Install Tensorflow GPU on Ubuntu 16.04 LTS on Google Cloud

In this Post we will discuss about how to install tensorflow GPU on google Cloud.
The first steps are to Install CUDA and CUDNN.

Tensorflow GPU install on ubuntu 16.04

These instructions are intended to set up a deep learning environment for GPU-powered tensorflow.
See here for pytorch GPU install instructions

After following these instructions you'll have:

1. Ubuntu 16.04.
2. Cuda 9.0 drivers installed.
3. A conda environment with python 3.6.
4. The latest tensorflow version with gpu support.

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Installation steps

0. update apt-get

sudo apt-get update

1. Install apt-get deps

sudo apt-get install -y openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy build-essential python-pip python3-pip python-virtualenv swig python-wheel libcurl3-dev curl   

2. install nvidia drivers

# The 16.04 installer works with 16.10.
# download drivers
curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb

# download key to allow installation
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub

# install actual package
sudo dpkg -i ./cuda-repo-ubuntu1604_9.0.176-1_amd64.deb

#  install cuda (but it'll prompt to install other deps, so we try to install twice with a dep update in between
sudo apt-get update
sudo apt-get -y install cuda-9-0   

2a. reboot Ubuntu

sudo reboot

2b. check nvidia driver install

nvidia-smi   

# you should see a list of gpus printed    
# if not, the previous steps failed.   

3. Install cudnn

wget https://s3.amazonaws.com/open-source-william-falcon/cudnn-9.0-linux-x64-v7.3.1.20.tgz
sudo tar -xzvf cudnn-9.0-linux-x64-v7.3.1.20.tgz
sudo cp cuda/include/cudnn.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

4. Add these lines to end of ~/.bashrc:

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
export CUDA_HOME=/usr/local/cuda
export PATH="$PATH:/usr/local/cuda/bin"

4a. Reload bashrc

source ~/.bashrc

5. Install miniconda

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh   

# press s to skip terms   

# Do you approve the license terms? [yes|no]
# yes

# Miniconda3 will now be installed into this location:
# accept the location

# Do you wish the installer to prepend the Miniconda3 install location
# to PATH in your /home/ghost/.bashrc ? [yes|no]
# yes    

5a. Reload bashrc

source ~/.bashrc

6. Create conda env to install tf

conda create -n tensorflow

# press y a few times 

7. Activate env

source activate tensorflow   

8. Install tensorflow with GPU support for python 3.6

pip install tf-nightly-gpu

# If the above fails, try the part below
# pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl

9. Test tf install

# start python shell   
python

# run test script   
import tensorflow as tf   

hello = tf.constant('Hello, TensorFlow!')

# when you run sess, you should see a bunch of lines with the word gpu in them (if install worked)
# otherwise, not running on gpu
sess = tf.Session()
print(sess.run(hello))