This is going to be a tutorial on how to install tensorflow 1.12 GPU version. We will also be installing CUDA 10.0 and cuDNN 7.3.1 along with the GPU version of tensorflow 1.12. At the time of writing this blog post, the latest version of tensorflow is 1.12. This tutorial is for building tensorflow from source. If you want to use the official pre-built pip package instead, I recommend another post, How to install Tensorflow using official pip package.

Tensorflow is an open source software library developed and used by Google that is fairly common among students, researchers, and developers for deep learning applications such as neural networks. It has both the CPU as well as GPU version available and although the CPU version works quite well, realistically, if you are going for deep learning, you will need GPU. In order to use the GPU version of TensorFlow, you will need an NVIDIA GPU with a compute capability > 3.0.

Using latest version of Tensorflow provides you latest features and optimization, using latest CUDA Toolkit provides you speed improvement with latest gpu support and using latest CUDNN greatly improves deep learing training time.

There must be 64-bit python installed tensorflow does not work on 32-bit python installation.

Step 1: Update and Upgrade your system:

sudo apt-get update

sudo apt-get upgrade

Step 2: Verify You Have a CUDA-Capable GPU:

lspci | grep -i nvidia

Note GPU model. eg. GeForce 840M

If you do not see any settings, update the PCI hardware database that Linux maintains by entering update-pciids (generally found in /sbin) at the command line and rerun the previous lspci command.

If your graphics card is from NVIDIA then goto http://developer.nvidia.com/cuda-gpus and verify if listed in CUDA enabled gpu list.

Note down its Compute Capability. eg. GeForce 840M 5.0

Step 3: Verify You Have a Supported Version of Linux:

To determine which distribution and release number you’re running, type the following at the command line:

uname -m && cat /etc/*release

The x86_64 line indicates you are running on a 64-bit system which is supported by cuda 9.1

Step 4: Install Dependencies:

Required to compile from source:

sudo apt-get install build-essential

sudo apt-get install cmake git unzip zip

sudo apt-get install python-dev python3-dev python-pip python3-pip

Step 5: Install linux kernel header:

Goto terminal and type:

uname -r

You can get like “4.15.0-36-generic”. Note down linux kernel version.

To install linux header supported by your linux kernel do following:

sudo apt-get install linux-headers-$(uname -r)

Step 6: Install NVIDIA CUDA 10.0:

Remove previous cuda installation:

sudo apt-get purge nvidia*

sudo apt-get autoremove

sudo apt-get autoclean

sudo rm -rf /usr/local/cuda*

Install cuda :

For Ubuntu 16.04 :

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

echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda.list

For Ubuntu 18.04 :

sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub

echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda.list

For Both Options:

sudo apt-get update

sudo apt-get -o Dpkg::Options::="--force-overwrite" install cuda-10-0 cuda-drivers

You can also install cuda toolkit following instructions from here and it is recommended to use deb[network].

Step 7: Reboot the system to load the NVIDIA drivers.

Step 8: Go to terminal and type:

echo 'export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}' >> ~/.bashrc

echo 'export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}' >> ~/.bashrc

source ~/.bashrc

sudo ldconfig

nvidia-smi

Check driver version probably Driver Version: 396.26

(not likely) If you got nvidia-smi is not found then you have unsupported linux kernel installed. Comment your linux kernel version noted in step 5.

You can check your cuda installation using following sample:

cuda-install-samples-10.0.sh ~

cd ~/NVIDIA_CUDA-10.0_Samples/5_Simulations/nbody

make

./nbody