Speed up your Neural Network with Theano and the GPU
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README.md
gputest.py
nn-theano-gpu.ipynb
nn-theano.ipynb
requirements.txt

README.md

Optimizing Neural Network implementation with Theano

Read the blog post here.

Local Jupyter notebook setup

# Create a new virtual environment (optional)
virtualenv venv
# Install requirements
pip install -r requirements.txt
# Start the notebook server
jupyter notebook .

Running on an GPU-optimized Amazon EC2 instance

Use an AWS GPU-optimized instance, for example g2.2xlarge. You can use the following commands to configure an Ubuntu machine:

# Install build tools
sudo apt-get update
sudo apt-get install -y build-essential git python-pip libfreetype6-dev libxft-dev libncurses-dev libopenblas-dev  gfortran python-matplotlib libblas-dev liblapack-dev libatlas-base-dev python-dev python-pydot linux-headers-generic linux-image-extra-virtual
sudo pip install -U pip

# Install CUDA 7
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1410/x86_64/cuda-repo-ubuntu1410_7.0-28_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1410_7.0-28_amd64.deb
sudo apt-get update
sudo apt-get install -y cuda
sudo reboot

# Clone the repo and install requirements
git clone git@github.com:dennybritz/nn-theano.git
cd nn-theano
sudo pip install -r requirements.txt

# Set Environment variables
export CUDA_ROOT=/usr/local/cuda-7.0
export PATH=$PATH:$CUDA_ROOT/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_ROOT/lib64
export THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32
# For profiling only
export CUDA_LAUNCH_BLOCKING=1

# Startup jupyter noteboook
jupyter notebook

To start a public notebook server that is accessible over the network you can follow the official instructions.