com.cloudera.knittingboar.sgd.TestRunPOLRMasterAndNWorkers.java Source code

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/**
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *     http://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.
 */

package com.cloudera.knittingboar.sgd;

import java.io.IOException;
import java.util.ArrayList;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.InputSplit;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;

import com.cloudera.knittingboar.io.InputRecordsSplit;
import com.cloudera.knittingboar.messages.GlobalParameterVectorUpdateMessage;
import com.cloudera.knittingboar.messages.GradientUpdateMessage;
import com.cloudera.knittingboar.utils.Utils;

import junit.framework.TestCase;

/**
 * A simulation of a single POLR master and a worker for each generated split of the data
 * 
 * @author jpatterson
 *
 */
public class TestRunPOLRMasterAndNWorkers extends TestCase {

    private static JobConf defaultConf = new JobConf();
    private static FileSystem localFs = null;
    static {
        try {
            defaultConf.set("fs.defaultFS", "file:///");
            localFs = FileSystem.getLocal(defaultConf);
        } catch (IOException e) {
            throw new RuntimeException("init failure", e);
        }
    }

    private static Path workDir = new Path(
            System.getProperty("test.build.data", "/Users/jpatterson/Downloads/datasets/20news-kboar/train4/"));

    public Configuration generateDebugConfigurationObject() {

        Configuration c = new Configuration();

        // feature vector size
        c.setInt("com.cloudera.knittingboar.setup.FeatureVectorSize", 10000);

        c.setInt("com.cloudera.knittingboar.setup.numCategories", 20);

        c.setInt("com.cloudera.knittingboar.setup.BatchSize", 500);

        c.setInt("com.cloudera.knittingboar.setup.NumberPasses", 1);

        // local input split path
        c.set("com.cloudera.knittingboar.setup.LocalInputSplitPath", "hdfs://127.0.0.1/input/0");

        // setup 20newsgroups
        c.set("com.cloudera.knittingboar.setup.RecordFactoryClassname",
                "com.cloudera.knittingboar.records.TwentyNewsgroupsRecordFactory");

        /*    // predictor label names
            c.set( "com.cloudera.knittingboar.setup.PredictorLabelNames", "x,y" );
            
            // predictor var types
            c.set( "com.cloudera.knittingboar.setup.PredictorVariableTypes", "numeric,numeric" );
                
            // target variables
            c.set( "com.cloudera.knittingboar.setup.TargetVariableName", "color" );
            
            // column header names
            c.set( "com.cloudera.knittingboar.setup.ColumnHeaderNames", "x,y,shape,color,k,k0,xx,xy,yy,a,b,c,bias" );
            //c.set( "com.cloudera.knittingboar.setup.ColumnHeaderNames", "\"x\",\"y\",\"shape\",\"color\",\"k\",\"k0\",\"xx\",\"xy\",\"yy\",\"a\",\"b\",\"c\",\"bias\"\n" );
         */
        return c;

    }

    public InputSplit[] generateDebugSplits(Path input_path, JobConf job) {

        long block_size = localFs.getDefaultBlockSize();

        System.out.println("default block size: " + (block_size / 1024 / 1024) + "MB");

        // ---- set where we'll read the input files from -------------
        FileInputFormat.setInputPaths(job, input_path);

        // try splitting the file in a variety of sizes
        TextInputFormat format = new TextInputFormat();
        format.configure(job);

        int numSplits = 1;

        InputSplit[] splits = null;

        try {
            splits = format.getSplits(job, numSplits);
        } catch (IOException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }

        return splits;

    }

    public void testRunMasterAndTwoWorkers() throws Exception {

        long ts_start = System.currentTimeMillis();

        System.out.println("start-ms:" + ts_start);

        POLRMasterDriver master = new POLRMasterDriver();
        // ------------------    
        // generate the debug conf ---- normally setup by YARN stuff
        master.setConf(this.generateDebugConfigurationObject());
        // now load the conf stuff into locally used vars
        try {
            master.LoadConfigVarsLocally();
        } catch (Exception e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
            System.out.println("Conf load fail: shutting down.");
            assertEquals(0, 1);
        }
        // now construct any needed machine learning data structures based on config
        master.Setup();
        // ------------------    

        // ---- this all needs to be done in 
        JobConf job = new JobConf(defaultConf);

        // TODO: work on this, splits are generating for everything in dir
        InputSplit[] splits = generateDebugSplits(workDir, job);

        System.out.println("split count: " + splits.length);

        ArrayList<POLRWorkerDriver> workers = new ArrayList<POLRWorkerDriver>();

        for (int x = 0; x < splits.length; x++) {
            //for ( int x = 0; x < 1; x++ ) {

            POLRWorkerDriver worker_model_builder = new POLRWorkerDriver(); //workers.get(x);
            worker_model_builder.internalID = String.valueOf(x);
            // simulates the conf stuff
            worker_model_builder.setConf(this.generateDebugConfigurationObject());

            InputRecordsSplit custom_reader_0 = new InputRecordsSplit(job, splits[x]);
            // TODO: set this up to run through the conf pathways
            worker_model_builder.setupInputSplit(custom_reader_0);

            worker_model_builder.LoadConfigVarsLocally();
            worker_model_builder.Setup();

            workers.add(worker_model_builder);

            System.out.println("> Setup Worker " + x);

        }

        boolean bContinuePass = true;
        int x = 0;

        while (bContinuePass) {

            bContinuePass = false;

            for (int worker_id = 0; worker_id < workers.size(); worker_id++) {

                //arContinueTracker[worker_id] 
                boolean result = workers.get(worker_id).RunNextTrainingBatch();
                if (result) {
                    bContinuePass = true;
                }

                //GradientUpdateMessage msg0 = workers.get(worker_id).GenerateUpdateMessage();
                GradientUpdateMessage msg0 = workers.get(worker_id).GenerateParamVectorUpdateMessage();

                master.AddIncomingGradientMessageToQueue(msg0);
                master.RecvGradientMessage(); // process msg

            }

            master.AveragePVec_GenerateGlobalUpdateVector(workers.size());

            if (bContinuePass) {

                /*        
                        //master.GenerateGlobalUpdateVector();
                        master.AveragePVec_GenerateGlobalUpdateVector(workers.size());
                */
                GlobalParameterVectorUpdateMessage returned_msg = master.GetNextGlobalUpdateMsgFromQueue();

                // process global updates
                for (int worker_id = 0; worker_id < workers.size(); worker_id++) {

                    workers.get(worker_id).ProcessIncomingParameterVectorMessage(returned_msg);

                }

                System.out.println("---------- cycle " + x + " done in pass "
                        + workers.get(0).GetCurrentLocalPassCount() + " ------------- ");

            } else {

                System.out.println("---------- cycle " + x + " done in pass "
                        + workers.get(0).GetCurrentLocalPassCount() + " ------------- ");

                System.out.println("> Saving Model...");

                master.SaveModelLocally("/tmp/TestRunPOLRMasterAndNWorkers.20news.model");

            } // if     

            x++;

        } // for

        Utils.PrintVectorSection(master.global_parameter_vector.gamma.viewRow(0), 3);

        long ts_total = System.currentTimeMillis() - ts_start;

        System.out.println("total time in ms:" + ts_total);

    }

}