====== Gensim Vector Space Modelling ======

===== Introduction =====

[[http://nlp.fi.muni.cz/projekty/gensim/index.html|Gensim]] is a python module for vector space modeling, and it is extremely neat. It implements an incremental stochastic singular value decomosition algorithm that may be computed on distributed computer networks((Halko, N. and Martinsson, P.G. and Tropp, J.A., 2009: [[http://arxiv.org/pdf/0909.4061|Finding structure with randomness: Stochastic algorithms for constructing approximate matrix decompositions]])). 

Within the IKW network, I set up an [[#Local GenSim Environment|local environment]] with which you can experiment.

Simple use case:

<code python>
from gensim import corpora, models, similarities

# load corpus from List-Of-Words file (each line is a document, first line states number of documents)
# Doesn't have to be bzipped, but it CAN :-)
corpus = corpora.LowCorpus('/net/data/CL/projects/wordspace/software_tests/corpora/editedCorpusLOW.bz2')

# corpus will now be represented as a spare bag of words matrix. It also created a dictionary that maps 
# IDs of words to words, which can be used like this:
# >>> corpus.id2word[2923]

# Transform this corpus using Latent Semantic Indexing - we want 100 topics.
lsi = models.LsiModel(corpus, numTopics=200, id2word=corpus.id2word)

# Done. We could now query for different topic, eg

lsimodel.printTopic(10, topN = 5)

# would yield
# '-0.340 * "category" + 0.298 * "$M$" + 0.183 * "algebra" + -0.174 * "functor" + -0.168 * "operator"'
</code>

===== Benchmark =====

I used the EditedCorpora File to benchmark performance. The File was edited into the List-of-Words-Format (by just inserting a line containing the number of documents at the top).  Afterwards Latent Semantic Indexing was performed on the corpus. As the algorithm used for singular value decomposition is incremental, the memory load is constant and can be controlled by passing a **chunks** parameter to the constructor of the LSI model. This parameter controls how many documents will be loaded into RAM at once, the default is 20000. Larger chunks will speed things up, but also require more RAM. In the distributed mode, this is the number of documents which will be passed to the workers over the network, hence we have to factor in the network transmission speed in choosing our chunk size. For the following experiments, a chunk size of 1000 documents was used.

== Loading the Corpus ==

Loading the corpus and transforming it into sparse vectors takes quite exactly 23 minutes on Quickie.

== Single Mode ==

== Distributed Mode ==

Please refer to the [[#Advanced Usage| advanced usage]] section for details on how to setup Gensim in distributed mode. For testing the distributed mode of the algorithm, twelve 2.54 GHz, 4 GB RAM dual core boxes have been used as workers, with one worker per core, totaling 24 workers. LI


====== Local GenSim Environment ======

===== Using Gensim within the IKW =====

Within the IKW network, there is a local installation of gensim (which in turn depends on Numpy >= 1.4 (current version on IKW machines is 1.3) and Pyro >=4.1), residing in

  /net/data/CL/projects/wordspace/gensim_local
  
to use it, change into this directory and run

<code Bash>
source bin/activate
python
</code>

This loads the libraries installed locally in this directory and starts the python interpreter. To stop using the virtual environment, simpy run

  deactivate

===== Installing GenSim =====

In theory, you can copy the entire gensim_local folder to your machine and run it from there, however I won't guarantee that this works in practice. However, it is easy to create such a local environment yourself:

<code Bash>
# Skip this step if you already have virtualenv
easy_install virtualenv
# If you don't have sudo rights, install virtualenv locally:
# mkdir ~/opt
# easy_install --instal-dir opt virtualenv

# Create a clean virtual environment
virtualenv --no-site-packages myVirtualEnv

# And activate it
cd myVirtualEnv
source bin/activate

# Now, using pip, install the other stuff
bin/pip install numpy
bin/pip install gensim[distributed]
bin/pip install Pyro4
</code>


====== Advanced Usage ======

===== Distributed Mode =====

==== Theory ====

The distributed mode works using the [[http://www.xs4all.nl/~irmen/pyro4/|Pyro4]] Library. The idea behind this library is that you can instantiate python objects remotely and then forget that you have instantiated them remotely and just work with them like normal objects. However, these objects will eat their own local resources. Plus, you don't have to rewrite a single line of code if you want to use them locally. Most of Pyro's functionality is neatly wrapped by Gensim and works off the shelf. Of course, there are a few things you'll have to take care of:

  - Tell Gensim which computers to use. We'll do this by running a little script on each computer on the network hat we want to work for us. This script will create workers which can be enslaved. I wrapped the script into another script which automatically creates enough workers to match the number of CPUs in the box you're using, each worker will hog onto one CPU.
  - Workers will get easily lost in the network, hence we'll need a name server that keeps track of all the workers we have. Of course, there's a script for that, too. Behind the scenes, workers will communicate over TCP/IP with the nameserver an
  - Furthermore, our enslaved workers may be good at maths, but behave like little children when it comes to sharing the data between them. Therefore, we will need a dispatcher that distributes data to process to all workers evenly, and handles the feedback from the worker. So, our algorithm as such will give a task to the dispatcher, he'll break it into chunks and gives to the workers (using the name server to locate them), they report back, we get our results.

==== Practise ====

iIn the local Gensim folder, I prepared four scripts to put the procedure described above into practice, 

  * run_nameserver.sh
  * create_worker.sh
  * run_dispatcher.sh
  * clean_up.sh

As neither the name server nor the dispatcher will require a lot of resources, we can easily run them on our local machine (however, remember the data flow is data server -> dispatcher -> worker, so keep it tight). For running the workers, we have three options:

  - Log into all desired machines manually and run the worker script
  - Use a [[http://cssh.sourceforge.net/docs/cssh_man.html|cluster ssh call]] to do this job
  - Use the [[https://doc.ikw.uni-osnabrueck.de/content/using-ikw-grid|IKW Grid Engine]]

 The advantage of 2) is that it is somewhat easier than using the grid, however make sure you're not clogging someone else's work station - the grid engine takes care of distributing the work load evenly. 

=== CSSH ===

Method 2) would be as follows:

{{ :software:12_cssh_jobs.png?500|CSSH needs a lot of screen real estate...}}

<code Bash>
$ # Run nameserver
$ cd /net/data/CL/projects/wordspace/gensim_local
$ ./run_nameserver
$ #  You may want to open separate terminals / tabs / screens for name server, dispatcher and cluster ssh.
$ cssh dolly01 dolly02 dolly03 dolly04
</code>

This will open a window with which you can send the same commands to all four machines (dolly01 - 04 in our case). A list of all nodes on the grid can be found on [[https://ganglia.ikw.uni-osnabrueck.de/|Ganglia]]. Log in, and perform the following:

<code Bash>
$ cd /net/data/CL/projects/wordspace/gensim_local
$ ./create_worker
</code>

This will create between one and eight workers, depending on the number of CPUs in each machine. Back on your local box, type

<code Bash>
$./run_dispatcher
</code>

Done, all set up. Now, start your favourite interactive Python shell and work with Gensim as introduced [[#Introduction|above]]. But this time, run the LSI in distributed mode:

<code Python>
lsi = models.LsiModel(corpus, numTopics=200, id2word=corpus.id2word, distributed = True, chunks = 2000)
</code>

That's all there is to it! Play around with the chunk size to maximize the speed. After you're done, make sure to clean up behind you by killing all slaves, the dispatcher and the name server. This brutal processocide can be efficiently and hygienically performed by running 

<code Bash>
$ ./clean_up.sh
</code>

on your local box and all boxes containing workers (do so with your cssh terminal). 

=== Use Screens! ===

A word of warning: using this method it's very easy to, well, loose your processes in the endless depths of the network. It is therefore recommendable to open a [[http://news.softpedia.com/news/GNU-Screen-Tutorial-44274.shtml|screen]] on your host machine (eg. quickie) before you start working. Simple tutorial:

  screen
  
Opens a screen. You may now create new virtual terminals with <CTRL + a><c> , switch between them with <CTRL + a><[0-9]>. To leave your screen _without terminating it_, type <CTRL + a><d> You are back on your terminal now. You may log out from this machine, go home, log in again. The screen will still be there, waiting for you. List available screens with

  $ screen -ls
  There is a screen on:
      15500.ttys001.beta	(Detached)
  1 Socket in /var/folders/zz/zzzivhrRnAmviuee++0-sU++US6/-Tmp-/.screen.

Get back to your screen with

  screen -r 15500

So, if you're connection is lost, if you want to continue working from somewhere else, or if somebody shuts down your computer while you are at the coffee break, the connections to your enslaved army of workers will still be there.