org.deeplearning4j.parallelism

Class ParallelWrapper.Builder<T extends org.deeplearning4j.nn.api.Model>

java.lang.Object

org.deeplearning4j.parallelism.ParallelWrapper.Builder<T>

Enclosing class:

ParallelWrapper

public static class ParallelWrapper.Builder<T extends org.deeplearning4j.nn.api.Model>
extends Object

Field Summary

Fields

Modifier and Type	Field and Description
protected org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator	accumulator
protected boolean	averageUpdaters
protected int	averagingFrequency
protected boolean	isMQ
protected boolean	legacyAveraging
protected T	model
protected int	prefetchSize
protected boolean	reportScore
protected TrainerContext	trainerContext
protected Object[]	trainerContextArgs
protected ParallelWrapper.TrainingMode	trainingMode
protected int	workers
protected org.deeplearning4j.nn.conf.WorkspaceMode	workspaceMode

Constructor Summary

Constructors

Constructor and Description
Builder(T model) Build ParallelWrapper for MultiLayerNetwork

Method Summary

Modifier and Type	Method and Description
ParallelWrapper.Builder	averageUpdaters(boolean reallyAverage) This method enables/disables updaters averaging.
ParallelWrapper.Builder	averagingFrequency(int freq) Model averaging frequency.
ParallelWrapper	build() This method returns ParallelWrapper instance
ParallelWrapper.Builder	gradientsAccumulator(org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator accumulator) This method allows you to specify GradientsAccumulator instance to be used in this ParallelWrapper instance
ParallelWrapper.Builder	prefetchBuffer(int size) Size of prefetch buffer that will be used for background data prefetching.
ParallelWrapper.Builder	reportScoreAfterAveraging(boolean reallyReport) This method enables/disables averaged model score reporting
ParallelWrapper.Builder	trainerContextArgs(Object... trainerContextArgs) Transer context args are for calling a TrainerContext init method when ParallelWrapper starts training
ParallelWrapper.Builder	trainerFactory(TrainerContext trainerContext) Specify a TrainerContext for the given ParallelWrapper instance.
ParallelWrapper.Builder	trainingMode(ParallelWrapper.TrainingMode mode)
ParallelWrapper.Builder	workers(int num) This method allows to configure number of workers that'll be used for parallel training
ParallelWrapper.Builder	workspaceMode(org.deeplearning4j.nn.conf.WorkspaceMode mode)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

trainingMode

protected ParallelWrapper.TrainingMode trainingMode

model

protected T extends org.deeplearning4j.nn.api.Model model

workers

protected int workers

prefetchSize

protected int prefetchSize

averagingFrequency

protected int averagingFrequency

reportScore

protected boolean reportScore

averageUpdaters

protected boolean averageUpdaters

legacyAveraging

protected boolean legacyAveraging

isMQ

protected boolean isMQ

trainerContext

protected TrainerContext trainerContext

trainerContextArgs

protected Object[] trainerContextArgs

workspaceMode

protected org.deeplearning4j.nn.conf.WorkspaceMode workspaceMode

accumulator

protected org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator accumulator

Constructor Detail

Builder

public Builder(@NonNull
               T model)

Build ParallelWrapper for MultiLayerNetwork

Parameters:

model -

Method Detail

trainerContextArgs

public ParallelWrapper.Builder trainerContextArgs(Object... trainerContextArgs)

Transer context args are for calling a TrainerContext init method when ParallelWrapper starts training

Parameters:

trainerContextArgs - the args to use (maybe null)

Returns:

trainerFactory

public ParallelWrapper.Builder trainerFactory(@NonNull
                                              TrainerContext trainerContext)

Specify a TrainerContext for the given ParallelWrapper instance. Defaults to DefaultTrainerContext otherwise

Parameters:

trainerContext - the trainer factory to use

Returns:

builder pattern

workspaceMode

public ParallelWrapper.Builder workspaceMode(@NonNull
                                             org.deeplearning4j.nn.conf.WorkspaceMode mode)

workers

public ParallelWrapper.Builder workers(int num)

This method allows to configure number of workers that'll be used for parallel training

Parameters:

num -

Returns:

averagingFrequency

public ParallelWrapper.Builder averagingFrequency(int freq)

Model averaging frequency.

Parameters:

freq - number of iterations between averaging

Returns:

averageUpdaters

public ParallelWrapper.Builder averageUpdaters(boolean reallyAverage)

This method enables/disables updaters averaging. Default value: TRUE PLEASE NOTE: This method is suitable for debugging purposes mostly. So don't change default value, unless you're sure why you need it.

Parameters:

reallyAverage -

Returns:

prefetchBuffer

public ParallelWrapper.Builder prefetchBuffer(int size)

Size of prefetch buffer that will be used for background data prefetching. Usually it's better to keep this value equal to the number of workers. Default value: 2

Parameters:

size - 0 to disable prefetching, any positive number

Returns:

trainingMode

public ParallelWrapper.Builder trainingMode(@NonNull
                                            ParallelWrapper.TrainingMode mode)

Parameters:

mode -

Returns:

gradientsAccumulator

public ParallelWrapper.Builder gradientsAccumulator(@NonNull
                                                    org.deeplearning4j.optimize.solvers.accumulation.GradientsAccumulator accumulator)

This method allows you to specify GradientsAccumulator instance to be used in this ParallelWrapper instance

Parameters:

accumulator -

Returns:

reportScoreAfterAveraging

public ParallelWrapper.Builder reportScoreAfterAveraging(boolean reallyReport)

This method enables/disables averaged model score reporting

Parameters:

reallyReport -

Returns:

build

public ParallelWrapper build()

This method returns ParallelWrapper instance

Returns: