rnn-tutorial-gru-lstm

by dennybritz

Language Model GRU with Python and Theano

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This repositoriy belongs to Part 4 of the WildML RNN Tutorial. The previous parts are here:

Jupyter Notebook Setup

System Requirements:

  • Python, pip
  • virtualenv (optional, but recommended)

To start the Jupyter Notebook:

# Clone the repo
git clone https://github.com/dennybritz/rnn-tutorial-lstm
cd rnn-tutorial-lstm

Create a new virtual environment (optional, but recommended)

virtualenv venv source venv/bin/activate

Install requirements

pip install -r requirements.txt

Start the notebook server

jupyter notebook

Setting up a CUDA-enabled GPU instance on EC2:

# 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 [email protected]: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.

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