First, i apologize for my poor English.
Recently, I bought RTX2060 for deep learning. I installed pytorch-gpu with conda by conda install pytorch torchvision cudatoolkit=10.1 -c pytorch. Of course, I setup NVIDIA Driver too.
But when i ran my pytorch code, it was so slow to train. So i checked task manger and it seems torch doesn’t using GPU at all!

캡처847×760 30.9 KB

Rather, as shown in picture, CPU was used highly more than GPU. It’s replying true for torch.cuda.is_available(), but overall training speed and task manager’s graph seems torch can’t utilize GPU well. Is there something I missed? Here are my  nvidia-smi.
Sun Mar 29 15:25:26 2020
±----------------------------------------------------------------------------+
| NVIDIA-SMI 431.91       Driver Version: 431.91       CUDA Version: 10.1     |
|-------------------------------±---------------------±---------------------+
| 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 RTX 2060   WDDM  | 00000000:01:00.0 Off |                  N/A |
| N/A   73C    P2    61W /  N/A |   2779MiB /  6144MiB |     46%      Default |
±------------------------------±---------------------±---------------------+
±----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      2984      C   …01\Anaconda3\envs\PyTorchGPU\python.exe N/A      |
|    0     10636      C   …01\Anaconda3\envs\PyTorchGPU\python.exe N/A      |
±----------------------------------------------------------------------------+