100 lines
3.9 KiB
Java
100 lines
3.9 KiB
Java
package eu.dnetlib.jobs.deeplearning;
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import eu.dnetlib.deeplearning.support.DataSetProcessor;
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import eu.dnetlib.deeplearning.support.NetworkConfigurations;
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import eu.dnetlib.jobs.AbstractSparkJob;
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import eu.dnetlib.jobs.SparkLDATuning;
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import eu.dnetlib.support.ArgumentApplicationParser;
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import eu.dnetlib.support.ConnectedComponent;
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import org.apache.spark.SparkConf;
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import org.apache.spark.api.java.JavaRDD;
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import org.apache.spark.api.java.JavaSparkContext;
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import org.apache.spark.sql.SparkSession;
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import org.codehaus.jackson.map.ObjectMapper;
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import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
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import org.deeplearning4j.nn.graph.ComputationGraph;
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import org.deeplearning4j.optimize.listeners.PerformanceListener;
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import org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.AdaptiveThresholdAlgorithm;
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import org.deeplearning4j.spark.api.RDDTrainingApproach;
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import org.deeplearning4j.spark.api.TrainingMaster;
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import org.deeplearning4j.spark.impl.graph.SparkComputationGraph;
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import org.deeplearning4j.spark.parameterserver.training.SharedTrainingMaster;
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import org.nd4j.linalg.dataset.api.MultiDataSet;
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import org.nd4j.parameterserver.distributed.conf.VoidConfiguration;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import java.io.IOException;
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import java.util.Optional;
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public class SparkGraphClassificationTraining extends AbstractSparkJob {
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private static final Logger log = LoggerFactory.getLogger(SparkGraphClassificationTraining.class);
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public SparkGraphClassificationTraining(ArgumentApplicationParser parser, SparkSession spark) {
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super(parser, spark);
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}
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public static void main(String[] args) throws Exception {
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ArgumentApplicationParser parser = new ArgumentApplicationParser(
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readResource("/jobs/parameters/graphClassificationTraining_parameters.json", SparkLDATuning.class)
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);
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parser.parseArgument(args);
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SparkConf conf = new SparkConf();
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new SparkGraphClassificationTraining(
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parser,
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getSparkSession(conf)
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).run();
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}
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@Override
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public void run() throws IOException {
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// read oozie parameters
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final String workingPath = parser.get("workingPath");
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final int numPartitions = Optional
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.ofNullable(parser.get("numPartitions"))
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.map(Integer::valueOf)
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.orElse(NUM_PARTITIONS);
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log.info("workingPath: '{}'", workingPath);
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log.info("numPartitions: '{}'", numPartitions);
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JavaSparkContext context = JavaSparkContext.fromSparkContext(spark.sparkContext());
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VoidConfiguration conf = VoidConfiguration.builder()
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.unicastPort(40123)
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// .networkMask("255.255.148.0/22")
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.controllerAddress("127.0.0.1")
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.build();
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TrainingMaster trainingMaster = new SharedTrainingMaster.Builder(conf,1)
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.rngSeed(12345)
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.collectTrainingStats(false)
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.thresholdAlgorithm(new AdaptiveThresholdAlgorithm(1e-3))
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.batchSizePerWorker(32)
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.workersPerNode(4)
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.rddTrainingApproach(RDDTrainingApproach.Direct)
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.build();
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JavaRDD<MultiDataSet> trainData = context.objectFile(workingPath + "/groupDataset");
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SparkComputationGraph sparkComputationGraph = new SparkComputationGraph(
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context,
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NetworkConfigurations.getSimpleGCN(3, 2, 5, 2),
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trainingMaster);
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sparkComputationGraph.setListeners(new PerformanceListener(10, true));
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//execute training
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for (int i = 0; i < 20; i ++) {
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sparkComputationGraph.fitMultiDataSet(trainData);
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}
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ComputationGraph network = sparkComputationGraph.getNetwork();
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System.out.println("network = " + network.getConfiguration().toJson());
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}
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}
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