/* * 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.protocol.StructuredPojo; import com.amazonaws.protocol.ProtocolMarshaller; /** *

* Specifies the training algorithm to use in a CreateTrainingJob * request. *

*

* For more information about algorithms provided by 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 SageMaker built-in algorithms, see Docker Registry * Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both * registry/repository[:tag] and registry/repository[@digest] image path formats. For more * information about using your custom training container, see Using Your Own Algorithms with Amazon * SageMaker. *

* *

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the * algorithm container to the TrainingImage parameter. *

*

* For more information, see the note in the AlgorithmName parameter description. *

* */ 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 Amazon Web Services Marketplace. *

* *

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the * algorithm container to the TrainingImage parameter. *

*

* Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage * parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for * TrainingImage, and vice versa. *

*

* If you specify values for both parameters, the training job might break; if you don't specify any value for both * parameters, the training job might raise a null error. *

* */ private String algorithmName; private String trainingInputMode; /** *

* A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse * algorithm logs. 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: *

* */ private Boolean enableSageMakerMetricsTimeSeries; /** *

* The entrypoint script for a Docker container used * to run a training job. This script takes precedence over the default train processing instructions. See How Amazon * SageMaker Runs Your Training Image for more information. *

*/ private java.util.List containerEntrypoint; /** *

* The arguments for a container used to run a training job. See How Amazon * SageMaker Runs Your Training Image for additional information. *

*/ private java.util.List containerArguments; /** *

* The configuration to use an image from a private Docker registry for a training job. *

*/ private TrainingImageConfig trainingImageConfig; /** *

* The registry path of the Docker image that contains the training algorithm. For information about docker registry * paths for SageMaker built-in algorithms, see Docker Registry * Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both * registry/repository[:tag] and registry/repository[@digest] image path formats. For more * information about using your custom training container, see Using Your Own Algorithms with Amazon * SageMaker. *

* *

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the * algorithm container to the TrainingImage parameter. *

*

* For more information, see the note in the AlgorithmName parameter description. *

* * * @param trainingImage * The registry path of the Docker image that contains the training algorithm. For information about docker * registry paths for SageMaker built-in algorithms, see Docker * Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports * both registry/repository[:tag] and registry/repository[@digest] image path * formats. For more information about using your custom training container, see Using Your Own Algorithms with * Amazon SageMaker.

*

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI of * the algorithm container to the TrainingImage parameter. *

*

* For more information, see the note in the AlgorithmName parameter description. *

*/ 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 SageMaker built-in algorithms, see Docker Registry * Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both * registry/repository[:tag] and registry/repository[@digest] image path formats. For more * information about using your custom training container, see Using Your Own Algorithms with Amazon * SageMaker. *

* *

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the * algorithm container to the TrainingImage parameter. *

*

* For more information, see the note in the AlgorithmName parameter description. *

* * * @return The registry path of the Docker image that contains the training algorithm. For information about docker * registry paths for SageMaker built-in algorithms, see Docker * Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports * both registry/repository[:tag] and registry/repository[@digest] image path * formats. For more information about using your custom training container, see Using Your Own Algorithms * with Amazon SageMaker.

*

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI * of the algorithm container to the TrainingImage parameter. *

*

* For more information, see the note in the AlgorithmName parameter description. *

*/ 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 SageMaker built-in algorithms, see Docker Registry * Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both * registry/repository[:tag] and registry/repository[@digest] image path formats. For more * information about using your custom training container, see Using Your Own Algorithms with Amazon * SageMaker. *

* *

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the * algorithm container to the TrainingImage parameter. *

*

* For more information, see the note in the AlgorithmName parameter description. *

* * * @param trainingImage * The registry path of the Docker image that contains the training algorithm. For information about docker * registry paths for SageMaker built-in algorithms, see Docker * Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports * both registry/repository[:tag] and registry/repository[@digest] image path * formats. For more information about using your custom training container, see Using Your Own Algorithms with * Amazon SageMaker.

*

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI of * the algorithm container to the TrainingImage parameter. *

*

* For more information, see the note in the AlgorithmName parameter description. *

* @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 Amazon Web Services Marketplace. *

* *

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the * algorithm container to the TrainingImage parameter. *

*

* Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage * parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for * TrainingImage, and vice versa. *

*

* If you specify values for both parameters, the training job might break; if you don't specify any value for both * parameters, the training job might raise a null error. *

* * * @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 Amazon Web Services Marketplace.

*

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI of * the algorithm container to the TrainingImage parameter. *

*

* Note that the AlgorithmName parameter is mutually exclusive with the * TrainingImage parameter. If you specify a value for the AlgorithmName parameter, * you can't specify a value for TrainingImage, and vice versa. *

*

* If you specify values for both parameters, the training job might break; if you don't specify any value * for both parameters, the training job might raise a null error. *

*/ 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 Amazon Web Services Marketplace. *

* *

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the * algorithm container to the TrainingImage parameter. *

*

* Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage * parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for * TrainingImage, and vice versa. *

*

* If you specify values for both parameters, the training job might break; if you don't specify any value for both * parameters, the training job might raise a null error. *

* * * @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 Amazon Web Services Marketplace.

*

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI * of the algorithm container to the TrainingImage parameter. *

*

* Note that the AlgorithmName parameter is mutually exclusive with the * TrainingImage parameter. If you specify a value for the AlgorithmName * parameter, you can't specify a value for TrainingImage, and vice versa. *

*

* If you specify values for both parameters, the training job might break; if you don't specify any value * for both parameters, the training job might raise a null error. *

*/ 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 Amazon Web Services Marketplace. *

* *

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the * algorithm container to the TrainingImage parameter. *

*

* Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage * parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for * TrainingImage, and vice versa. *

*

* If you specify values for both parameters, the training job might break; if you don't specify any value for both * parameters, the training job might raise a null error. *

* * * @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 Amazon Web Services Marketplace.

*

* You must specify either the algorithm name to the AlgorithmName parameter or the image URI of * the algorithm container to the TrainingImage parameter. *

*

* Note that the AlgorithmName parameter is mutually exclusive with the * TrainingImage parameter. If you specify a value for the AlgorithmName parameter, * you can't specify a value for TrainingImage, and vice versa. *

*

* If you specify values for both parameters, the training job might break; if you don't specify any value * for both parameters, the training job might raise a null error. *

* @return Returns a reference to this object so that method calls can be chained together. */ public AlgorithmSpecification withAlgorithmName(String algorithmName) { setAlgorithmName(algorithmName); return this; } /** * @param trainingInputMode * @see TrainingInputMode */ public void setTrainingInputMode(String trainingInputMode) { this.trainingInputMode = trainingInputMode; } /** * @return * @see TrainingInputMode */ public String getTrainingInputMode() { return this.trainingInputMode; } /** * @param trainingInputMode * @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; } /** * @param trainingInputMode * @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. 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. 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. 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. 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. 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. 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. 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. 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 SageMaker built-in algorithms *

    *
    • *
  • *

    * You use one of the following Prebuilt 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 SageMaker built-in algorithms *

    *
    • *
  • *

    * You use one of the following Prebuilt 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 SageMaker built-in algorithms *

      *
      • *
    • *

      * You use one of the following Prebuilt 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 SageMaker built-in algorithms *

      *
      • *
    • *

      * You use one of the following Prebuilt 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 SageMaker built-in algorithms *

        *
        • *
      • *

        * You use one of the following Prebuilt 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 SageMaker built-in algorithms *

        *
        • *
      • *

        * You use one of the following Prebuilt 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 SageMaker built-in algorithms *

          *
          • *
        • *

          * You use one of the following Prebuilt 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 SageMaker built-in algorithms *

          *
          • *
        • *

          * You use one of the following Prebuilt 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; } /** *

          * The entrypoint script for a Docker container used * to run a training job. This script takes precedence over the default train processing instructions. See How Amazon * SageMaker Runs Your Training Image for more information. *

          * * @return The entrypoint script for a Docker * container used to run a training job. This script takes precedence over the default train processing * instructions. See How * Amazon SageMaker Runs Your Training Image for more information. */ public java.util.List getContainerEntrypoint() { return containerEntrypoint; } /** *

          * The entrypoint script for a Docker container used * to run a training job. This script takes precedence over the default train processing instructions. See How Amazon * SageMaker Runs Your Training Image for more information. *

          * * @param containerEntrypoint * The entrypoint script for a Docker * container used to run a training job. This script takes precedence over the default train processing * instructions. See How * Amazon SageMaker Runs Your Training Image for more information. */ public void setContainerEntrypoint(java.util.Collection containerEntrypoint) { if (containerEntrypoint == null) { this.containerEntrypoint = null; return; } this.containerEntrypoint = new java.util.ArrayList(containerEntrypoint); } /** *

          * The entrypoint script for a Docker container used * to run a training job. This script takes precedence over the default train processing instructions. See How Amazon * SageMaker Runs Your Training Image for more information. *

          *

          * NOTE: This method appends the values to the existing list (if any). Use * {@link #setContainerEntrypoint(java.util.Collection)} or {@link #withContainerEntrypoint(java.util.Collection)} * if you want to override the existing values. *

          * * @param containerEntrypoint * The entrypoint script for a Docker * container used to run a training job. This script takes precedence over the default train processing * instructions. See How * Amazon SageMaker Runs Your Training Image for more information. * @return Returns a reference to this object so that method calls can be chained together. */ public AlgorithmSpecification withContainerEntrypoint(String... containerEntrypoint) { if (this.containerEntrypoint == null) { setContainerEntrypoint(new java.util.ArrayList(containerEntrypoint.length)); } for (String ele : containerEntrypoint) { this.containerEntrypoint.add(ele); } return this; } /** *

          * The entrypoint script for a Docker container used * to run a training job. This script takes precedence over the default train processing instructions. See How Amazon * SageMaker Runs Your Training Image for more information. *

          * * @param containerEntrypoint * The entrypoint script for a Docker * container used to run a training job. This script takes precedence over the default train processing * instructions. See How * Amazon SageMaker Runs Your Training Image for more information. * @return Returns a reference to this object so that method calls can be chained together. */ public AlgorithmSpecification withContainerEntrypoint(java.util.Collection containerEntrypoint) { setContainerEntrypoint(containerEntrypoint); return this; } /** *

          * The arguments for a container used to run a training job. See How Amazon * SageMaker Runs Your Training Image for additional information. *

          * * @return The arguments for a container used to run a training job. See How * Amazon SageMaker Runs Your Training Image for additional information. */ public java.util.List getContainerArguments() { return containerArguments; } /** *

          * The arguments for a container used to run a training job. See How Amazon * SageMaker Runs Your Training Image for additional information. *

          * * @param containerArguments * The arguments for a container used to run a training job. See How * Amazon SageMaker Runs Your Training Image for additional information. */ public void setContainerArguments(java.util.Collection containerArguments) { if (containerArguments == null) { this.containerArguments = null; return; } this.containerArguments = new java.util.ArrayList(containerArguments); } /** *

          * The arguments for a container used to run a training job. See How Amazon * SageMaker Runs Your Training Image for additional information. *

          *

          * NOTE: This method appends the values to the existing list (if any). Use * {@link #setContainerArguments(java.util.Collection)} or {@link #withContainerArguments(java.util.Collection)} if * you want to override the existing values. *

          * * @param containerArguments * The arguments for a container used to run a training job. See How * Amazon SageMaker Runs Your Training Image for additional information. * @return Returns a reference to this object so that method calls can be chained together. */ public AlgorithmSpecification withContainerArguments(String... containerArguments) { if (this.containerArguments == null) { setContainerArguments(new java.util.ArrayList(containerArguments.length)); } for (String ele : containerArguments) { this.containerArguments.add(ele); } return this; } /** *

          * The arguments for a container used to run a training job. See How Amazon * SageMaker Runs Your Training Image for additional information. *

          * * @param containerArguments * The arguments for a container used to run a training job. See How * Amazon SageMaker Runs Your Training Image for additional information. * @return Returns a reference to this object so that method calls can be chained together. */ public AlgorithmSpecification withContainerArguments(java.util.Collection containerArguments) { setContainerArguments(containerArguments); return this; } /** *

          * The configuration to use an image from a private Docker registry for a training job. *

          * * @param trainingImageConfig * The configuration to use an image from a private Docker registry for a training job. */ public void setTrainingImageConfig(TrainingImageConfig trainingImageConfig) { this.trainingImageConfig = trainingImageConfig; } /** *

          * The configuration to use an image from a private Docker registry for a training job. *

          * * @return The configuration to use an image from a private Docker registry for a training job. */ public TrainingImageConfig getTrainingImageConfig() { return this.trainingImageConfig; } /** *

          * The configuration to use an image from a private Docker registry for a training job. *

          * * @param trainingImageConfig * The configuration to use an image from a private Docker registry for a training job. * @return Returns a reference to this object so that method calls can be chained together. */ public AlgorithmSpecification withTrainingImageConfig(TrainingImageConfig trainingImageConfig) { setTrainingImageConfig(trainingImageConfig); 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 (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()).append(","); if (getContainerEntrypoint() != null) sb.append("ContainerEntrypoint: ").append(getContainerEntrypoint()).append(","); if (getContainerArguments() != null) sb.append("ContainerArguments: ").append(getContainerArguments()).append(","); if (getTrainingImageConfig() != null) sb.append("TrainingImageConfig: ").append(getTrainingImageConfig()); 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; if (other.getContainerEntrypoint() == null ^ this.getContainerEntrypoint() == null) return false; if (other.getContainerEntrypoint() != null && other.getContainerEntrypoint().equals(this.getContainerEntrypoint()) == false) return false; if (other.getContainerArguments() == null ^ this.getContainerArguments() == null) return false; if (other.getContainerArguments() != null && other.getContainerArguments().equals(this.getContainerArguments()) == false) return false; if (other.getTrainingImageConfig() == null ^ this.getTrainingImageConfig() == null) return false; if (other.getTrainingImageConfig() != null && other.getTrainingImageConfig().equals(this.getTrainingImageConfig()) == 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()); hashCode = prime * hashCode + ((getContainerEntrypoint() == null) ? 0 : getContainerEntrypoint().hashCode()); hashCode = prime * hashCode + ((getContainerArguments() == null) ? 0 : getContainerArguments().hashCode()); hashCode = prime * hashCode + ((getTrainingImageConfig() == null) ? 0 : getTrainingImageConfig().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); } }