Hi Jack, in this case almost surely the problem is due to different environments.
In the command line (where it works) run
where python
You should get the location of the binary for the environment. For example, in my case (I regrettably use the base env for everything) I get:
C:\Users\valer\anaconda3\python.exe
I don’t use Visual Studio Code but you should find your answer here:
Using Python Environments in Visual Studio Code . Your VScode interpreter should be the same as the one outputted by the command line.
Let me know if it works

OK the issue has been resolved and I think the reason this all happened is because I have conda installed in different user’s directory. (This is due to the fact that my initial user name is in Non-English language so it was impossible to install conda so I created a dummy user to install conda)

So thank you ptrblack and Valerio_Biscione. I don’t think I would have solved issue anytime soon without your help!!

when I run “conda install torch”, I got this:
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.

PackagesNotFoundError: The following packages are not available from current channels:

torch

Current channels:

https://conda.anaconda.org/conda-forge/linux-64

https://conda.anaconda.org/conda-forge/noarch

https://conda.anaconda.org/r/linux-64

https://conda.anaconda.org/r/noarch

https://repo.anaconda.com/pkgs/main/linux-64

https://repo.anaconda.com/pkgs/main/noarch

https://repo.anaconda.com/pkgs/r/linux-64

https://repo.anaconda.com/pkgs/r/noarch

To search for alternate channels that may provide the conda package you’re
looking for, navigate to

https://anaconda.org
and use the search bar at the top of the page.
Thanks ptrblck.

Actually, I have tried both already. I got this
Collecting package metadata (repodata.json): done

Solving environment: done
Package Plan
environment location: /home/sol/anaconda3/envs/concat1
added / updated specs:

- pytorch
The following packages will be downloaded:
package                    |            build
---------------------------|-----------------
pytorch-0.4.0              |   py36hdf912b8_0       224.9 MB  defaults
------------------------------------------------------------
                                       Total:       224.9 MB
The following NEW packages will be INSTALLED:
intel-openmp       pkgs/main/linux-64::intel-openmp-2021.2.0-h06a4308_610

nccl               pkgs/main/linux-64::nccl-1.3.5-cuda9.0_0

pytorch            pkgs/main/linux-64::pytorch-0.4.0-py36hdf912b8_0
The following packages will be SUPERSEDED by a higher-priority channel:
mkl                conda-forge::mkl-2021.2.0-h726a3e6_389 → pkgs/main::mkl-2018.0.3-1
Proceed ([y]/n)? y
Downloading and Extracting Packages

pytorch-0.4.0        | 224.9 MB  | ##################################### | 100%

Preparing transaction: done

Verifying transaction: done

Executing transaction: done
But still can’t import torch. And got: Torch CUDA is not Available
              
I observe the following problem:
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia
pytorch                       1.8.1  py3.9_cuda11.1_cudnn8.0.5_0
conda install pytorch torchvision torchaudio cudatoolkit=11.0 -c pytorch -c nvidia
pytorch                       1.8.1  py3.9_cpu_0
can you help me figure out why the CPU only version is installed when using version 11.0 of the cudatoolkit?
              
@ptrblck

Hello ptrblck,
Do we need to explicitly install cudnn after running following command?
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c nvidia
Thanks ans regards,

sudhir
              
No, cudnn8.0.5 will be installed using your posted command and you can check it via:
torch.backends.cudnn.is_available()
torch.backends.cudnn.version()
              
@ptrblck -
I know this thread seems to have run on forever, and please forgive me for asking, but I’m seemingly having the same issue on my Ubuntu 18.04 machine.  I have followed what instructions I can above, but I’m also getting a CUDA Available = False result.  Below is the output of environment, can you please provide guidance?  thanks
(base) grbman@Zidane:~/GFPGAN$ nvidia-smi

Thu Jan  6 15:33:35 2022

±----------------------------------------------------------------------------+

| NVIDIA-SMI 418.226.00   Driver Version: 418.226.00   CUDA Version: 10.1     |

|-------------------------------±---------------------±---------------------+

| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |

| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |

|===============================+======================+======================|

|   0  Quadro K1100M       Off  | 00000000:01:00.0  On |                  N/A |

| N/A   45C    P0    N/A /  N/A |    410MiB /  1991MiB |      1%      Default |

±------------------------------±---------------------±---------------------+
±----------------------------------------------------------------------------+

| Processes:                                                       GPU Memory |

|  GPU       PID   Type   Process name                             Usage      |

|=============================================================================|

|    0      1117      G   /usr/lib/xorg/Xorg                            55MiB |

|    0      1233      G   /usr/bin/gnome-shell                          53MiB |

|    0      1533      G   /usr/lib/xorg/Xorg                           163MiB |

|    0      1661      G   /usr/bin/gnome-shell                         100MiB |

|    0      2713      G   …AAgAAAAAAAAACAAAAAAAAAA= --shared-files    31MiB |

±----------------------------------------------------------------------------+
(base) grbman@Zidane:~/GFPGAN$ nvcc -V

nvcc: NVIDIA (R) Cuda compiler driver

Built on Fri_Nov__3_21:07:56_CDT_2017

Cuda compilation tools, release 9.1, V9.1.85
(base) grbman@Zidane:~/GFPGAN$ gcc --version

gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0

This is free software; see the source for copying conditions.  There is NO

warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
(base) grbman@Zidane:~/Downloads$ python3 collect_env.py

Collecting environment information…

PyTorch version: 1.10.1

Is debug build: False

CUDA used to build PyTorch: Could not collect

ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.6 LTS (x86_64)

GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0

Clang version: Could not collect

CMake version: Could not collect

Libc version: glibc-2.27
Python version: 3.9.7 (default, Sep 16 2021, 13:09:58)  [GCC 7.5.0] (64-bit runtime)

Python platform: Linux-5.4.0-92-generic-x86_64-with-glibc2.27

Is CUDA available: False

CUDA runtime version: 9.1.85

GPU models and configuration: GPU 0: Quadro K1100M

Nvidia driver version: 418.226.00

cuDNN version: Could not collect

HIP runtime version: N/A

MIOpen runtime version: N/A
Versions of relevant libraries:

[pip3] mypy-extensions==0.4.3

[pip3] numpy==1.20.3

[pip3] numpydoc==1.1.0

[pip3] torch==1.10.1

[pip3] torchaudio==0.10.1

[pip3] torchvision==0.11.2

[conda] blas                      1.0                         mkl

[conda] cpuonly                   2.0                           0    pytorch

[conda] cudatoolkit               10.2.89              hfd86e86_1

[conda] mkl                       2021.4.0           h06a4308_640

[conda] mkl-service               2.4.0            py39h7f8727e_0

[conda] mkl_fft                   1.3.1            py39hd3c417c_0

[conda] mkl_random                1.2.2            py39h51133e4_0

[conda] mypy_extensions           0.4.3            py39h06a4308_0

[conda] numpy                     1.20.3           py39hf144106_0

[conda] numpy-base                1.20.3           py39h74d4b33_0

[conda] numpydoc                  1.1.0              pyhd3eb1b0_1

[conda] pytorch                   1.10.1              py3.9_cpu_0    pytorch

[conda] pytorch-mutex             1.0                         cpu    pytorch

[conda] torchaudio                0.10.1                 py39_cpu  [cpuonly]  pytorch

[conda] torchvision               0.11.2                 py39_cpu  [cpuonly]  pytorch
              
Yeah, so it looks like now I have a mis-match between what is available in the driver for my K1100M (Cuda 10.1) and the PyTorch build which is Cuda 10.2.
…guess we’re gonna have to upgrade a video card.  Thanks for all the help…I do appreciate the effort
              
Pardon me guys, may I ask what’s the command that allows me to download PyTorch with GPU connectivity. I’ve used the suggested ones from the here but it still provides me packages with cpuonly version.
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
The following packages will be downloaded:
    package                    |            build
    ---------------------------|-----------------
    pytorch-1.10.1             |      py3.9_cpu_0        86.2 MB  pytorch
    torchaudio-0.10.1          |         py39_cpu         4.3 MB  pytorch
    torchvision-0.11.2         |         py39_cpu         7.1 MB  pytorch
    ------------------------------------------------------------
                                           Total:        97.6 MB
The following NEW packages will be INSTALLED:
  pytorch            pytorch/linux-64::pytorch-1.10.1-py3.9_cpu_0
  torchaudio         pytorch/linux-64::torchaudio-0.10.1-py39_cpu
  torchvision        pytorch/linux-64::torchvision-0.11.2-py39_cpu
PS: I’m using RTX 3080 and requires sm_86 CUDA capability.