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Deep Learning Vagrant Machine

These Vagrant files automates the installation of a working Deep Learning machine running on Ubuntu 14.04.

What's in the box:

  • Keras - minimalist, highly modular neural networks library.
  • Theano - library to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
  • Tensorflow - library for numerical computation using data flow graphs.
  • Jupyter - web application to create, share documents that contain live code, equations, visualizations and explanatory text.

Keras Theano TensorFlow Jupyter

Requirements

You must install VirtubalBox and Vagrant before continuing.

Getting started

Once Vagrant and VirtualBox are installed, clone this repository or import Vagrantfile and bootstrap.sh in a directory.

From this directory, let's start your Vagrant box by typing in your terminal (it might take some time to download the Ubuntu image):

$ vagrant up

Once the setup is complete, just run:

$ vagrant ssh

You are in! Now, let's train your first recurrent neuronal network:

$ python keras/examples/addition_rnn.py

If you can see that, it means that you setup is working and that you are training your recurrent neuronnal network to perform addition! addition_rnn-screenshot

To go through the code step by step, type:

$ jupyter notebook --no-browser --ip=0.0.0.0 --FileContentsManager.root_dir=/home/vagrant/keras/examples/

Open a browser and browse http://127.0.0.1:8888

Looking for some resources to get started with Deep Learning? Check out our introductory workshops.

Tips and tricks

To access files present on your computer from your Vagrant/Ubuntu machine, go to the /vagrant directory which is mounted to the directory you started you Vagrant box from:

$ cd /vagrant/

To get a list of available vagrant commands (from your host computer), just type:

$ vagrant

If you want to start your virtual machine from scratch, disconnect from it and from your host computer run:

$ vagrant destroy
$ vagrant up

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Vagrant machine ready to explore the deep learning world

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