The registry path of the Docker image that contains the training algorithm. For information about docker
* registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. Amazon SageMaker supports both
* registry/repository[:tag] and registry/repository[@digest] image path formats.
* For more information, see Using Your Own Algorithms with
* Amazon SageMaker.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public AlgorithmSpecification withTrainingImage(String trainingImage) {
setTrainingImage(trainingImage);
return this;
}
/**
*
* The name of the algorithm resource to use for the training job. This must be an algorithm resource that you
* created or subscribe to on AWS Marketplace. If you specify a value for this parameter, you can't specify a value
* for TrainingImage.
*
*
* @param algorithmName
* The name of the algorithm resource to use for the training job. This must be an algorithm resource that
* you created or subscribe to on AWS Marketplace. If you specify a value for this parameter, you can't
* specify a value for TrainingImage.
*/
public void setAlgorithmName(String algorithmName) {
this.algorithmName = algorithmName;
}
/**
*
* The name of the algorithm resource to use for the training job. This must be an algorithm resource that you
* created or subscribe to on AWS Marketplace. If you specify a value for this parameter, you can't specify a value
* for TrainingImage.
*
*
* @return The name of the algorithm resource to use for the training job. This must be an algorithm resource that
* you created or subscribe to on AWS Marketplace. If you specify a value for this parameter, you can't
* specify a value for TrainingImage.
*/
public String getAlgorithmName() {
return this.algorithmName;
}
/**
*
* The name of the algorithm resource to use for the training job. This must be an algorithm resource that you
* created or subscribe to on AWS Marketplace. If you specify a value for this parameter, you can't specify a value
* for TrainingImage.
*
*
* @param algorithmName
* The name of the algorithm resource to use for the training job. This must be an algorithm resource that
* you created or subscribe to on AWS Marketplace. If you specify a value for this parameter, you can't
* specify a value for TrainingImage.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public AlgorithmSpecification withAlgorithmName(String algorithmName) {
setAlgorithmName(algorithmName);
return this;
}
/**
*
* The input mode that the algorithm supports. 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.
*
*
* In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download
* from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm
* container use ML storage volume to also store intermediate information, if any.
*
*
* For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is
* predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any
* further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is
* also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
*
*
* @param trainingInputMode
* The input mode that the algorithm supports. 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.
*
* In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data
* download from S3. In addition to the training data, the ML storage volume also stores the output model.
* The algorithm container use ML storage volume to also store intermediate information, if any.
*
*
* For distributed algorithms using File mode, training data is distributed uniformly, and your training
* duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not
* split the files any further for model training. If the object sizes are skewed, training won't be optimal
* as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming
* bottleneck in training.
* @see TrainingInputMode
*/
public void setTrainingInputMode(String trainingInputMode) {
this.trainingInputMode = trainingInputMode;
}
/**
*
* The input mode that the algorithm supports. 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.
*
*
* In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download
* from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm
* container use ML storage volume to also store intermediate information, if any.
*
*
* For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is
* predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any
* further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is
* also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
*
*
* @return The input mode that the algorithm supports. 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.
*
* In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data
* download from S3. In addition to the training data, the ML storage volume also stores the output model.
* The algorithm container use ML storage volume to also store intermediate information, if any.
*
*
* For distributed algorithms using File mode, training data is distributed uniformly, and your training
* duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not
* split the files any further for model training. If the object sizes are skewed, training won't be optimal
* as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming
* bottleneck in training.
* @see TrainingInputMode
*/
public String getTrainingInputMode() {
return this.trainingInputMode;
}
/**
*
* The input mode that the algorithm supports. 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.
*
*
* In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download
* from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm
* container use ML storage volume to also store intermediate information, if any.
*
*
* For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is
* predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any
* further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is
* also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
*
*
* @param trainingInputMode
* The input mode that the algorithm supports. 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.
*
* In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data
* download from S3. In addition to the training data, the ML storage volume also stores the output model.
* The algorithm container use ML storage volume to also store intermediate information, if any.
*
*
* For distributed algorithms using File mode, training data is distributed uniformly, and your training
* duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not
* split the files any further for model training. If the object sizes are skewed, training won't be optimal
* as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming
* bottleneck in training.
* @return Returns a reference to this object so that method calls can be chained together.
* @see TrainingInputMode
*/
public AlgorithmSpecification withTrainingInputMode(String trainingInputMode) {
setTrainingInputMode(trainingInputMode);
return this;
}
/**
*
* The input mode that the algorithm supports. 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.
*
*
* In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download
* from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm
* container use ML storage volume to also store intermediate information, if any.
*
*
* For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is
* predictable if the input data objects size is approximately same. Amazon SageMaker does not split the files any
* further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is
* also skewed where one host in a training cluster is overloaded, thus becoming bottleneck in training.
*
*
* @param trainingInputMode
* The input mode that the algorithm supports. 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.
*
* In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data
* download from S3. In addition to the training data, the ML storage volume also stores the output model.
* The algorithm container use ML storage volume to also store intermediate information, if any.
*
*
* For distributed algorithms using File mode, training data is distributed uniformly, and your training
* duration is predictable if the input data objects size is approximately same. Amazon SageMaker does not
* split the files any further for model training. If the object sizes are skewed, training won't be optimal
* as the data distribution is also skewed where one host in a training cluster is overloaded, thus becoming
* bottleneck in training.
* @return Returns a reference to this object so that method calls can be chained together.
* @see TrainingInputMode
*/
public AlgorithmSpecification withTrainingInputMode(TrainingInputMode trainingInputMode) {
this.trainingInputMode = trainingInputMode.toString();
return this;
}
/**
*
* A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse
* algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
*
*
* @return A list of metric definition objects. Each object specifies the metric name and regular expressions used
* to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
*/
public java.util.List getMetricDefinitions() {
return metricDefinitions;
}
/**
*
* A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse
* algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
*
*
* @param metricDefinitions
* A list of metric definition objects. Each object specifies the metric name and regular expressions used to
* parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
*/
public void setMetricDefinitions(java.util.Collection metricDefinitions) {
if (metricDefinitions == null) {
this.metricDefinitions = null;
return;
}
this.metricDefinitions = new java.util.ArrayList(metricDefinitions);
}
/**
*
* A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse
* algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
*
*
* NOTE: This method appends the values to the existing list (if any). Use
* {@link #setMetricDefinitions(java.util.Collection)} or {@link #withMetricDefinitions(java.util.Collection)} if
* you want to override the existing values.
*
*
* @param metricDefinitions
* A list of metric definition objects. Each object specifies the metric name and regular expressions used to
* parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public AlgorithmSpecification withMetricDefinitions(MetricDefinition... metricDefinitions) {
if (this.metricDefinitions == null) {
setMetricDefinitions(new java.util.ArrayList(metricDefinitions.length));
}
for (MetricDefinition ele : metricDefinitions) {
this.metricDefinitions.add(ele);
}
return this;
}
/**
*
* A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse
* algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
*
*
* @param metricDefinitions
* A list of metric definition objects. Each object specifies the metric name and regular expressions used to
* parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public AlgorithmSpecification withMetricDefinitions(java.util.Collection metricDefinitions) {
setMetricDefinitions(metricDefinitions);
return this;
}
/**
*
* To generate and save time-series metrics during training, set to true. The default is
* false and time-series metrics aren't generated except in the following cases:
*
*
* -
*
* You use one of the Amazon SageMaker built-in algorithms
*
*
* -
*
* You use one of the following Prebuilt Amazon SageMaker Docker Images:
*
*
* -
*
* Tensorflow (version >= 1.15)
*
*
* -
*
* MXNet (version >= 1.6)
*
*
* -
*
* PyTorch (version >= 1.3)
*
*
*
*
* -
*
* You specify at least one MetricDefinition
*
*
*
*
* @param enableSageMakerMetricsTimeSeries
* To generate and save time-series metrics during training, set to true. The default is
* false and time-series metrics aren't generated except in the following cases:
*
* -
*
* You use one of the Amazon SageMaker built-in algorithms
*
*
* -
*
* You use one of the following Prebuilt Amazon SageMaker Docker Images:
*
*
* -
*
* Tensorflow (version >= 1.15)
*
*
* -
*
* MXNet (version >= 1.6)
*
*
* -
*
* PyTorch (version >= 1.3)
*
*
*
*
* -
*
* You specify at least one MetricDefinition
*
*
*/
public void setEnableSageMakerMetricsTimeSeries(Boolean enableSageMakerMetricsTimeSeries) {
this.enableSageMakerMetricsTimeSeries = enableSageMakerMetricsTimeSeries;
}
/**
*
* To generate and save time-series metrics during training, set to true. The default is
* false and time-series metrics aren't generated except in the following cases:
*
*
* -
*
* You use one of the Amazon SageMaker built-in algorithms
*
*
* -
*
* You use one of the following Prebuilt Amazon SageMaker Docker Images:
*
*
* -
*
* Tensorflow (version >= 1.15)
*
*
* -
*
* MXNet (version >= 1.6)
*
*
* -
*
* PyTorch (version >= 1.3)
*
*
*
*
* -
*
* You specify at least one MetricDefinition
*
*
*
*
* @return To generate and save time-series metrics during training, set to true. The default is
* false and time-series metrics aren't generated except in the following cases:
*
* -
*
* You use one of the Amazon SageMaker built-in algorithms
*
*
* -
*
* You use one of the following Prebuilt Amazon SageMaker Docker Images:
*
*
* -
*
* Tensorflow (version >= 1.15)
*
*
* -
*
* MXNet (version >= 1.6)
*
*
* -
*
* PyTorch (version >= 1.3)
*
*
*
*
* -
*
* You specify at least one MetricDefinition
*
*
*/
public Boolean getEnableSageMakerMetricsTimeSeries() {
return this.enableSageMakerMetricsTimeSeries;
}
/**
*
* To generate and save time-series metrics during training, set to true. The default is
* false and time-series metrics aren't generated except in the following cases:
*
*
* -
*
* You use one of the Amazon SageMaker built-in algorithms
*
*
* -
*
* You use one of the following Prebuilt Amazon SageMaker Docker Images:
*
*
* -
*
* Tensorflow (version >= 1.15)
*
*
* -
*
* MXNet (version >= 1.6)
*
*
* -
*
* PyTorch (version >= 1.3)
*
*
*
*
* -
*
* You specify at least one MetricDefinition
*
*
*
*
* @param enableSageMakerMetricsTimeSeries
* To generate and save time-series metrics during training, set to true. The default is
* false and time-series metrics aren't generated except in the following cases:
*
* -
*
* You use one of the Amazon SageMaker built-in algorithms
*
*
* -
*
* You use one of the following Prebuilt Amazon SageMaker Docker Images:
*
*
* -
*
* Tensorflow (version >= 1.15)
*
*
* -
*
* MXNet (version >= 1.6)
*
*
* -
*
* PyTorch (version >= 1.3)
*
*
*
*
* -
*
* You specify at least one MetricDefinition
*
*
* @return Returns a reference to this object so that method calls can be chained together.
*/
public AlgorithmSpecification withEnableSageMakerMetricsTimeSeries(Boolean enableSageMakerMetricsTimeSeries) {
setEnableSageMakerMetricsTimeSeries(enableSageMakerMetricsTimeSeries);
return this;
}
/**
*
* To generate and save time-series metrics during training, set to true. The default is
* false and time-series metrics aren't generated except in the following cases:
*
*
* -
*
* You use one of the Amazon SageMaker built-in algorithms
*
*
* -
*
* You use one of the following Prebuilt Amazon SageMaker Docker Images:
*
*
* -
*
* Tensorflow (version >= 1.15)
*
*
* -
*
* MXNet (version >= 1.6)
*
*
* -
*
* PyTorch (version >= 1.3)
*
*
*
*
* -
*
* You specify at least one MetricDefinition
*
*
*
*
* @return To generate and save time-series metrics during training, set to true. The default is
* false and time-series metrics aren't generated except in the following cases:
*
* -
*
* You use one of the Amazon SageMaker built-in algorithms
*
*
* -
*
* You use one of the following Prebuilt Amazon SageMaker Docker Images:
*
*
* -
*
* Tensorflow (version >= 1.15)
*
*
* -
*
* MXNet (version >= 1.6)
*
*
* -
*
* PyTorch (version >= 1.3)
*
*
*
*
* -
*
* You specify at least one MetricDefinition
*
*
*/
public Boolean isEnableSageMakerMetricsTimeSeries() {
return this.enableSageMakerMetricsTimeSeries;
}
/**
* 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 (getTrainingImage() != null)
sb.append("TrainingImage: ").append(getTrainingImage()).append(",");
if (getAlgorithmName() != null)
sb.append("AlgorithmName: ").append(getAlgorithmName()).append(",");
if (getTrainingInputMode() != null)
sb.append("TrainingInputMode: ").append(getTrainingInputMode()).append(",");
if (getMetricDefinitions() != null)
sb.append("MetricDefinitions: ").append(getMetricDefinitions()).append(",");
if (getEnableSageMakerMetricsTimeSeries() != null)
sb.append("EnableSageMakerMetricsTimeSeries: ").append(getEnableSageMakerMetricsTimeSeries());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof AlgorithmSpecification == false)
return false;
AlgorithmSpecification other = (AlgorithmSpecification) obj;
if (other.getTrainingImage() == null ^ this.getTrainingImage() == null)
return false;
if (other.getTrainingImage() != null && other.getTrainingImage().equals(this.getTrainingImage()) == false)
return false;
if (other.getAlgorithmName() == null ^ this.getAlgorithmName() == null)
return false;
if (other.getAlgorithmName() != null && other.getAlgorithmName().equals(this.getAlgorithmName()) == false)
return false;
if (other.getTrainingInputMode() == null ^ this.getTrainingInputMode() == null)
return false;
if (other.getTrainingInputMode() != null && other.getTrainingInputMode().equals(this.getTrainingInputMode()) == false)
return false;
if (other.getMetricDefinitions() == null ^ this.getMetricDefinitions() == null)
return false;
if (other.getMetricDefinitions() != null && other.getMetricDefinitions().equals(this.getMetricDefinitions()) == false)
return false;
if (other.getEnableSageMakerMetricsTimeSeries() == null ^ this.getEnableSageMakerMetricsTimeSeries() == null)
return false;
if (other.getEnableSageMakerMetricsTimeSeries() != null
&& other.getEnableSageMakerMetricsTimeSeries().equals(this.getEnableSageMakerMetricsTimeSeries()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getTrainingImage() == null) ? 0 : getTrainingImage().hashCode());
hashCode = prime * hashCode + ((getAlgorithmName() == null) ? 0 : getAlgorithmName().hashCode());
hashCode = prime * hashCode + ((getTrainingInputMode() == null) ? 0 : getTrainingInputMode().hashCode());
hashCode = prime * hashCode + ((getMetricDefinitions() == null) ? 0 : getMetricDefinitions().hashCode());
hashCode = prime * hashCode + ((getEnableSageMakerMetricsTimeSeries() == null) ? 0 : getEnableSageMakerMetricsTimeSeries().hashCode());
return hashCode;
}
@Override
public AlgorithmSpecification clone() {
try {
return (AlgorithmSpecification) 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.AlgorithmSpecificationMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}