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Java example source code file (TrainingMaster.java)

This example Java source code file (TrainingMaster.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

javardd, sparktrainingstats, trainingmaster, trainingresult, trainingworker, util

The TrainingMaster.java Java example source code

package org.deeplearning4j.spark.api;

import org.apache.spark.api.java.JavaRDD;
import org.deeplearning4j.optimize.api.IterationListener;
import org.deeplearning4j.spark.api.stats.SparkTrainingStats;
import org.deeplearning4j.spark.impl.graph.SparkComputationGraph;
import org.deeplearning4j.spark.impl.multilayer.SparkDl4jMultiLayer;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.MultiDataSet;

import java.util.Collection;

/**
 * A TrainingMaster controls how distributed training is executed in practice<br>
 * In principle, a large number of different approches can be used in distributed training (synchronous vs. asynchronous,
 * parameter vs. gradient averaging, etc). Each of these different approaches would be implemented as a TrainingMaster;
 * this allows {@link SparkDl4jMultiLayer} and {@link SparkComputationGraph} to be used with different training methods.
 *
 * @author Alex Black
 */
public interface TrainingMaster<R extends TrainingResult, W extends TrainingWorker {

    /**
     * Get the worker instance for this training master
     *
     * @param network Current SparkDl4jMultiLayer
     * @return Worker instance
     */
    W getWorkerInstance(SparkDl4jMultiLayer network);

    /**
     * Get the worker instance for this training master
     *
     * @param graph Current SparkComputationGraph
     * @return Worker instance
     */
    W getWorkerInstance(SparkComputationGraph graph);

    /**
     * Train the SparkDl4jMultiLayer with the specified data set
     *
     * @param network      Current network state
     * @param trainingData Data to train on
     */
    void executeTraining(SparkDl4jMultiLayer network, JavaRDD<DataSet> trainingData);

    /**
     * Train the SparkComputationGraph with the specified data set
     *
     * @param graph        Current network state
     * @param trainingData Data to train on
     */
    void executeTraining(SparkComputationGraph graph, JavaRDD<DataSet> trainingData);

    /**
     * Train the SparkComputationGraph with the specified data set
     *
     * @param graph        Current network state
     * @param trainingData Data to train on
     */
    void executeTrainingMDS(SparkComputationGraph graph, JavaRDD<MultiDataSet> trainingData);

    /**
     * Set whether the training statistics should be collected. Training statistics may include things like per-epoch run times,
     * time spent waiting for data, etc.
     * <p>
     * These statistics are primarily used for debugging and optimization, in order to gain some insight into what aspects
     * of network training are taking the most time.
     *
     * @param collectTrainingStats If true: collecting training statistics will be
     */
    void setCollectTrainingStats(boolean collectTrainingStats);

    /**
     * Get the current setting for collectTrainingStats
     */
    boolean getIsCollectTrainingStats();

    /**
     * Return the training statistics. Note that this may return null, unless setCollectTrainingStats has been set first
     *
     * @return Training statistics
     */
    SparkTrainingStats getTrainingStats();

    /**
     * Set the iteration listeners. These should be called after every averaging (or similar) operation in the TrainingMaster,
     * though the exact behaviour may be dependent on each IterationListener
     *
     * @param listeners     Listeners to set
     */
    void setListeners(Collection<IterationListener> listeners);
}

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