#! /bin/bash

# Refer to https://gist.github.com/lyastro/26e0cd8245bcf64914857dd5e8445724
# Preliminary requirement: 
# CUDA GPU(s) is (are) present
# gcc gcc-c++ python-pip python-devel atlas atlas-devel gcc-gfortran openssl-devel libffi-devel are present

# NVIDIA CUDA toolkit and driver are installed. For CentOS 7, it is found out that only nvidia cuda version 9.0 will work. 9.2 or 10 don't. 
# To this end, use http://comsics.usm.my/tlyoon/configrepo/howto/customise_centos/nvidia_cuda/inst_cuda_centos_localrunfile.txt to first install cuda 9.2 (if you not have already done so).

# Then download and install cuda-9.0 using local runfile, but choose not to install the graphic card driver. Install only the 9.0 toolkit. This is done via the following lines:

cd /share/apps/configrepo
wget -O cuda_9.0.176_384.81_linux-run https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/cuda_9.0.176_384.81_linux-run
chmod +x cuda_9.0.176_384.81_linux-run
./cuda_9.0.176_384.81_linux-run

## patch 1
wget -O cuda_9.0.176.1_linux-run https://developer.nvidia.com/compute/cuda/9.0/Prod/patches/1/cuda_9.0.176.1_linux-run
chmod +x cuda_9.0.176.*
./cuda_9.0.176.1_linux-run

## patch 2
wget -O cuda_9.0.176.2_linux-run https://developer.nvidia.com/compute/cuda/9.0/Prod/patches/1/cuda_9.0.176.2_linux-run
chmod +x cuda_9.0.176.*
./cuda_9.0.176.2_linux-run

## patch 3
wget -O cuda_9.0.176.3_linux-run https://developer.nvidia.com/compute/cuda/9.0/Prod/patches/1/cuda_9.0.176.3_linux-run
chmod +x cuda_9.0.176.*
./cuda_9.0.176.3_linux-run

## patch 4
wget -O cuda_9.0.176.4_linux-run https://developer.nvidia.com/compute/cuda/9.0/Prod/patches/1/cuda_9.0.176.4_linux-run
chmod +x cuda_9.0.176.*
./cuda_9.0.176.4_linux-run

# Once cuda-9.0 is installed, proceed to install cudnn. This step has to be done manually. 
# Go to https://developer.nvidia.com/rdp/cudnn-download to download the latest cudnn for linux version in a new directory /share/apps/configurepo/cudnn-download. Need to register manually before downloading.

mkdir cudnn-download
cd cudnn-download
### download cudnn-10.0-linux-x64-v7.3.1.20.solitairetheme8 to here
tar -zxf cudnn-10.0-linux-x64-v7.3.1.20.solitairetheme8 ### or any newer version
cd cuda
cp include/cudnn.h /usr/local/cuda/include/
cp lib64/libcudnn* /usr/local/cuda/lib64/.
chmod a+x /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
pip install --upgrade pip
pip install tensorflow-gpu
pip install ipython

# Check Tensorflow with ipython
ipython

## In [1]: import tensorflow as tf
## In [2]: sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
## check nvidia-smi to see GPU status. 

## If the tensorflow test fails to run, try to link /usr/local/cuda to /usr/local/cuda-9.0 in your ~/.bashrc.