Examples with ParallelWrapper org.deeplearning4j.parallelism.ParallelWrapper used on opensource projects

Example 1 with ParallelWrapper

use of org.deeplearning4j.parallelism.ParallelWrapper in project deeplearning4j by deeplearning4j.

the class ParallelWrapperMain method runMain.

public void runMain(String... args) throws Exception {
    JCommander jcmdr = new JCommander(this);
    try {
        jcmdr.parse(args);
    } catch (ParameterException e) {
        System.err.println(e.getMessage());
        //User provides invalid input -> print the usage info
        jcmdr.usage();
        try {
            Thread.sleep(500);
        } catch (Exception e2) {
        }
        System.exit(1);
    }
    Model model = ModelGuesser.loadModelGuess(modelPath);
    // ParallelWrapper will take care of load balancing between GPUs.
    ParallelWrapper wrapper = new ParallelWrapper.Builder(model).prefetchBuffer(prefetchSize).workers(workers).averagingFrequency(averagingFrequency).averageUpdaters(averageUpdaters).reportScoreAfterAveraging(reportScore).useLegacyAveraging(legacyAveraging).build();
    if (dataSetIteratorFactoryClazz != null) {
        DataSetIteratorProviderFactory dataSetIteratorProviderFactory = (DataSetIteratorProviderFactory) Class.forName(dataSetIteratorFactoryClazz).newInstance();
        DataSetIterator dataSetIterator = dataSetIteratorProviderFactory.create();
        if (uiUrl != null) {
            // it's important that the UI can report results from parallel training
            // there's potential for StatsListener to fail if certain properties aren't set in the model
            StatsStorageRouter remoteUIRouter = new RemoteUIStatsStorageRouter("http://" + uiUrl);
            wrapper.setListeners(remoteUIRouter, new StatsListener(null));
        }
        wrapper.fit(dataSetIterator);
        ModelSerializer.writeModel(model, new File(modelOutputPath), true);
    } else if (multiDataSetIteratorFactoryClazz != null) {
        MultiDataSetProviderFactory multiDataSetProviderFactory = (MultiDataSetProviderFactory) Class.forName(multiDataSetIteratorFactoryClazz).newInstance();
        MultiDataSetIterator iterator = multiDataSetProviderFactory.create();
        if (uiUrl != null) {
            // it's important that the UI can report results from parallel training
            // there's potential for StatsListener to fail if certain properties aren't set in the model
            StatsStorageRouter remoteUIRouter = new RemoteUIStatsStorageRouter("http://" + uiUrl);
            wrapper.setListeners(remoteUIRouter, new StatsListener(null));
        }
        wrapper.fit(iterator);
        ModelSerializer.writeModel(model, new File(modelOutputPath), true);
    } else {
        throw new IllegalStateException("Please provide a datasetiteraator or multi datasetiterator class");
    }
}