Theano is a very interesting Python library developed mainly for deep learning, which can run calculations on some NVIDIA GPUs by using the CUDA library.  Setting up Theano to use the GPU can be a little tricky and take a bit of work.

Install the pre-reqs

sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev

Next, create a symlink to libglut, which will allow you to install the CUDA samples as described on Utkarsh Jaiswal’s blog

sudo ln -s /usr/lib/x86_64-linux-gnu/libglut.so.3 /usr/lib/libglut.so

Install CUDA
Download CUDA from the NVIDIA site and then install it:

sudo apt-get remove --purge nvidia*
chmod +x cuda_5.0.35_linux_64_ubuntu11.10-1.run
sudo service lightdm stop
sudo ./cuda_5.0.35_linux_64_ubuntu11.10-1.run

Install Theano

Get the latest released version of Theano:

sudo apt-get install python-dev libopenblas-dev liblapack-dev gfortran
sudo pip install --upgrade Theano

Create a ~/.theanorc file to enable the GPU:

[global]
floatX = float32
device = gpu

Test it out

Now run the sample program under “Testing Theano with GPU” in the Theano tutorial. It will hopefully tell you that it used your GPU.

A good benchmark to test out the speed of your setup is to run /usr/local/lib/python2.7/dist-packages/theano/misc/check_blas.py

Credits

Thanks to the Theano developers for providing this awesome library and to Andrew Ng, Samy Bengio, and the other Googlers who have been taking their time to teach the rest of us more machine learning concepts.

We recently had a PhD candidate from UCI come in and speak to the AI club at Google Irvine to speak about her research on Latent Dirichlet Allocation (LDA). LDA is a topic model and groups words into topics where each article is comprised of a mixture of topics. I was interested to play around with this a bit, so I downloaded Mallet and wrote up some quick code to try making my own LDA model.

package com.benmccann.topicmodel;

import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.TreeSet;

import cc.mallet.pipe.CharSequence2TokenSequence;
import cc.mallet.pipe.Pipe;
import cc.mallet.pipe.SerialPipes;
import cc.mallet.pipe.TokenSequence2FeatureSequence;
import cc.mallet.pipe.TokenSequenceLowercase;
import cc.mallet.pipe.TokenSequenceRemoveStopwords;
import cc.mallet.pipe.iterator.ArrayIterator;
import cc.mallet.topics.ParallelTopicModel;
import cc.mallet.types.Alphabet;
import cc.mallet.types.IDSorter;
import cc.mallet.types.InstanceList;

import com.google.inject.Guice;
import com.google.inject.Inject;
import com.google.inject.Injector;

public class Lda {

  @Inject private com.benmccann.topicmodel.TextProvider textProvider;

  InstanceList createInstanceList(List<String> texts) throws IOException {
    ArrayList<Pipe> pipes = new ArrayList<Pipe>();
    pipes.add(new CharSequence2TokenSequence());
    pipes.add(new TokenSequenceLowercase());
    pipes.add(new TokenSequenceRemoveStopwords());
    pipes.add(new TokenSequence2FeatureSequence());
    InstanceList instanceList = new InstanceList(new SerialPipes(pipes));
    instanceList.addThruPipe(new ArrayIterator(texts));
    return instanceList;
  }

  private ParallelTopicModel createNewModel() throws IOException {
    List<String> texts = textProvider.getTexts();
    InstanceList instanceList = createInstanceList(texts);
    int numTopics = instanceList.size() / 5;
    ParallelTopicModel model = new ParallelTopicModel(numTopics);
    model.addInstances(instanceList);
    model.estimate();
    return model;
  }

  ParallelTopicModel getOrCreateModel() throws Exception {
    return getOrCreateModel("model");
  }

  private ParallelTopicModel getOrCreateModel(String directoryPath)
      throws Exception {
    File directory = new File(directoryPath);
    if (!directory.exists()) {
      directory.mkdir();
    }
    File file = new File(directory, "mallet-lda.model");
    ParallelTopicModel model = null;
    if (!file.exists()) {
      model = createNewModel();
      model.write(file);
    } else {
      model = ParallelTopicModel.read(file);
    }
    return model;
  }

  public void printTopics() throws Exception {
    ParallelTopicModel model = getOrCreateModel();
    Alphabet alphabet = model.getAlphabet();
    for (TreeSet<IDSorter> set : model.getSortedWords()) {
      System.out.print("TOPIC: ");
      for (IDSorter s : set) {
        System.out.print(alphabet.lookupObject(s.getID()) + ", ");
      }
      System.out.println();
    }
  }

  public static void main(String[] args) throws Exception {
    Injector injector = Guice.createInjector();
    Lda lda = injector.getInstance(Lda.class);
    lda.printTopics();
  }

}

One of the things I found interesting was that you have to specify a number of topics. This is where the ‘art’ of machine learning comes in. With some training data this parameter could be tuned to perform better than my random guesses.