Run the nvidia-smi command.

Get the name/model of your NVidia card, then find it on this page: https://developer.nvidia.com/cuda-gpus

So below, you can see my GeForce GTX 950 has a computer power of 5.0:

The reason for checking this was from a blog on Medium regarding TensorFlow. It said:
Check for compatibility of your graphics card. The latest environment, called “CUDA Toolkit 9”, requires a compute capability of 3 or higher.

PyTorch is a machine learning package for Python. This code sample will test if it access to your Graphical Processing Unit (GPU) to use “CUDA

from __future__ import print_function
import torch

x = torch.rand(5, 3)
print(x)

if not torch.cuda.is_available():
   print ("Cuda is available")
   device_id = torch.cuda.current_device()
   gpu_properties = torch.cuda.get_device_properties(device_id)
   print("Found %d GPUs available. Using GPU %d (%s) of compute capability %d.%d with "
          "%.1fGb total memory.\n" % 
          (torch.cuda.device_count(),
          device_id,
          gpu_properties.name,
          gpu_properties.major,
          gpu_properties.minor,
          gpu_properties.total_memory / 1e9))
else:    
   print ("Cuda is not available")

CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs.

In GPU-accelerated applications, the sequential part of the workload runs on the CPU – which is optimized for single-threaded performance – while the compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers program in popular languages such as C, C++, Fortran, Python and MATLAB and express parallelism through extensions in the form of a few basic keywords.

C:\Program Files\NVIDIA Corporation\NVSMI>nvidia-smi

Sun Nov 24 13:35:47 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 441.22 Driver Version: 441.22 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 950M WDDM | 00000000:01:00.0 Off | N/A |
| N/A 60C P0 N/A / N/A | 141MiB / 4096MiB | 0% Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 2664 C+G ...iles (x86)\Opera\64.0.3417.92\opera.exe N/A |
| 0 10684 C+G ...hell.Experiences.TextInput.InputApp.exe N/A |
| 0 14500 C+G ...osoft.LockApp_cw5n1h2txyewy\LockApp.exe N/A |
| 0 15348 C+G ...iles\TechSmith\Snagit 2019\Snagit32.exe N/A |
| 0 15408 C+G ...vernote\Evernote\EvernoteSubprocess.exe N/A |
| 0 16448 C+G ...\TechSmith\Snagit 2019\SnagitEditor.exe N/A |
+-----------------------------------------------------------------------------+

Some machine learning tools that require GPU require a certain level of capability:
For more info, see https://www.tenforums.com/tutorials/136634-determine-nvidia-graphics-display-driver-version-installed-windows.html