Why 'Torch.Cuda.Is_Available()' Returns False Even After Installing Pytorch with Cuda

Why `torch.cuda.is_available()` returns False even after installing pytorch with cuda?

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:

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

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.

1. How to check if your GPU/graphics card supports a particular CUDA version

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

Locate your graphics card model in the big table and take note of the compute capability version. For example, the GeForce 820M compute capability is 2.1.
In the bullet list preceding the table check to see if the required CUDA version is supported by the compute capability of your graphics card. For example, CUDA 9.2 is not supported for compute compatibility 2.1.

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.

2. How to check if your GPU/graphics driver supports a particular CUDA version

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 3 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:

On Windows

Determine your current graphics driver version (Source https://www.nvidia.com/en-gb/drivers/drivers-faq/)

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.

Visit the CUDA release notes and scroll down to Table 3. Use this table to verify your graphics driver is new enough to support the required version of CUDA.

On Linux/OS X

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 3 on the CUDA release notes page.

3. How to check if a particular version of PyTorch is compatible with your GPU/graphics card compute capability

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.

4. Conclusion

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

Compile PyTorch from source with support for your compute capability (see here)
Install PyTorch without CUDA support (CPU-only)
Install an older version of the PyTorch binaries that support your compute capability (not recommended as PyTorch 0.3.1 is very outdated at this point). AFAIK compute capability older than 3.X has never been supported in the pre-built binaries
Upgrade your graphics card

If your graphics card doesn't support the required version of CUDA (section 1) then your options are

Install PyTorch without CUDA support (CPU-only)
Install an older version of PyTorch that supports a CUDA version supported by your graphics card (still may require compiling from source if the binaries don't support your compute capability)
Upgrade your graphics card

torch.cuda.is_available() returns False why?

You are using a PyTorch version compiled for CPU, you should install the appropriate version instead:

Using conda:

conda install pytorch torchvision cudatoolkit=11.1 -c pytorch -c conda-forge

Using pip:

python -m pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html

Pytorch says that CUDA is not available (on Ubuntu)

PyTorch doesn't use the system's CUDA library. When you install PyTorch using the precompiled binaries using either pip or conda it is shipped with a copy of the specified version of the CUDA library which is installed locally. In fact, you don't even need to install CUDA on your system to use PyTorch with CUDA support.

There are two scenarios which could have caused your issue.

You installed the CPU only version of PyTorch. In this case PyTorch wasn't compiled with CUDA support so it didn't support CUDA.
You installed the CUDA 10.2 version of PyTorch. In this case the problem is that your graphics card currently uses the 418.87 drivers, which only support up to CUDA 10.1. The two potential fixes in this case would be to either install updated drivers (version >= 440.33 according to Table 2) or to install a version of PyTorch compiled against CUDA 10.1.

To determine the appropriate command to use when installing PyTorch you can use the handy widget in the "Install PyTorch" section at pytorch.org. Just select the appropriate operating system, package manager, and CUDA version then run the recommended command.

In your case one solution was to use

conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

which explicitly specifies to conda that you want to install the version of PyTorch compiled against CUDA 10.1.

For more information about PyTorch CUDA compatibility with respect drivers and hardware see this answer.

Edit After you added the output of collect_env we can see that the problem was that you had the CUDA 10.2 version of PyTorch installed. Based on that an alternative solution would have been to update the graphics driver as elaborated in item 2 and the linked answer.

I have a GPU and CUDA installed in Windows 10 but Pytorch's torch.cuda.is_available() returns false; how can I correct this?

I also had the same issue.
And running this => a=torch.cuda.FloatTensor(), gave the assertion error AssertionError: Torch not compiled with CUDA enabled . ...which kind of cleared that i was running pytorch without cuda.

Steps:

Make sure you have un-installed Pytorch by invoking the following command:
pip uninstall torch
Go to https://pytorch.org/get-started/locally/ and select your system configurations(as shown in the figure).
Copy the exact command from the Run this command dialog and run it on your terminal.

Sample Image

Why 'Torch.Cuda.Is_Available()' Returns False Even After Installing Pytorch with Cuda