Set up a Google Cloud GPU instance for doing the exercises in course.fast.ai

install-gpu-gce.sh

#!/bin/bash
if ! dpkg-query -W cuda; then
  curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
  dpkg -i ./cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
fi
apt-get update
apt-get -y upgrade
apt-get -y install tmux build-essential gcc g++ make binutils software-properties-common cuda unzip
modprobe nvidia
nvidia-smi
mkdir downloads
cd downloads
# Replace with an anaconda mirror near your server
wget "https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda2-4.3.0-Linux-x86_64.sh" -O "anaconda-install.sh"
bash "anaconda-install.sh" -b
echo "export PATH=\"$HOME/anaconda2/bin:\$PATH\"" >> ~/.bashrc
export PATH="$HOME/anaconda2/bin:$PATH"
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --set show_channel_urls yes
conda install -y bcolz
conda upgrade -y --all
pip install theano
echo "[global]
device = gpu
floatX = float32

[cuda]
root = /usr/local/cuda" > ~/.theanorc

pip install keras
mkdir ~/.keras
echo '{
    "image_dim_ordering": "th",
    "epsilon": 1e-07,
    "floatx": "float32",
    "backend": "theano"
}' > ~/.keras/keras.json

wget "http://platform.ai/files/cudnn.tgz" -O "cudnn.tgz"
tar -zxf cudnn.tgz
cd cuda
sudo cp lib64/* /usr/local/cuda/lib64/
sudo cp include/* /usr/local/cuda/include/
jupyter notebook --generate-config
# Replace with your own password hash
echo "c.NotebookApp.password = u'sha1:d12dfae8859909bcde06a4192d604d347d06da8c:0943f80b4790'" >> $HOME/.jupyter/jupyter_notebook_config.py
echo "c.NotebookApp.ip = '*'
c.NotebookApp.port = 443
c.NotebookApp.open_browser = False" >> $HOME/.jupyter/jupyter_notebook_config.py
cd ~
git clone https://github.com/fastai/courses.git
cd ~/courses/deeplearning1/nbs
openssl req -x509 -nodes -days 3650 -newkey rsa:1024 -keyout ~/mykey.key -out ~/mycert.pem
jupyter notebook  --certfile=~/mycert.pem --keyfile ~/mykey.key