Python is an amazing programming language for creating a web applications, game applications, and website pages. Programmers like coding with Python because it is easy to use and execute. It is a worldwide accepted language that is still in demand. It is never considered an old language as it always gets updates with newer versions available for programmers as well as new functionalities to meet the need of current programming trends, and that’s the reason Python is popularly used for advanced programming. Beginners also learn Python to step in the shoe of professional programmers to create marvelous applications. Programmers also use CUDA for Python, which is known to provide uniform APIs and drivers for libraries and toolkits to access accelerated processing based on GPU. When you are working with CUDA Python, you may get the error “CUDA initialization: unexpected error from cudaGetDeviceCount()” in Python.
No matter how complex the error seems, there are always solutions to fix it, the same goes for this one. We will guide you on how you can simply tackle the error to peacefully continue the coding. First, let’s check out how the error pops up
How the error occurs
When you are working on a project using Python, you end up with the error warning. This is what you get
UserWarning: CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 804: forward compatibility was attempted on non supported HW (Triggered internally at /opt/conda/conda-bld/pytorch_1603729096996/work/c10/cuda/CUDAFunctions.cpp:100.)
How To Fix the Error “CUDA initialization: unexpected error from cudaGetDeviceCount()” in Python
We come up with two amazing options to help you solve the error successfully. Take a look at those
Option 1 – Restart
Whenever you get this error message, the first thing you need to try is to restart the application. The error pops up where there is a driver/ library of the NVIDIA version. If you have everything working file along with the Nvidia driver and the chipset GeForce GT 730M, then you just need to restart to make the functions work properly with no error showing up. If restarting doesn’t solve the issue, then use the below command
!pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
!pip3 install torch-scatter -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
!pip3 install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
!pip3 install torch-cluster -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
!pip3 install torch-geometric
This works to remove the error.
Option 2 – Execute the nvidia-smi in a different terminal
To avoid rebooting again, you need to execute nvidia-smi in a different terminal to check what you are getting in return. If you get the error ‘NML: Driver/library version mismatch’, then you need to do a simple procedure. For this, follow the below steps
- Type lsmod | grep nvidia in your terminal and run it
- Next, you need to unload the nvidia drivers’ module dependent. To do that, follow these commands
sudo rmmod nvidia_drm
sudo rmmod nvidia_modeset
sudo rmmod nvidia_uvm
- Now you need to unload sudo rmmod nvidia, which is the nvidia module
- Use lsmod | grep nvidia again. This time you get nothing in your terminal as an output
- Execute nvidia-smi to confirm you are getting the output you want.
It fixes the error.
We highlighted the options to help you fix the error “CUDA initialization: unexpected error from cudaGetDeviceCount()” in Python. You can try out the one that is ideal for your projec’s current status.
I hope it helps!