CUDA is installed, but PyTorch v1.13 on Windows 10 not working. PyTorch not using GPU; how to fix PyTorch out of sync with CUDA 11.x drivers? - cuda

Question

How can I find where CUDA 11.x for PyTorch-GPU 1.13 get installed on Windows 10 on my computer?
What I tried:
I installed the NVIDIA CUDA drivers and toolkit for Windows from the NVIDIA website. I can verify this by typing: !nvidia-smi in Jupyter Lab, which gives me the following output. This indicates that the CUDA tools are installed, but not being used by my PyTorch package. I need to find out what version of CUDA drivers are installed so I can install the correct PyTorch-GPU package.
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 513.63 Driver Version: 513.63 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Quadro P2000 WDDM | 00000000:01:00.0 Off | N/A |
| N/A 46C P8 N/A / N/A | 0MiB / 4096MiB | 1% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
I find many Ubuntu questions and answers for locating CUDA to add it to my PATH, but nothing specific for Windows 10.
For example:
Pytorch CUDA installation fails,
Pytorch CUDA installation using conda,
pytorch-says-that-cuda-is-not-available
What are the equivalent Python commands on Windows 10 to locate the CUDA 11.x toolkits and driver version that my PyTorch-GPU package must use? And then how to fix the problem if PyTorch is out of sync?

Answer 1

I am answering my own question here...
PyTorch-GPU must be compiled against specific CUDA binary drivers.
I finally found this hint Why torch.cuda.is_available() returns False even after installing pytorch with cuda? which identifies the issue.
import torch
torch.zeros(1).cuda()
The return value clearly identifies the problem.
AssertionError Traceback (most recent call last)
Cell In [222], line 2
1 import torch
----> 2 torch.zeros(1).cuda()
File C:\ProgramData\Anaconda3\envs\tf210_gpu\lib\site-packages\torch\cuda\__init__.py:221, in _lazy_init()
217 raise RuntimeError(
218 "Cannot re-initialize CUDA in forked subprocess. To use CUDA with "
219 "multiprocessing, you must use the 'spawn' start method")
220 if not hasattr(torch._C, '_cuda_getDeviceCount'):
--> 221 raise AssertionError("Torch not compiled with CUDA enabled")
222 if _cudart is None:
223 raise AssertionError(
224 "libcudart functions unavailable. It looks like you have a broken build?")
AssertionError: Torch not compiled with CUDA enabled
The problem is: "Torch not compiled with CUDA enabled"
Now I have to see if I can just re-install PyTorch-GPU to replace the current PyTorch-CPU version with one that is compiled against my CUDA CUDA-GPU v11.6 driver, without rebuilding the entire conda environment. I would rather not rebuild the conda environment from scratch unless it is really necessary.