How to use

ParallelWrapper$Builder

in

org.deeplearning4j.parallelism

ParallelWrapper wrapper = new ParallelWrapper.Builder(vgg16)
  .prefetchBuffer(24)
  .workers(Nd4j.getAffinityManager().getNumberOfDevices())
  .trainingMode(ParallelWrapper.TrainingMode.SHARED_GRADIENTS)
  .build();

ParallelWrapper wrapper = new ParallelWrapper.Builder(model)
  .prefetchBuffer(24)
  .workers(4)
  .averagingFrequency(3)
  .reportScoreAfterAveraging(true)
  .build();

ParallelWrapper wrapper = new ParallelWrapper.Builder(model)
        .prefetchBuffer(prefetchSize)
        .workers(workers)
        .averagingFrequency(averagingFrequency).averageUpdaters(averageUpdaters)
        .reportScoreAfterAveraging(reportScore)
        .build();

ParallelWrapper wrapper = new ParallelWrapper.Builder(model)
        .prefetchBuffer(prefetchSize)
        .workers(workers)
        .averagingFrequency(averagingFrequency).averageUpdaters(averageUpdaters)
        .reportScoreAfterAveraging(reportScore)
        .build();

public EarlyStoppingParallelTrainer(EarlyStoppingConfiguration<T> earlyStoppingConfiguration, T model,
        DataSetIterator train, MultiDataSetIterator trainMulti, EarlyStoppingListener<T> listener,
        int workers, int prefetchBuffer, int averagingFrequency, boolean reportScoreAfterAveraging,
        boolean useLegacyAveraging) {
  this.esConfig = earlyStoppingConfiguration;
  this.train = train;
  this.trainMulti = trainMulti;
  this.iterator = (train != null ? train : trainMulti);
  this.listener = listener;
  this.model = model;
  // adjust UI listeners
  AveragingIterationListener trainerListener = new AveragingIterationListener(this);
  if (model instanceof MultiLayerNetwork) {
    Collection<IterationListener> listeners = ((MultiLayerNetwork) model).getListeners();
    Collection<IterationListener> newListeners = new LinkedList<>(listeners);
    newListeners.add(trainerListener);
    model.setListeners(newListeners);
  } else if (model instanceof ComputationGraph) {
    Collection<IterationListener> listeners = ((ComputationGraph) model).getListeners();
    Collection<IterationListener> newListeners = new LinkedList<>(listeners);
    newListeners.add(trainerListener);
    model.setListeners(newListeners);
  }
  this.wrapper = new ParallelWrapper.Builder<>(model).workers(workers).prefetchBuffer(prefetchBuffer)
          .averagingFrequency(averagingFrequency)
          //.useLegacyAveraging(useLegacyAveraging)
          .reportScoreAfterAveraging(reportScoreAfterAveraging).build();
}

public EarlyStoppingParallelTrainer(EarlyStoppingConfiguration<T> earlyStoppingConfiguration, T model,
        DataSetIterator train, MultiDataSetIterator trainMulti, EarlyStoppingListener<T> listener,
        int workers, int prefetchBuffer, int averagingFrequency, boolean reportScoreAfterAveraging,
        boolean useLegacyAveraging) {
  this.esConfig = earlyStoppingConfiguration;
  this.train = train;
  this.trainMulti = trainMulti;
  this.iterator = (train != null ? train : trainMulti);
  this.listener = listener;
  this.model = model;
  // adjust UI listeners
  AveragingIterationListener trainerListener = new AveragingIterationListener(this);
  if (model instanceof MultiLayerNetwork) {
    Collection<IterationListener> listeners = ((MultiLayerNetwork) model).getListeners();
    Collection<IterationListener> newListeners = new LinkedList<>(listeners);
    newListeners.add(trainerListener);
    model.setListeners(newListeners);
  } else if (model instanceof ComputationGraph) {
    Collection<IterationListener> listeners = ((ComputationGraph) model).getListeners();
    Collection<IterationListener> newListeners = new LinkedList<>(listeners);
    newListeners.add(trainerListener);
    model.setListeners(newListeners);
  }
  this.wrapper = new ParallelWrapper.Builder<>(model).workers(workers).prefetchBuffer(prefetchBuffer)
          .averagingFrequency(averagingFrequency)
          //.useLegacyAveraging(useLegacyAveraging)
          .reportScoreAfterAveraging(reportScoreAfterAveraging).build();
}

public ParallelTrainerOnGPU(ComputationGraph graph, int miniBatchSize, int totalExamplesPerIterator) {
  String numWorkersString = System.getProperty("framework.parallelWrapper.numWorkers");
  int numWorkers = numWorkersString != null ? Integer.parseInt(numWorkersString) : 4;
  String prefetchBufferString = System.getProperty("framework.parallelWrapper.prefetchBuffer");
  int prefetchBuffer = prefetchBufferString != null ? Integer.parseInt(prefetchBufferString) : 12 * numWorkers;
  String averagingFrequencyString = System.getProperty("framework.parallelWrapper.averagingFrequency");
  int averagingFrequency = averagingFrequencyString != null ? Integer.parseInt(averagingFrequencyString) : 3;
  wrapper = new ParallelWrapper.Builder<>(graph)
      .prefetchBuffer(prefetchBuffer)
      .workers(numWorkers)
      .averagingFrequency(averagingFrequency)
      .reportScoreAfterAveraging(false)
      // .useLegacyAveraging(true)
      .build();
  wrapper.setListeners(perListener);
  this.numExamplesPerIterator = totalExamplesPerIterator;
  this.miniBatchSize = miniBatchSize;
}

ParallelWrapper wrapper = new ParallelWrapper.Builder(model)
  .prefetchBuffer(24)
  .workers(2)
  .workspaceMode(WorkspaceMode.SINGLE)
  .trainerFactory(new SymmetricTrainerContext())
  .trainingMode(ParallelWrapper.TrainingMode.CUSTOM)
  .gradientsAccumulator(new EncodedGradientsAccumulator(2, 1e-3))
  .build();

ParallelWrapper wrapper = new ParallelWrapper.Builder(net)
  .prefetchBuffer(24)
  .workers(8)
  .averagingFrequency(3)
  .reportScoreAfterAveraging(true)
  .build();

ParallelWrapper wrapper = new ParallelWrapper.Builder(model)
  .prefetchBuffer(24)
  .workers(2)
  .averagingFrequency(3)
  .reportScoreAfterAveraging(true)
  .build();

ParallelWrapper wrapper = new ParallelWrapper.Builder(net)
  .prefetchBuffer(24)
  .workers(4)
  .averagingFrequency(3)
  .reportScoreAfterAveraging(true)
  .build();

log.info(transferLearningHelper.unfrozenGraph().summary());
ParallelWrapper wrapper = new ParallelWrapper.Builder(transferLearningHelper.unfrozenGraph())
  .prefetchBuffer(24)
  .workers(Nd4j.getAffinityManager().getNumberOfDevices())
  .averagingFrequency(3)
  .reportScoreAfterAveraging(true)
  .build();

DataSetIterator test = new ExistingMiniBatchDataSetIterator(new File(TEST_PATH));
ParallelWrapper pw = new ParallelWrapper.Builder<>(net)
  .prefetchBuffer(16 * Nd4j.getAffinityManager().getNumberOfDevices())
  .reportScoreAfterAveraging(true)
  .averagingFrequency(10)
  .workers(Nd4j.getAffinityManager().getNumberOfDevices())
  .build();