Four steps to setup Pytorch for my laptop GPU NVIDIA GTX 960m (Asus VivoBook n552vw) in Ubuntu 16.4

In this post, I’m gonna describe the steps I used to make Pytorch use GPU on my laptop (It takes me about half a day to find the steps).

Generally, you should check compatibility of several things:

• Pytorch Version
• Nvidia Driver
• Cuda Version

First Step: Check compatibilities

Check Cuda and Nvidia GPU drivers compatibility at:

https://docs.nvidia.com/deploy/cuda-compatibility/index.html

Here is the important table:

CUDA Toolkit	Linux x86_64 Driver Version
CUDA 11.1 (11.1.0)	>= 450.80.02
CUDA 11.0 (11.0.3)	>= 450.36.06
CUDA 10.2 (10.2.89)	>= 440.33
CUDA 10.1 (10.1.105)	>= 418.39
CUDA 10.0 (10.0.130)	>= 410.48
CUDA 9.2 (9.2.88)	>= 396.26
CUDA 9.1 (9.1.85)	>= 390.46
CUDA 9.0 (9.0.76)	>= 384.81
CUDA 8.0 (8.0.61 GA2)	>= 375.26
CUDA 8.0 (8.0.44)	>= 367.48
CUDA 7.5 (7.5.16)	>= 352.31
CUDA 7.0 (7.0.28)	>= 346.46

Check Pytorch compatibility at:

https://pytorch.org/get-started/previous-versions/

Based on my system configuration I decide to install the following versions:

Cuda: 10.1

GPU Driver: 418.56

Pytorch: 1.6.0 / Torchvision: 0.7.0

Second Step: Install GPU Driver

Install appropriate version of your GPU using one of the following options:

1. Go to Software & Update -> Additional Drivers -> Using Nvidia binary driver – version X.X.
2. Or if you can’t see the first option run the following command: sudo apt install nvidia-418
3. Or alternatively you can download drivers from the Official Nvidia website.
4. reboot your system
5. Now run the command nvidia-smi. It must show something like the following:

Third Step: Install Cuda Toolkit

Download and install the proper Cuda Tool kit version from https://developer.nvidia.com/cuda-toolkit-archive and then reboot. Specifically I download Cuda 10.1 toolkit from https://developer.nvidia.com/cuda-10.1-download-archive-base?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1604&target_type=runfilelocal

and install the .run file by:

sudo sh cuda_10.1.105_418.39_linux.run

Final Step: Install Pytorch

Install the appropriate Pytorch version from:

https://pytorch.org/get-started/previous-versions/

I have installed using the following command:

pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html

Enjoy GPU Power!