Hi Peter and Pu!
Thanks for your help getting pytorch working with my old cuda
compute capability 3.0 gpu.  Thanks also to Andrei for his
post (in the thread linked to below) for the observation
that helped get me past my sticking point.
I will select Peter’s post linking to the legacy builds as the
solution although the whole discussion has been helpful.
The solution (for me) is to use a work-around for the
“cuda runtime error (30)” issue – namely don’t call
torch.cuda.is_available().
I was led to this by this post in the thread linked to by Peter:

  
    
    
    [resolved] Cuda Runtime Error(30) 
  
  
    This always works for me (Win10, Cuda 10.1, Python 3.7.2, PyTorch 1.0.1, NVIDIA GTX 1050 Ti): 
import torch
torch.cuda.current_device()

but this always fails for me: 
import torch
torch.cuda.is_available()
torch.cuda.current_device()  # fails here

@Mohamed_Ghadban, how can I access the NVIDIA Nsight Options? Thanks in advance 
  


I’ve posted some observations about “cuda runtime error (30)” here:

  
    
    
    Some observations on "cuda runtime error (30)" 
  
  
    Hello Forum! 
I have some information about the behavior of “cuda runtime 
error (30)” (probably somewhat specific to my particular 
configuration). 
This is a follow-on to a number of threads about “error 30,” 
and, in particular, to this post: 


Clued in by Andrei’s observation that torch.cuda.is_available() 
“breaks” cuda, I find (for me) that if torch.cuda.is_available() 
is the first cuda call, subsequent cuda calls will throw “error 30” 
unless the first subsequent call is called promptly…
  


Here is a script showing some simple gpu-tensor manipulations:
import torch
print (torch.__version__)
torch.cuda.get_device_capability (0)
torch.cuda.get_device_name (0)
ct = torch.cuda.FloatTensor([[3.3, 4.4, 5.5], [6.6, 7.7, 8.8]])
ct
ct + 0.01 * ct
quit()

And here is the output:
Python 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print (torch.__version__)
0.3.0b0+591e73e
>>> torch.cuda.get_device_capability (0)
(3, 0)
>>> torch.cuda.get_device_name (0)
'Quadro K1100M'
>>> ct = torch.cuda.FloatTensor([[3.3, 4.4, 5.5], [6.6, 7.7, 8.8]])
>>> ct

 3.3000  4.4000  5.5000
 6.6000  7.7000  8.8000
[torch.cuda.FloatTensor of size 2x3 (GPU 0)]

>>> ct + 0.01 * ct

 3.3330  4.4440  5.5550
 6.6660  7.7770  8.8880
[torch.cuda.FloatTensor of size 2x3 (GPU 0)]

>>> quit()

I’ll follow up if I have any issues actually running models on
this gpu.
Thanks again.
K. Frank