Question

今天，我将尝试在ND4J和Deeplearnint4j项目中使用CUDA。此后，Neural Net（从Keras进口）开始更快地工作。但是下一个代码开始工作缓慢

我已经尝试将ND4J后端更改为本机（CPU），并且得到了快速的结果。

突出显示的部分带有注释（两行）

import com.rabbitmq.client.Channel;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.ops.transforms.Transforms;

import java.io.IOException;
import java.sql.*;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.ConcurrentHashMap;

public class GraphUpdater implements Runnable {
    private Pair pubPair;
    private ConcurrentHashMap<Integer, INDArray> pubsList;
    private Connection connectionMain;
    private Connection connectionSite;
    private Channel channel;

    GraphUpdater(Pair pubPair, ConcurrentHashMap<Integer, INDArray> pubsList, Channel channel) throws SQLException {
    this.pubPair = pubPair;
    this.channel = channel;
    this.pubsList = pubsList;
    connectionMain = DataBaseConnectionsPool.getConnection();
    connectionSite = DataBaseConnectionsPool.getConnectionSite();
}

@Override
public void run(){
    try {
        channel.basicAck(pubPair.deliveryTag, false);
    } catch (IOException e) {
        System.out.println("Error, pub="+pubPair.pub);
        e.printStackTrace();
    }
    PreparedStatement st;
    PreparedStatement stNew;
    try {
        st = connectionMain.prepareStatement("update vec_graph set closed_pubs=closed_pubs || ? where pub=?");
        stNew = connectionMain.prepareStatement("insert into vec_graph values (?, ?)");

        Statement psNew = connectionMain.createStatement();
        ResultSet rs = psNew.executeQuery("select * from new_public_vectors where pub="+pubPair.pub);
        float[] _floatArr = new float[64];
        while (rs.next()){
            Array arr = rs.getArray("vector");
            Object[] obj = (Object[]) arr.getArray();
            for (int vIndex=0; vIndex < 64; vIndex++){
                _floatArr[vIndex] = (float)(double)obj[vIndex];
            }
            pubsList.put(rs.getInt(1), Nd4j.create(_floatArr));
        }

        //pub from task X all pubs from db
        int pub = pubPair.pub;
        List<Integer> closed = new ArrayList<>();
        double mean = 0.96D;
        INDArray currentVector = pubsList.get(pub);
        //!%!%!%!%slowly part of code
        for (int pubId : pubsList.keySet()) {
            INDArray publicVector = pubsList.get(pubId);
            if (currentVector == null || pub == pubId || publicVector == null){
                continue;
            }
            //!%!%!%!%mega slowly part of code, ~99% of CPU time in VisualVM
            double dist = -Transforms.cosineDistance(currentVector, publicVector) + 1; // Transfer from cosine sim to cosine dist
            if ((dist - mean) < 0.01 && (dist - mean) > 0){
                mean = (mean+dist)/2;
            }else if (dist > mean){
                mean = dist;
                closed.clear();
                st.clearBatch();
            }else{
                continue;
            }
            Array a = connectionMain.createArrayOf("int", new Object[]{pub});
            st.setArray(1, a);
            st.setInt(2, pubId);
            st.addBatch();
            closed.add(pubId);
        }
        Object[] obj_vector = new Object[closed.size()];
        for (int i = 0; i < closed.size(); i++){
            obj_vector[i] = closed.get(i);
        }
        Array closedArray = connectionMain.createArrayOf("int", obj_vector);
        stNew.setInt(1, pub);
        stNew.setArray(2, closedArray);
        stNew.addBatch();

        if (pubPair.byUser != 0){
            showToUser(closed, pub, pubPair.byUser);
        }
        try {
            st.executeBatch();
            stNew.executeBatch();
        }catch (BatchUpdateException e){
            e.printStackTrace();
            e.getNextException().printStackTrace();
        }
    } catch (BatchUpdateException e){
        e.printStackTrace();
        e.getNextException().printStackTrace();
    } catch (SQLException e) {
        e.printStackTrace();
    }finally {
        try {
            connectionMain.close();
            connectionSite.close();
        } catch (SQLException e) {
            e.printStackTrace();
        }
    }
}

我想从这个清单中取些东西：

获得更快的结果并使用GPU
为这部分代码关闭GPU，然后将其保留为NN

Answer 1

好的，我将用cosineDistance重写部分代码到我自己的实现中

ND4J在GPU上运行缓慢，但在CPU上运行快速

1 个答案: