NVIDIA RTX A1000 Laptop GPU cuda compatibility

Dear NVIDIA CUDA Developer Community,

I am writing to seek assistance regarding the compatibility of CUDA with my GPU. I have been experiencing challenges in finding a compatible CUDA version for my GPU model. I am using a [NVIDIA RTX A1000 Laptop GPU].
The installation process for both CUDA 11,10, 9 and 12 seemed to proceed without errors. However, upon running the command print(torch.cuda.is_available()) in Python, it consistently returns False , indicating that CUDA is not available on my system.

After further investigation, I suspect that I might not have correctly added the CUDA binaries and libraries to the system’s PATH. I would greatly appreciate guidance on the proper steps to ensure CUDA is properly added to the PATH environment variable.

I have already attempted the following steps:

  1. Located the CUDA installation directory, which is located at [insert CUDA installation path].
  2. Accessed the system’s environment variables settings and modified the “Path” variable to include the CUDA “bin” directory, [insert CUDA bin directory].

I am reaching out to the CUDA developer community with the hope that you can provide guidance, insights, or alternative suggestions to resolve this compatibility issue. I am eager to utilize CUDA for GPU computing and would greatly appreciate any assistance you can offer in identifying a compatible CUDA version for my GPU.

If there are any specific diagnostic steps, additional configurations, or troubleshooting recommendations that I should consider, please kindly provide them. Your expertise and support in resolving this matter would be invaluable.

Thank you for your time and consideration. I eagerly await your response and guidance.

Best regards,

Welcome @raounek.zeghdoud to the NVIDIA developer forums.

Great to hear that you are joining the wider CUDA development community!

How did you install CUDA? If you are planning to use it with Pytorch, it is highly recommended to NOT install CUDA yourself but rather follow the instructions on the official Pytorch pages. Fixing PATHs after the fact and trying to patch the setup, especially if you are not very familiar with Linux, will lead to trouble down the road.

But in general rest assured, the A1000 is compatible with all the latest CUDA versions.

For more details on CUDA setup, installation and usage, I can recommend our dedicated CUDA categories as well.


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Hi @MarkusHoHo,

Thank you for the warm welcome to the NVIDIA developer forums. I’m excited to be a part of the wider CUDA development community!

Regarding the installation of CUDA, I’ve tried two methods. First, I followed the instructions on the NVIDIA website to install CUDA. Additionally, I also used the command “pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117” to install PyTorch with CUDA support. I want to mention that I’m using Windows and not Linux.

I have taken the necessary steps to ensure that all the required paths are correctly set up. I understand the importance of a proper setup, especially when it comes to using CUDA with PyTorch.
It’s good to know that the A1000 GPU is compatible with the latest CUDA versions.
However, I am still encountering the same issue where CUDA is not available.

I would also like to mention that I am using a virtual machine, specifically an ncv3 instance. I have installed the appropriate driver from the Azure website, which indicates that CUDA 11 is the suitable version for it. I created a new environment and installed all the necessary dependencies, including CUDA 11. I have verified the paths, and when I run “!nvidia-smi,” I get the following output:

| NVIDIA-SMI 531.14                 Driver Version: 451.82       CUDA Version: 11.0     |
| GPU  Name                      TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf            Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|   0  Tesla V100-PCIE-16GB          TCC  | 00000001:00:00.0 Off |                    0 |
| N/A   29C    P0               25W / 250W| Not Supported        |      0%      Default |
|                                         |                      |                  N/A |
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|  No running processes found                                                           |

Additionally, when I run “!nvcc --version,” I get the following output:

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Thu_Jun_11_22:26:48_Pacific_Daylight_Time_2020
Cuda compilation tools, release 11.0, V11.0.194
Build cuda_11.0_bu.relgpu_drvr445TC445_37.28540450_0

However, when I run the following Python code:


I get the output:


I dant know what is the problem
If you have any further recommendations or details on CUDA setup, installation, or usage, I would greatly appreciate your guidance. Thank you once again for the warm welcome!

Just to quickly verify, you see the issue of torch.cuda.is_available() being False both on your Windows/A1000 setup and an Azure ncv3 setup with a Tesla V100?