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Step by step instructions for installing CUDA Toolkit 10.0 CentOS 7 Server machine for running Deep Learning projects
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#!/bin/bash | |
## This gist contains step by step instructions to install cuda v10.1 and cudnn 7.6 in CentOS 7 | |
### steps #### | |
# verify the system has a cuda-capable gpu | |
# download and install the nvidia cuda toolkit and cudnn | |
# setup environmental variables | |
# verify the installation | |
### | |
### to verify your gpu is cuda enable check | |
lspci | grep -i nvidia | |
### gcc compiler is required for development using the cuda toolkit. to verify the version of gcc install enter | |
gcc --version | |
# First download the latest Nvidia CUDA from official repository | |
wget https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-repo-rhel7-10.0.130-1.x86_64.rpm | |
# install the packages | |
sudo rpm -i cuda-repo-rhel7-10.0.130-1.x86_64.rpm | |
# install cuda | |
sudo yum install cuda | |
# setup your paths | |
echo 'export PATH=/usr/local/cuda-10.0/bin:$PATH' >> ~/.bashrc | |
echo 'export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc | |
source ~/.bashrc | |
sudo ldconfig | |
# install cuDNN v7.5 | |
# in order to download cuDNN you have to be regeistered here https://developer.nvidia.com/developer-program/signup | |
# then download cuDNN v7.5 form https://developer.nvidia.com/cudnn | |
CUDNN_TAR_FILE="cudnn-10.0-linux-x64-v7.5.0.56" | |
wget https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v7.5.0.56/prod/10.0_20190219/cudnn-10.0-linux-x64-v7.5.0.56.tgz | |
tar -xzvf ${CUDNN_TAR_FILE} | |
# copy the following files into the cuda toolkit directory. | |
sudo cp -P cuda/include/cudnn.h /usr/local/cuda-10.0/include | |
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda-10.0/lib64/ | |
sudo chmod a+r /usr/local/cuda-10.0/lib64/libcudnn* | |
# Finally, to verify the installation, check | |
nvidia-smi | |
nvcc -V | |
# install Tensorflow (an open source machine learning framework) | |
# I choose version 1.13.1 because it is stable and compatible with CUDA 10.0 Toolkit and cuDNN 7.5 | |
sudo pip3 install --user tensorflow-gpu==1.13.1 |
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