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On a Windows 10 PC with an NVidia GeForce 820M I installed CUDA 9.2 and cudnn 7.1 successfully, and then installed PyTorch using the instructions at pytorch.org.
Specifically I used the command
pip install torch==1.4.0+cu92 torchvision==0.5.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html because i use pip and not Anaconda.
Yet I get the following
>>> import torch >>> torch.cuda.is_available() False Why is this happening?
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Your graphics card must support the required version of CUDA. Your graphics card driver must support the required version of CUDA. The PyTorch binaries must be built with support for the compute capability of your graphics card.
torch.cuda. is_available ()[source] Returns a bool indicating if CUDA is currently available. © Copyright 2022, PyTorch Contributors.
You can verify that you have a CUDA-capable GPU through the Display Adapters section in the Windows Device Manager. Here you will find the vendor name and model of your graphics card(s). If you have an NVIDIA card that is listed in http://developer.nvidia.com/cuda-gpus, that GPU is CUDA-capable.
Your graphics card does not support CUDA 9.0.
Since I've seen a lot of questions that refer to issues like this I'm writing a broad answer on how to check if your system is compatible with CUDA, specifically targeted at using PyTorch with CUDA support. Various circumstance-dependent options for resolving issues are described in the last section of this answer.
The system requirements to use PyTorch with CUDA are as follows:
Note: If you install pre-built binaries (using either pip or conda) then you do not need to install the CUDA toolkit or runtime on your system before installing PyTorch with CUDA support. This is because PyTorch, unless compiled from source, is always delivered with a copy of the CUDA library.
First, identify the model of your graphics card.
Before moving forward ensure that you've got an NVIDIA graphics card. AMD and Intel graphics cards do not support CUDA.
NVIDIA doesn't do a great job of providing CUDA compatibility information in a single location. The best resource is probably this section on the CUDA Wikipedia page. To determine which versions of CUDA are supported
If your card doesn't support the required CUDA version then see the options in section 4 of this answer.
Note: Compute capability refers to the computational features supported by your graphics card. Newer versions of the CUDA library rely on newer hardware features, which is why we need to determine the compute capability in order to determine the supported versions of CUDA.
The graphics driver is the software that allows your operating system to communicate with your graphics card. Since CUDA relies on low-level communication with the graphics card you need to have an up-to-date driver in order use the latest versions of CUDA.
First, make sure you have an NVIDIA graphics driver installed on your system. You can acquire the newest driver for your system from NVIDIA's website.
If you've installed the latest driver version then your graphics driver probably supports every CUDA version compatible with your graphics card (see section 1). To verify, you can check Table 2 in the CUDA release notes. In rare cases I've heard of the latest recommended graphics drivers not supporting the latest CUDA releases. You should be able to get around this by installing the CUDA toolkit for the required CUDA version and selecting the option to install compatible drivers, though this usually isn't required.
If you can't, or don't want to upgrade the graphics driver then you can check to see if your current driver supports the specific CUDA version as follows:
Right-click on your desktop and select NVIDIA Control Panel. From the NVIDIA Control Panel menu, select Help > System Information. The driver version is listed at the top of the Details window. For more advanced users, you can also get the driver version number from the Windows Device Manager. Right-click on your graphics device under display adapters and then select Properties. Select the Driver tab and read the Driver version. The last 5 digits are the NVIDIA driver version number.
Run the following command in a terminal window
nvidia-smi This should result in something like the following
Sat Apr 4 15:31:57 2020 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 435.21 Driver Version: 435.21 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 GeForce RTX 206... Off | 00000000:01:00.0 On | N/A | | 0% 35C P8 16W / 175W | 502MiB / 7974MiB | 1% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | 0 1138 G /usr/lib/xorg/Xorg 300MiB | | 0 2550 G /usr/bin/compiz 189MiB | | 0 5735 G /usr/lib/firefox/firefox 5MiB | | 0 7073 G /usr/lib/firefox/firefox 5MiB | +-----------------------------------------------------------------------------+ Driver Version: ###.## is your graphic driver version. In the example above the driver version is 435.21.
CUDA Version: ##.# is the latest version of CUDA supported by your graphics driver. In the example above the graphics driver supports CUDA 10.1 as well as all compatible CUDA versions before 10.1.
Note: The CUDA Version displayed in this table does not indicate that the CUDA toolkit or runtime are actually installed on your system. This just indicates the latest version of CUDA your graphics driver is compatible with.
To be extra sure that your driver supports the desired CUDA version you can visit Table 2 on the CUDA release notes page.
Even if your graphics card supports the required version of CUDA then it's possible that the pre-compiled PyTorch binaries were not compiled with support for your compute capability. For example, in PyTorch 0.3.1 support for compute capability <= 5.0 was dropped.
First, verify that your graphics card and driver both support the required CUDA version (see Sections 1 and 2 above), the information in this section assumes that this is the case.
The easiest way to check if PyTorch supports your compute capability is to install the desired version of PyTorch with CUDA support and run the following from a python interpreter
>>> import torch >>> torch.zeros(1).cuda() If you get an error message that reads
Found GPU0 XXXXX which is of cuda capability #.#. PyTorch no longer supports this GPU because it is too old. then that means PyTorch was not compiled with support for your compute capability. If this runs without issue then you should be good to go.
Update If you're installing an old version of PyTorch on a system with a newer GPU then it's possible that the old PyTorch release wasn't compiled with support for your compute capability. Assuming your GPU supports the version of CUDA used by PyTorch, then you should be able to rebuild PyTorch from source with the desired CUDA version or upgrade to a more recent version of PyTorch that was compiled with support for the newer compute capabilities.
If your graphics card and driver support the required version of CUDA (section 1 and 2) but the PyTorch binaries don't support your compute capability (section 3) then your options are
If your graphics card doesn't support the required version of CUDA (section 1) then your options are
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The same error can appear when the version of your Pytorch supports different CUDA. For example, my Pytorch version was with CUDA 8.0 support, but I had CUDA 9.0 installed. To fix that I had to upgrade my Pytorch to cu90 like this:
pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cu90/torch_nightly.html Reference: here
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