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When attempting to complete your task, you receive the following problem: “CUDA initialization: Unexpected error from cudaGetDeviceCount() in Python“. It is one popular error that every programmer makes. So, what creates it, and how can it be remedied? We’ll talk about it with you.
Why Does The Problem “CUDA initialization: Unexpected error from cudaGetDeviceCount() in Python” Happen?
You are running my program when you get this error.
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.)
Some Easy Tips For You To Solve The Error “CUDA initialization: Unexpected error from cudaGetDeviceCount() in Python”
Method 1: Attempt a restart
As a result of this issue, other users were receiving the same error message:
- Their configuration was hampered by a driver/library mismatch (rebooting seemed to fix the problem)
- Within a single Docker container, the installed drivers did not match the user-mode operator. And the forward compatibility was broken because of the use of non-server GPUs.
You attempted a restart. The issue was resolved. It was discovered that the problem was caused by a mismatch in the NVIDIA NVML Driver/library version. It will solve your issue.
When running CUDA 8.0 samples on Ubuntu 14, you received the same error (cudaGetDeviceCount 30). Everything was up to date, including the Nvidia driver (375) and the GeForce GT 730M chipset (capacity 3). After restarting xserver, it worked.
If restarting does not work, try the following commands:
!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
Method 2: Operate nvidia-smi in a distinct terminal
Excepting the above solution, there is anotther solution for you to solve the error “CUDA initialization: Unexpected error from cudaGetDeviceCount() in Python”. It is operating nvidia-smi in a different terminal. Let’s follow these steps to do that:
- First, let’s run
lsmod | grep nvidiain a terminal.
- The next step is unloading the module dependence on nvidia driver by the commands below:
sudo rmmod nvidia_drm sudo rmmod nvidia_modeset sudo rmmod nvidia_uvm
- Then, let’s the nvidia module need to be unloaded:
sudo rmmod nvidia.
- Now when you attempt
lsmod | grep nvidia, you will get nothing in the output of terminal.
- Run nvidia-smi to verify that you are getting the proper results, and then you are ready to go!
The solutions listed above are our most practical for those still perplexed by the issue: “CUDA initialization: Unexpected error from cudaGetDeviceCount() in Python“. If you need suggestions or have other Python questions, we have a thriving community where everyone is eager to help. Finally, we wish all of you a wonderful day full of new code solutions.
→ Resolve The Error: RuntimeError: cuDNN error: CUDNN_STATUS_NOT_INITIALIZED in Python
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