I’m unsure what you mean by CUDA and cudnn “modules”. A source build would require a local CUDA toolkit installation including the compiler (nvcc).
Yes, the latest CUDA and cudnn versions are compatible with the current PyTorch master branch, but note some issues we’ve found, such as:
  
      github.com/pytorch/pytorch
  
  
    
  
	  
  

  
    
      test_optim.py failures for CUDA 11.2 on CI 1
    

    
      
        opened 
        
          
        
        Feb 9, 2021
      
      


      
        
          
          janeyx99
        
      
    
  



  🐛 Bug
The following tests in test_optim.py fail in CI on CUDA 11.2 in linux and windows.
test_adadelta
test_adam
test_adamw
test_multi_tensor_optimizers
test_rmsprop
To Reproduce
Steps to reproduce the behavior:
Run...



    high priority
    module: ci
    module: cuda
    triage review
    triaged


  
  
    
    
  
  


While it seems to be a CUDA11.2 compiler issue, we’ve reproduced an illegal memory access internally caused in the multi_tensor_apply implementation, which is now visible by using more aggressive optimizations in the compiler.