Full installation guide for TensorFlow, Keras, CUDA and cuDNN on Ubuntu 16.04 (64bit) with Python 3.5.
GPU driver installation
Check for system updates and install them.
$ sudo apt-get update
$ sudo apt-get upgrade
$ sudo apt-get dist-upgrade
Open configuration for grub.
$ sudo gedit /etc/default/grub
Find line GRUB_CMDLINE_LINUX_DEFAULT and change it:
GRUB_CMDLINE_LINUX_DEFAULT="nouveau.blacklist=1 quiet splash nomodeset"
Save and close the file, then update grub settings.
$ sudo update-grub2
We need to make sure there is no other installation of GPU driver and eventually remove it. Reboot your computer after this step.
$ sudo apt-get remove nvidia*
$ sudo apt-get autoremove
$ sudo reboot
Download from
NVIDIA webpage correct
driver for your card. Now make the .run file executable.
# File name can be different
$ cd Downloads
$ chmod +x NVIDIA-Linux-x86_64-375.26.run
Open file blacklist.conf
$ sudo gedit /etc/modprobe.d/blacklist.conf
Place these line at the end of the file, save and close.
blacklist vga16fb
blacklist nouveau
blacklist rivafb
blacklist nvidiafb
blacklist rivatv
blacklist lbm-nouveau
options nouveau modeset=0
alias nouveau off
alias lbm-nouveau off
Open putty terminal with Ctrl + Alt + F1 and stop running graphical
enviroment, so we can successfully install driver.
$ sudo service lightdm stop
$ cd Downloads
$ sudo ./chmod +x NVIDIA-Linux-x86_64-375.26.run
Open grub config again.
$ sudo nano /etc/default/grub
Find line GRUB_CMDLINE_LINUX_DEFAULT and edit it.
GRUB_CMDLINE_LINUX_DEFAULT="quiet splash nomodeset"
Finally, update driver and restart your computer.
$ sudo update-grub2
$ sudo reboot
You should now have working driver for your graphic card.
NVIDIA CUDA installation
Download installation file,
choose deb (local) version and install it.
$ cd Downloads
$ sudo dpkg -i cuda-repo-ubuntu1604-8-0-local_8.0.44-1_amd64.deb
$ sudo apt-get update
$ sudo apt-get install cuda
NVIDIA cuDNN installation
You need to register on NVIDIA website in order to download cuDNN. After your approval, download correct version of cuDNN (newest), which is compatible with your version of CUDA.
Unzip the archive and move files into the CUDA installation folder.
$ cd Downloads
$ tar xvzf cudnn-8.0-linux-x64-v5.1-ga.tgz
$ sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include
$ sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
$ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
It is useful to add paths into bash settings. Open bash config file.
$ gedit ~/.bash_profile
Put these lines at the end.
$ export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
$ export CUDA_HOME=/usr/local/cuda
TensorFlow installation
You need a Python installation before installing TensorFlow. If you do not have one yet, you can install Anaconda distribution, preferably with Python 3.5.
Open terminal and create new enviroment for TensorFlow installation.
$ conda create -n tensorflow python=3.5
$ source activate tensorflow
Set URL for TensorFlow to download. This URL can be different for other versions. If you are not on 64bit version of Ubuntu, check line for TensorFlow original installation beneath.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc1-cp35-cp35m-linux_x86_64.whl
And finally install TensorFlow.
$ pip3 install --ignore-installed --upgrade $TF_BINARY_URL
Keras installation
This step is optional. Read more at Keras.io
You can install Keras from PyPI, so all you need to do is switch to tensorflow enviroment and install it using pip.
$ source activate tensorflow
$ sudo pip install keras
Conclusion
You now have installation of TensorFlow and Keras support GPU. When you want to
use TensorFlow, type source activate tensorflow into terminal,
source deactivate to switch back to default enviroment.
If you need to install new packages into this enviroment, use conda install or
pip install. Remember that these packages will only be installed into
currently used enviroment.
One useful thing when you use your graphic card is nvidia-smi. It is a small
CLI program, that let you monitor GPU utilization. Type watch nvidia-smi into
terminal and it will by updated every second.