Hello, I really need guidance about the situation I faced.
Let me give details about my working environment:

Working in Google Colab
Pytorch version is 0.4.1.post2
GPU: Tesla k80, Driver Version: 460.32.03, CUDA Version: 11.2, Compute Cap: 3.7

I have a network written in PyTorch 0.4 which is an old version. The network uses Fast RCNN as a backbone and there are some codes written in Cuda as an extension to the python script for making non-max suppression and ROI align faster. Because of these codes, I cannot easily switch to the latest version of PyTorch.  I tried and I got the error of “ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.” So I installed PyTorch 0.4 and training starts without any error until it comes to codes that PyTorch utilizes the GPU. When the code tries to utilize GPU it hits “cudaCheckError() failed : no kernel image is available for execution on the device”  error. I really looked at similar questions and I inferred that PyTorch 0.4 is a very old version and I need to build it from source to run with CUDA 11. Moreover, Cuda codes are previously compiled with these options, “-x cu -Xcompiler -fPIC -arch=sm_60”.
At this point, I confused. I am not sure sm_60 is a right architecture for Tesla K80. Can this be the cause of the problem? Will it be enough to only compile cuda codes with proper options? Or if I need to build pytorch 0.4 from sources, how do I build it with CUDA 11 support? And is it possible to do that in Google Colab?
I really need someone to make my mind clear about this problem because I dig the web too much and all answers I found said different things.
Thank you very much in advance for any help!