Making your Ubuntu deep learning ready
Installing Nvidia driver, Cuda, cuDNN, TensorFlow, Keras & PyTorch in Ubuntu
I have stumbled upon many guides to install deep learning libraries, Cuda, cuDNN & nvidia-driver. But I have never been successful in one go following only one guide. So, I decided to write this article. First, this guide is to install deep learning libraries ( TensorFlow, Keras, PyTorch) with GPU support. CPU support is pretty straight-forward, so I won’t be talking about that.
You need a
NVIDIA® GPU card with CUDA® Compute Capability 3.5 or higher.
You can check whether your GPU is compatible or not by visiting this link.
Installing Nvidia driver
Run these following commands. It adds the necessary repository to download the Nvidia driver. It will
sudo add-apt-repository ppa:graphic-drivers/ppasudo apt updateubuntu-drivers devices | grep nvidia
It will show you the driver versions compatible with your GPU card. I have an RTX 2060 super GPU card. It shows
driver : nvidia-430 — third-party free recommendedIt may also show multiple entries depending on the GPU card you are using. Example
driver : nvidia-340 — distro non-free
driver : nvidia-304 — distro non-free
driver : nvidia-384 — distro non-free recommendedInstall the suitable version. Your driver will decide the minimum version of CUDA you are going to install. You can find the table here.
sudo apt install nvidia-xxxRepace xxx with your desired version. Reboot and check the installation
sudo reboot
lsmod|grep nvidia
nvidia-smiInstalling CUDA and cuDNN
Download the CUDA installer from here. I prefer installing from the runfile. Install and then add the paths in PATH and LD_LIBRARY_PATH variable. Replace the path of Cuda (in my case /usr/local/cuda-10.1/ ) with your path.
./cuda_10.1.105_418.39_linux.runecho “export LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64:$LD_LIBRARY_PATH”>> ~/.bashrcecho “export PATH=/usr/local/cuda-10.1/bin:$PATH”>> ~/.bashrc
Visit this table get the tested build versions. Check the version of cuDNN you need to install. Download the cuDNN from here. I prefer the zipped version.
After downloading unzip and copy the files to CUDA directory.
tar -xzvf cudnn-x.x-linux-x64-v8.x.x.x.tgz
sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
Install Tensorflow, Keras, Pytorch
Install TensorFlow and Keras using
pip install tensorflow-gpu==2.2.0 kerasTo install PyTorch with GPU support visit this link. Select Version, OS, Language, package installer, CUDA version and then follow the highlighted portion of the following image to install.
There you have it. Now verify your installation using these python scripts.
Tensorflow code
from tensorflow.python.client import device_lib
def get_available_gpus():
local_device_protos = device_lib.list_local_devices()
return [x.physical_device_desc for x in local_device_protos if x.device_type == 'GPU']
get_available_gpus()
Pytorch code
import torchdef get_available_gpus():
return [ torch.cuda.get_device_properties(i) for i in range(torch.cuda.device_count())]
get_available_gpus()
There you have it.
All the libraries are installed. Now turn your CREATIVE mode on.
