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org.deeplearning4j.arbiter.task.MultiLayerNetworkTaskCreator Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License 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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.arbiter.task;
import lombok.AllArgsConstructor;
import lombok.Getter;
import lombok.NoArgsConstructor;
import lombok.Setter;
import lombok.extern.slf4j.Slf4j;
import org.apache.commons.lang3.exception.ExceptionUtils;
import org.bytedeco.javacpp.Pointer;
import org.deeplearning4j.arbiter.DL4JConfiguration;
import org.deeplearning4j.arbiter.listener.DL4JArbiterStatusReportingListener;
import org.deeplearning4j.arbiter.optimize.api.Candidate;
import org.deeplearning4j.arbiter.optimize.api.OptimizationResult;
import org.deeplearning4j.arbiter.optimize.api.TaskCreator;
import org.deeplearning4j.arbiter.optimize.api.data.DataProvider;
import org.deeplearning4j.arbiter.optimize.api.data.DataSource;
import org.deeplearning4j.arbiter.optimize.api.evaluation.ModelEvaluator;
import org.deeplearning4j.arbiter.optimize.api.saving.ResultReference;
import org.deeplearning4j.arbiter.optimize.api.saving.ResultSaver;
import org.deeplearning4j.arbiter.optimize.api.score.ScoreFunction;
import org.deeplearning4j.arbiter.optimize.runner.CandidateInfo;
import org.deeplearning4j.arbiter.optimize.runner.CandidateStatus;
import org.deeplearning4j.arbiter.optimize.runner.IOptimizationRunner;
import org.deeplearning4j.arbiter.optimize.runner.listener.StatusListener;
import org.deeplearning4j.arbiter.scoring.util.ScoreUtil;
import org.deeplearning4j.earlystopping.EarlyStoppingConfiguration;
import org.deeplearning4j.earlystopping.EarlyStoppingResult;
import org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.function.BiFunction;
import org.nd4j.util.StringUtils;
import java.io.IOException;
import java.util.List;
import java.util.Properties;
import java.util.concurrent.Callable;
/**
* Task creator for MultiLayerNetworks
*
* @author Alex Black
*/
@AllArgsConstructor
@NoArgsConstructor
@Slf4j
public class MultiLayerNetworkTaskCreator implements TaskCreator {
private ModelEvaluator modelEvaluator;
@Getter
@Setter
private TaskListener taskListener;
public MultiLayerNetworkTaskCreator(ModelEvaluator modelEvaluator){
this(modelEvaluator, null);
}
@Override
public Callable create(Candidate candidate, DataProvider dataProvider,
ScoreFunction scoreFunction, List statusListeners,
IOptimizationRunner runner) {
return new DL4JLearningTask(candidate, dataProvider, scoreFunction, modelEvaluator, statusListeners, taskListener, runner);
}
@Override
public Callable create(Candidate candidate, Class dataSource, Properties dataSourceProperties,
ScoreFunction scoreFunction, List statusListeners, IOptimizationRunner runner) {
return new DL4JLearningTask(candidate, dataSource, dataSourceProperties, scoreFunction, modelEvaluator, statusListeners, taskListener, runner);
}
private static class DL4JLearningTask implements Callable {
private Candidate candidate;
private DataProvider dataProvider;
private Class dataSource;
private Properties dataSourceProperties;
private ScoreFunction scoreFunction;
private ModelEvaluator modelEvaluator;
private List listeners;
private TaskListener taskListener;
private IOptimizationRunner runner;
private long startTime;
public DL4JLearningTask(Candidate candidate, DataProvider dataProvider, ScoreFunction scoreFunction,
ModelEvaluator modelEvaluator, List listeners, TaskListener taskListener,
IOptimizationRunner runner) {
this.candidate = candidate;
this.dataProvider = dataProvider;
this.scoreFunction = scoreFunction;
this.modelEvaluator = modelEvaluator;
this.listeners = listeners;
this.taskListener = taskListener;
this.runner = runner;
}
public DL4JLearningTask(Candidate candidate, Class dataSource, Properties dataSourceProperties,
ScoreFunction scoreFunction, ModelEvaluator modelEvaluator, List listeners, TaskListener taskListener,
IOptimizationRunner runner) {
this.candidate = candidate;
this.dataSource = dataSource;
this.dataSourceProperties = dataSourceProperties;
this.scoreFunction = scoreFunction;
this.modelEvaluator = modelEvaluator;
this.listeners = listeners;
this.taskListener = taskListener;
this.runner = runner;
}
@Override
public OptimizationResult call() {
try {
OptimizationResult result = callHelper();
if(listeners != null && !listeners.isEmpty()){
CandidateInfo ci = new CandidateInfo(candidate.getIndex(), CandidateStatus.Complete, result.getScore(),
startTime, startTime, System.currentTimeMillis(), candidate.getFlatParameters(), null);
for(StatusListener sl : listeners){
try{
sl.onCandidateStatusChange(ci, runner, result);
} catch (Exception e){
log.error("Error in status listener for candidate {}", candidate.getIndex(), e);
}
}
}
return result;
} catch (Throwable e) {
String stackTrace = ExceptionUtils.getStackTrace(e);
log.warn( "Execution failed for task {}", candidate.getIndex(), e );
CandidateInfo ci = new CandidateInfo(candidate.getIndex(), CandidateStatus.Failed, null, startTime,
null, null, candidate.getFlatParameters(), stackTrace);
return new OptimizationResult(candidate, null, candidate.getIndex(), null, ci, null);
} finally {
//Destroy workspaces to free memory
Nd4j.getWorkspaceManager().destroyAllWorkspacesForCurrentThread();
System.gc();
try {
//Sleep for a few seconds - workspace destruction and memory deallocation happens quickly but doesn't
// happen instantly; if we didn't have this, we may run into a situation where the next thread/task
// tries to allocate before WS memory is fully deallocated, resulting in an OOM in memory constrained
// environments
Thread.sleep(2000L);
} catch (Exception e){ }
}
}
private OptimizationResult callHelper() {
startTime = System.currentTimeMillis();
CandidateInfo ci = new CandidateInfo(candidate.getIndex(), CandidateStatus.Running, null,
startTime, startTime, null, candidate.getFlatParameters(), null);
//Create network
MultiLayerNetwork net = new MultiLayerNetwork(
((DL4JConfiguration) candidate.getValue()).getMultiLayerConfiguration());
net.init();
if(taskListener != null){
net = taskListener.preProcess(net, candidate);
}
if (listeners != null) {
net.addListeners(new DL4JArbiterStatusReportingListener(listeners, ci));
}
//Early stopping or fixed number of epochs:
DataSetIterator dataSetIterator;
if(dataSource != null){
DataSource dsInstance;
try{
dsInstance = dataSource.newInstance();
} catch (Exception e){
throw new RuntimeException("Error instantiating instance of DataSource for class " + dataSource.getName());
}
if(dataSourceProperties != null)
dsInstance.configure(dataSourceProperties);
dataSetIterator = ScoreUtil.getIterator(dsInstance.trainData());
} else {
dataSetIterator = ScoreUtil.getIterator(dataProvider.trainData(candidate.getDataParameters()));
}
EarlyStoppingConfiguration esConfig =
((DL4JConfiguration) candidate.getValue()).getEarlyStoppingConfiguration();
EarlyStoppingResult esResult = null;
if (esConfig != null) {
EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConfig, net, dataSetIterator, null);
esResult = trainer.fit();
net = esResult.getBestModel(); //Can return null if failed OR if
switch (esResult.getTerminationReason()) {
case Error:
ci.setCandidateStatus(CandidateStatus.Failed);
ci.setExceptionStackTrace(esResult.getTerminationDetails());
break;
case IterationTerminationCondition:
case EpochTerminationCondition:
ci.setCandidateStatus(CandidateStatus.Complete);
break;
}
} else {
//Fixed number of epochs
int nEpochs = ((DL4JConfiguration) candidate.getValue()).getNumEpochs();
for (int i = 0; i < nEpochs; i++) {
net.fit(dataSetIterator);
}
ci.setCandidateStatus(CandidateStatus.Complete);
}
Object additionalEvaluation = null;
if (esConfig != null && esResult.getTerminationReason() != EarlyStoppingResult.TerminationReason.Error) {
additionalEvaluation =
(modelEvaluator != null ? modelEvaluator.evaluateModel(net, dataProvider) : null);
}
Double score = null;
if (net != null) {
if(dataSource != null){
score = scoreFunction.score(net, dataSource, dataSourceProperties);
} else {
score = scoreFunction.score(net, dataProvider, candidate.getDataParameters());
}
ci.setScore(score);
}
if(taskListener != null){
taskListener.postProcess(net, candidate);
}
OptimizationResult result = new OptimizationResult(candidate, score, candidate.getIndex(), additionalEvaluation, ci, null);
//Save the model:
ResultSaver saver = runner.getConfiguration().getResultSaver();
ResultReference resultReference = null;
if (saver != null) {
try {
resultReference = saver.saveModel(result, net);
} catch (IOException e) {
//TODO: Do we want ta warn or fail on IOException?
log.warn("Error saving model (id={}): IOException thrown. ", result.getIndex(), e);
}
}
result.setResultReference(resultReference);
return result;
}
}
}
