Till Apr26th, 2022, CUDA has updated to version 11.6, which can be installed by Nvidia Instruction:
wget https://developer.download.nvidia.com/compute/cuda/11.6.2/local_installers/cuda_11.6.2_510.47.03_linux.run
sudo sh cuda_11.6.2_510.47.03_linux.run
I guess the version of cudatoolkit will also be 11.6
However, there is no version of pytorch that matches CUDA11.6.
On the website of pytorch, the newest CUDA version is 11.3, pytorch version will be 1.11.0(stable)
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
So if I used CUDA11.6 and pytorch1.11.0 with cudatoolkit=11.3, will it perform normally?
and if there is any difference between Nvidia Instruction and conda method below?
conda install cuda -c nvidia
Best regards!
It should be fine. Otherwise, I saw here that you can build it from the source (I have python=3.8.13) build instructions
pip install torch --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu116
Related
I Try to install Pytorch on my Windows 10 system.
I wanna Use a anaconda env.
i followed the instruction 'https://pytorch.org/' stable 1.12.1 && Conda && Python && cuda 11.6
(conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge)
Before I installed conda 11.6, when i enter nvcc --version in the console i get the output :
NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Tue_Mar__8_18:36:24_Pacific_Standard_Time_2022
Cuda compilation tools, release 11.6, V11.6.124
Build cuda_11.6.r11.6/compiler.31057947_0
I also installed conda forge following this instruction https://conda-forge.org/docs/user/introduction.html
but now, if i try to run print(torch.cuda.is_available()) 'false' is outprinted
If i run conda list i get this (just some):
pytorch 1.10.2 py3.9_cpu_0 pytorch
pytorch 1.10.2 py3.9_cpu_0 pytorch
torchaudio 0.10.2 py39_cpu [cpuonly] pytorch
torchvision 0.11.3 py39_cpu [cpuonly] pytorch
My GPU is an RTX 2070 Super. Can anyone help me?
In my case i try to change the environment so i create new environment using conda then i download pytorch again from pytorch.org the compatible version for my GPU and then i tap the cmd of training and it works. hope it helps you
I really need help setting up CUDA for development with Pytorch. I have a Nvidia graphics card and am using Python 3.8. To install pytorch with the correct CUDA integration I ran conda install pytorch torchvision cudatoolkit=10.1 -c python. The problem is that torch.cuda.is_available() always returns False.
Can anyone help me here?
I the following are my versionings:
nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243
nvidia-smi
NVIDIA-SMI 510.47.03 Driver Version: 510.47.03 CUDA Version: 11.6
As in the PyTorch website, install with conda install pytorch torchvision cudatoolkit=11.3 -c pytorch or conda install pytorch torchvision cudatoolkit=11.6 -c pytorch
How could I update PyTorch from 1.4 -> 1.5 using Anaconda either through terminal or navigator?
Updating Anaconda with conda update --all updated some of the packages, not all, PyTorch included.
Initially, I installed PyTorch by running conda install -c pytorch pytorch
From PyTorch Github page there's the command
conda install -c pytorch magma-cuda90 # or [magma-cuda92 | magma-cuda100 | magma-cuda101 ] depending on your cuda version
but I wonder if there's going to be any kind of conflict between the already installed version and this one.
Thanks
PyTorch latest stable release (as of April 06, 2020) is still 1.4, as you can see here.
Therefore, if you want to install the nightly build (which is on track to the 1.5) using conda, you can follow the official instructions:
Cuda 10.1:
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch-nightly -c defaults -c conda-forge
Cuda 9.2:
conda install pytorch torchvision cudatoolkit=9.2 -c pytorch-nightly -c defaults -c conda-forge -c numba/label/dev
or the CPU-only version:
conda install pytorch torchvision cpuonly -c pytorch-nightly -c defaults -c conda-forge
Or, you can just wait 1.5 to be an stable release (currently, we are in the release candidate 2) and update the pytorch package as you'd have done otherwise.
Be aware that:
PyTorch 1.4 is the last release that supports Python 2
So, if you're moving to PyTorch 1.5, say goodbye to Python 2 (Yay!!).
I have a conda environment on my Ubuntu 16.04 system.
When I install Pytorch using:
conda install pytorch
and I try and run the script I need, I get the error message:
raise AssertionError("Torch not compiled with CUDA enabled")
From looking at forums, I see that this is because I have installed Pytorch without CUDA support.
I then tried:
conda install -c pytorch torchvision cudatoolkit=10.1 pytorch
but now I get the error:
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
File "/home/username/miniconda3/envs/super_resolution/lib/python3.6/site-packages/torch/__init__.py", line 81, in <module>
from torch._C import *
ImportError: /lib64/libc.so.6: version `GLIBC_2.14' not found
So it seems that these two installs are installing different versions of Pytorch(?). The first one that seemed to work was Pytorch 1.3.1.
My question: How do I install Pytorch with CUDA enabled, but ensure it is version 1.3.1 so that it works with my system?
Given that your system is running Ubuntu 16.04, it comes with glibc installed. You can check your version by typing ldd --version.
Keep in mind that PyTorch is compiled on CentOS which runs glibc version 2.17.
Then check the CUDA version installed on your system nvcc --version
Then install PyTorch as follows e.g. if your cuda version is 9.2:
conda install pytorch torchvision cudatoolkit=9.2 -c pytorch
If you get the glibc version error, try installing an earlier version of PyTorch.
If neither of the above options work, then try installing PyTorch from sources.
If you would like to set a specific PyTorch version to install, please set it as <version_nr> in the below command:
conda install pytorch=<version_nr> torchvision cudatoolkit=9.2 -c pytorch
For CUDA 10.1:
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
For CUDA 9.2:
conda install pytorch torchvision cudatoolkit=9.2 -c pytorch
For no CUDA:
conda install pytorch torchvision cpuonly -c pytorch
Not sure whether you have solved your problem or not, but I have this exact same problem before because I was trying to install pytorch on a cluster and I don't have root access. You need to download glibc into your directoryand set the environmental variable LD_LIBRARY_PATH to your local glibc https://stackoverflow.com/a/48650638/5662642.
To install glibc locally, I will point you to this thread that I read to solve my problem
https://stackoverflow.com/a/38317265/5662642 (instead of setting --prefix=/opt/glibc-2.14 when installing, you might want to set it to other directory that you have access to). Hope it works for you
Related to this post
I have installed TensorFlow (GPU) with anaconda using the installation instructions in here
conda create -n tf-gpu tensorflow-gpu
conda activate tf-gpu
But I realized Anaconda is installing library cudnn version 7.6.5
According to TensorFlow page
it should not be installing cudnn 7.4?
This can cause any problem?