@ptrblck, thank you for the response. I remember I had installed PyTorch with conda. Around that time, I had done a pip install for a different version of torch. But ‘conda list torch’ gives me the current global version as 1.3.0.
Also, ‘conda list cuda’ returns this:
# packages in environment at C:\Users\hp\Anaconda3:
#
# Name                    Version                   Build  Channel
cuda100                   1.0                           0    pytorch
cudatoolkit               10.1.168                      0

I also recently installed CUDA 10.2, and cudnn with it globally.
I have used torch on my system for DL before. But I am confused now, as to whether all of the training in my DL projects was actually being done by CPU and not my GPU(I know it sounds really funny, but I seemed sure it was my GPU, because the training was quite quick). Is there any way I can check my GPU history?
When I went through my previous jupyter notebooks(not run from any virtual env), I found this:

pic1359×309 13.7 KB

And then I ran this code on my system yesterday:
import torch 
print(torch.__version__)

print(torch.cuda.current_device())
print(torch.cuda.device(0))
print(torch.cuda.device_count())
print(torch.cuda.get_device_name(0))
print(torch.cuda.is_available())
print(torch.cuda.current_device())

Output:
1.3.0
driver version : 10020
0
<torch.cuda.device object at 0x0000028603786FD0>
1
GeForce GTX 950M
True
0

This seems contradicting to me(how the jupyter notebook says that CUDA is not available, and how the cmd program says that CUDA is available).