https://github.com/ogrisel/parallel_ml_tutorial

Tutorial on scikit-learn and IPython for parallel machine learning
https://github.com/ogrisel/parallel_ml_tutorial

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Tutorial on scikit-learn and IPython for parallel machine learning

• Host: GitHub
• URL: https://github.com/ogrisel/parallel_ml_tutorial
• Owner: ogrisel
• Created: 2013-01-10T22:31:26.000Z (about 12 years ago)
• Default Branch: master
• Last Pushed: 2016-10-04T04:50:13.000Z (over 8 years ago)
• Last Synced: 2025-01-12T01:04:45.324Z (7 days ago)
• Language: Jupyter Notebook
• Size: 4.79 MB
• Stars: 1,593
• Watchers: 183
• Forks: 599
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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README

        
# Parallel Machine Learning with scikit-learn and IPython

[![Video Tutorial](https://raw.github.com/ogrisel/parallel_ml_tutorial/master/resources/youtube_screenshot.png)](https://www.youtube.com/watch?v=iFkRt3BCctg)

Video recording of this tutorial given at PyCon in 2013. The tutorial material

has been rearranged in part and extended. Look at the title of the of the

notebooks to be able to follow along the presentation.

Browse the static [notebooks on nbviewer.ipython.org](

http://nbviewer.ipython.org/github/ogrisel/parallel_ml_tutorial/tree/master/rendered_notebooks/).

## Scope of this tutorial:

- Learn common machine learning concepts and how they match the scikit-learn

  Estimator API.

- Learn about scalable feature extraction for text classification and

  clustering

- Learn how to perform parallel cross validation and hyper parameters grid

  search in parallel with IPython.

- Learn to analyze the kinds of common errors predictive models are subject to

  and how to refine your modeling to take this analysis into account.

- Learn to optimize memory allocation on your computing nodes with numpy memory

  mapping features.

- Learn how to run a cheap IPython cluster for interactive predictive modeling on

  the Amazon EC2 spot instances using [StarCluster](http://star.mit.edu/cluster/).

## Target audience

This tutorial targets developers with some experience with scikit-learn and

machine learning concepts in general.

It is recommended to first go through one of the tutorials hosted at

[scikit-learn.org](http://scikit-learn.org) if you are new to scikit-learn.

You might might also want to have a look at [SciPy Lecture

Notes](http://scipy-lectures.github.com) first if you are new to the NumPy /

SciPy / matplotlib ecosystem.

## Setup

Install NumPy, SciPy, matplotlib, IPython, psutil, and scikit-learn in their latest

stable version (e.g. IPython 2.2.0 and scikit-learn 0.15.2 at the time of

writing).

You can find up to date installation instructions on

[scikit-learn.org](http://scikit-learn.org) and

[ipython.org](http://ipython.org) .

To check your installation, launch the `ipython` interactive shell in a console

and type the following import statements to check each library:

    >>> import numpy

    >>> import scipy

    >>> import matplotlib

    >>> import psutil

    >>> import sklearn

If you don't get any message, everything is fine. If you get an error message,

please ask for help on the mailing list of the matching project and don't

forget to mention the version of the library you are trying to install along

with the type of platform and version (e.g. Windows 8.1, Ubuntu 14.04, OSX

10.9...).

You can exit the `ipython` shell by typing `exit`.

## Fetching the data

It is recommended to fetch the datasets ahead of time before diving into the

tutorial material itself. To do so run the `fetch_data.py` script in this

folder:

    python fetch_data.py

## Using the IPython notebook to follow the tutorial

The tutorial material and exercises are hosted in a set of IPython executable

notebook files.

To run them interactively do:

    $ cd notebooks

    $ ipython notebook

This should automatically open a new browser window listing all the notebooks

of the folder.

You can then execute the cell in order by hitting the "Shift-Enter" keys and

watch the output display directly under the cell and the cursor move on to the

next cell. Go to the "Help" menu for links to the notebook tutorial.

Credits

=======

Some of this material is adapted from the scipy 2013 tutorial:

    http://github.com/jakevdp/sklearn_scipy2013

Original authors:

- Gael Varoquaux [@GaelVaroquaux](https://twitter.com/GaelVaroquaux) | http://gael-varoquaux.info

- Jake VanderPlas [@jakevdp](https://twitter.com/jakevdp) | http://jakevdp.github.com

- Olivier Grisel [@ogrisel](https://twitter.com/ogrisel) | http://ogrisel.com