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!!
1 Like
Hi,
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
It seems conda is trying to download the old 0.4.0 release, so you might need to update conda to be able to use the current nightly.
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?
PyTorch 1.8.1 wasn’t built with cudatoolkit=11.0 (the last PyTorch release with this CUDA runtime was 1.7.1), so you could install the release with cudatoolkit 11.1 or 10.2 as described in the install instructions.
1 Like
I am getting the same error for some Python/PyTorch combinations, for example, py3.9 and pt1.7
I’m unsure what “the same” error is referring to, but in case you are seeing @geoHeil’s error, note that 1.8.1 wasn’t built with cudatoolkit=11.0 as already mentioned and you would thus have to use 11.1 or 10.2.
@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
Copyright (c) 2005-2017 NVIDIA Corporation
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
Copyright (C) 2017 Free Software Foundation, Inc.
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
Please uninstall this.
As @Tony-Y already pointed out, you have installed the CPU-only version of the PyTorch binary, so CUDA won’t be available. However, your K1100M has a compute capability of 3.0, which is not supported in the current binaries anymore.
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.
Update: pip solves the problem.
pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
5 Likes
running nvidia-smi shows that the driver for my GeForce RTX 2080 is CUDA 11.6
is there any way to enable CUDA with PyTorch on my machine?