My remote has cuda==11.0 and I want to install pytorch on it.
I use the command conda install pytorch cudatoolkit=11.0 -c pytorch -c conda-forge but in the installation list:
cudatoolkit conda-forge/linux-64::cudatoolkit-11.0.3-h15472ef_8
pytorch pytorch/linux-64::pytorch-1.10.0-py3.8_cpu_0
I found that pytorch is a cpu one.
Alternatively, I substitute 11.0 with 11.1 and the installation list appears to be:
cudatoolkit conda-forge/linux-64::cudatoolkit-11.1.1-h6406543_8
pytorch pytorch/linux-64::pytorch-1.10.0-py3.8_cuda11.1_cudnn8.0.5_0
where pytorch is a gpu one.
My question is: are the above two installation essentially same? If not, how can I install pytorch=1.10.0 with cuda==11.0?
I'd also like to know how does the cuda compatibility work? Is a cudatoolkit==11.1 compatible with programs compiled with cudatoolkit==11.0?
It all depends on whether the pytorch channel has built a version against the particular cudatoolkit version. I don't know a specific way to search this, but one can browse what builds are available on the pytorch channel. For PyTorch 1.10 on linux-64 platform it appears only CUDA versions 10.2, 11.1, and 11.3 are available.
As mentioned in the comments, one can try forcing a CUDA build of PyTorch with
conda create -n foo -c pytorch -c conda-forge cudatoolkit=11.0 'pytorch=*=*cuda*'
which would fail in this combination.
As for compatibility, no, the pytorch package builds lock in the minor version of cudatoolkit. For example,
Related
How to install PyTorch if my CUDA version is 11.2 and CudNN version is 8.1.0?
Offical documentation at https://pytorch.org/get-started/locally/ suggests only CUDA 10.2 and 11.3 versions.
Should I try to install it with v10.2 with next command?
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
Won`t it have any incompatible version problems in the future?
As far as I can tell[1], PyTorch does not provide precompiled libraries for CUDA 11.2. You would have to compile it yourself. For that, read this section of PyTorch Github's README.
[1]: I think so because on a similar question posed on PyTorch forums, one of the moderators said the same thing - https://discuss.pytorch.org/t/want-to-install-pytorch-for-custom-cuda-version-cuda-11-2/141159/2
edit: You can install PyTorch compiled for CUDA v11.1. In my case, it seems to work without any problems.
I have anaconda3 installed on my windows10 system and I want to install pytorch using anaconda.
I was looking at the official website
https://pytorch.org/get-started/locally/
where I see only the versions 10.2 and 11.1 for cuda are written in the selector. Now, I know I have a cuda-capable gpu in my computer, and the version is NVIDIA GeForce GTX 1660 Ti.
I was also checking nvidia website for finding out the compute capability of my gpu so that I can select the appropriate one in the installation. Here's the link:
https://developer.nvidia.com/cuda-gpus#compute
As you see, the compute capability of all the versions is at most 8.7 ( Mine was 7.5 )
Does this mean I can't use this command for installing pytorch?
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c conda-forge
Or can I use this command? and they won't cause a problem?
I have two version of CUDA installed on my Ubuntu 16.04 machine: 9.0 and 10.1.
They are located in /usr/local/cuda-9.0 and /usr/local/10.1 respectively.
If I install PyTorch 1.6.0 (which needs CUDA 10.1) via pip (pip install torch==1.6.0), it uses version 9.0 and thus detects no GPUs. I already changed my LD_LIBRARY_PATH to "/usr/local/cuda-10.1/lib64:/usr/local/cuda-10.1/cuda/extras/CUPTI/lib64" but PyTorch is still using CUDA 9.0.
How do I tell PyTorch to use CUDA 10.1?
Prebuilt wheels for torch built with different versions of CUDA are available at torch stable releases page. For example you can install torch v1.9.0 built with CUDA v11.1 like this:
pip install --upgrade torch==1.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
But not all the combinations are available.
I am running CNN on PyTorch. The torch.cuda.is_available() function returned false and no GPU is detected. However, I can run Keras model with GPU. Here is my system information:
OS: Ubuntu 18.04.3
Python 3.7.3 (Conda)
GPU: GTX1080Ti
Nvidia driver: 430.50
When I check nvidia-smi, the output said that the CUDA version is 10.1. However, the nvcc -V command tells me that it is CUDA 9.1.
I downloaded NVIDIA-Linux-x86_64-430.50.run from the official site and install it with command line. I installed CUDA 10.1 using these following command line recommended by the official site:
wget http://developer.download.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.243_418.87.00_linux.run
sudo sh cuda_10.1.243_418.87.00_linux.run
I installed PyTorch through pip install. What is wrong? Thanks in advance!
The default Pytorch 1.2 package depends on CUDA 10.0, but you have CUDA 9.1. The output of nvidia-smi just tells you the maximum CUDA version your GPU supports, nvcc gives the CUDA installed on your system. It seems that your installation of CUDA 10.1 was unsuccessful.
In addition to CUDA 10.0, Pytorch also supports CUDA 9.2 and I've found that the Pytorch package compiled for CUDA 10.0 also works with CUDA 10.1. So you can either upgrade your CUDA installation to 9.2 and install the Pytorch CUDA 9.2 package with
pip3 install torch==1.2.0+cu92 torchvision==0.4.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html
Or get a working installation of CUDA 10.1. There are detailed Linux instructions here. (Note that you may have to remove previous installations of CUDA before installing a new one.)
FYI, this answer is a hack which could mess up your conda env, but may work more easily than installing a fresh env. A consistency-checking tool would be really helpful because of all the people having exactly this problem. Matching anaconda's CUDA version with the system driver and the actual hardware and the other system environment settings is challenging to say the least and almost an art.
I found that Anaconda improperly guesses the CUDA version to use frequently. So I have found the best way to fix this is to surgically uninstall and reinstall just pytorch with pip:
pip uninstall torch
pip install torch
Note that pip calls pytorch torch while conda calls it pytorch.
However, I also found that pip sometimes refuses to reinstall torch because it didn't get rid of the anaconda site package files. If that is the case you can very carefully remove them manually as:
rm -fr $HOME/miniconda3/envs/<ENV>/lib/python3.9/site-packages/torch/
rm -fr $HOME/miniconda3/envs/<ENV>/lib/python3.9/site-packages/torch-*.dist-info/
where should be replaced with your environment name and miniconda might be anaconda or something else depending on your installation.
Be very careful not to delete anything other than the torch files or you may mess something else up. Then you would be best served by installing yet another fresh environment.
After this pip install torch should work and torch.cuda.is_available() should return True. Unless there is another problem... YMMV.
Note that I recommend using miniconda because the full anaconda comes overloaded with packages and I find it quickly gets clogged and broken.
I have tried several solutions which hinted at what to do when the CUDA GPU is available and CUDA is installed but the Torch.cuda.is_available() returns False. They did help but only temporarily, meaning torch.cuda-is_available() reported True but after some time, it switched back to False. I use CUDA 9.0.176 and GTX 1080. What should I do to get the permanent effect?
I tried the following methods:
https://forums.fast.ai/t/torch-cuda-is-available-returns-false/16721/5
https://github.com/pytorch/pytorch/issues/15612
Note: When torch.cuda.is_available() works fine but then at some point switches to False, then I have to restart the computer and then it works again (for some time).
The reason for torch.cuda.is_available() resulting False is the incompatibility between the versions of pytorch and cudatoolkit.
As on Jun-2022, the current version of pytorch is compatible with cudatoolkit=11.3 whereas the current cuda toolkit version = 11.7. Source
Solution:
Uninstall Pytorch for a fresh installation. You cannot install an old version on top of a new version without force installation (using pip install --upgrade --force-reinstall <package_name>.
Run conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch to install pytorch.
Install CUDA 11.3 version from https://developer.nvidia.com/cuda-11.3.0-download-archive.
You are good to go.
Also with torch.cuda.is_available () had false.
But when installing the Nvidia driver to the most updated version 436.48, True is displayed. I previously updated Pytorch to 1.2.0.
I have windows 10 and Anaconda.
Install CUDA 9.1 using apt-get, following the instructions in this link:
https://cryptoandcoffee.com/mining-gems/cuda-9-0-install-ubuntu-16-04-apt-get/
Installed PyTorch using pip:
pip install torchvision ( this will install both torch and torchvision )
Rebooted
Now try it:
~$ python -c 'import torch; print torch.cuda.is_available()'
I saw this issue as well. The reason was the CUDA version used by Pytorch being out of sync with the installed Nvidia driver. As in Joe's answer, the solution was updating the Nvidia drivers. Some other important background info to be aware of:
Each release of CUDA requires a minimum Nvidia driver version (see here for a compatibility table).
You can check your Nvidia driver version with nvidia-smi.
Pytorch comes pre-packaged with a version of CUDA that may be different from the version you installed on your computer.
The CUDA version that you installed manually is the one shows up when you run nvidia-smi. Even if your driver version is compatible with this CUDA version, it may be incompatible with the Pytorch CUDA version.
You can get the Pytorch CUDA version by printing the torch.version.cuda variable in ipython or in a Python program. This is the version that determines the needed Nvidia driver version.