Arda_Tumay:

Moreover, there should be some misleading information in Google Colab about Tesla K80. Below picture was the output of the command of !nvidia-smi


The shown CUDA version would either refer to the locally installed CUDA toolkit (if available) or the CUDA toolkit release version coming with the driver, so not necessarily the one used in PyTorch (the PyTorch binaries ship with their own CUDA runtime).



 Arda_Tumay:

Finally, I want to ask two more things about the installation from source process. If I want to build PyTorch from the source, how to compile it with a specific CUDA version? Or is it automatically built with the CUDA installed in the machine?


You can specify the location to the locally installed CUDA toolkit via:
CUDA_HOME=/usr/local/cuda python setup.py install

If you don’t set CUDA_HOME then the default location(s) will be searched.



 Arda_Tumay:

And is there any compatibility table that shows PyTorch versions and their compatible CUDA versions? I am asking this because I have RTX 2080 Super card in my local machine and I may have to run PyTorch 0.4 on my local machine in near future. RTX2080 super has compute capability of 7.5.


Not that I’m aware of, as the general advice is to update to the latest release, which is compatible with all CUDA versions >=10.2, which also includes your RTX2080 Super.
I would thus highly recommend not to use PyTorch 0.4 as it was released in ~April 2018.