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DeepLearning4j分布式訓(xùn)練怎么實現(xiàn)

小億
107
2024-03-25 13:51:51

DeepLearning4j可以通過使用Apache Spark或者Hadoop來實現(xiàn)分布式訓(xùn)練。下面是使用Apache Spark來實現(xiàn)分布式訓(xùn)練的步驟：

  1. 在pom.xml文件中添加以下依賴：
<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-core</artifactId>
    <version>1.0.0-beta3</version>
</dependency>
<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-ui_2.10</artifactId>
    <version>1.0.0-beta3</version>
</dependency>
<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-scaleout</artifactId>
    <version>1.0.0-beta3</version>
</dependency>
<dependency>
    <groupId>org.nd4j</groupId>
    <artifactId>nd4j-native</artifactId>
    <version>1.0.0-beta3</version>
</dependency>
<dependency>
    <groupId>org.nd4j</groupId>
    <artifactId>nd4j-cuda-9.2-platform</artifactId>
    <version>1.0.0-beta3</version>
</dependency>
<dependency>
    <groupId>org.datavec</groupId>
    <artifactId>datavec-api</artifactId>
    <version>1.0.0-beta3</version>
</dependency>
<dependency>
    <groupId>org.datavec</groupId>
    <artifactId>datavec-local</artifactId>
    <version>1.0.0-beta3</version>
</dependency>
<dependency>
    <groupId>org.datavec</groupId>
    <artifactId>datavec-spark_2.10</artifactId>
    <version>1.0.0-beta3</version>
</dependency>
  1. 創(chuàng)建一個SparkConf對象和JavaSparkContext對象：
SparkConf conf = new SparkConf();
conf.setAppName("DL4J Spark");
JavaSparkContext sc = new JavaSparkContext(conf);
  1. 加載數(shù)據(jù)集并創(chuàng)建一個DataSet對象：
JavaRDD<String> data = sc.textFile("hdfs://path/to/data.txt");
JavaRDD<DataSet> dataSet = data.map(new StringToDataSet());
  1. 創(chuàng)建一個MultiLayerConfiguration對象并設(shè)置神經(jīng)網(wǎng)絡(luò)的配置：
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
    .seed(12345)
    .weightInit(WeightInit.XAVIER)
    .updater(new Adam(0.01))
    .list()
    .layer(0, new DenseLayer.Builder().nIn(784).nOut(250)
        .activation(Activation.RELU)
        .build())
    .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
        .activation(Activation.SOFTMAX)
        .nIn(250).nOut(10).build())
    .build();
  1. 創(chuàng)建一個ComputationGraph對象并使用SparkComputationGraph對象進(jìn)行訓(xùn)練：
ComputationGraph model = new ComputationGraph(conf);
model.init();
SparkComputationGraph sparkNet = new SparkComputationGraph(sc, model);
sparkNet.fit(dataSet);

通過以上步驟，就可以使用DeepLearning4j和Apache Spark實現(xiàn)分布式訓(xùn)練。同樣的，如果要使用Hadoop來實現(xiàn)分布式訓(xùn)練，可以使用datavec-hadoop依賴來讀取HDFS中的數(shù)據(jù)集，并使用SparkComputationGraph對象進(jìn)行訓(xùn)練。

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