PyTorch RuntimeError: CUDA error: invalid device function
PyTorch RuntimeError: CUDA error: invalid device function
Understanding PyTorch and Its Purpose
PyTorch is an open-source machine learning library developed by Facebook's AI Research lab. It is widely used for applications such as natural language processing and computer vision. PyTorch provides a flexible platform for deep learning research and development, offering dynamic computation graphs and seamless integration with Python.
Identifying the Symptom: RuntimeError
When working with PyTorch, you might encounter the error: RuntimeError: CUDA error: invalid device function. This error typically occurs when executing a PyTorch script that utilizes GPU acceleration. The script fails to run, and this error message is displayed, indicating an issue with the CUDA setup.
Explaining the Issue: CUDA Error
The error CUDA error: invalid device function suggests that the CUDA function being called is not supported by the GPU in use. This can happen if there is a mismatch between the CUDA version installed and the GPU's compute capability. Each GPU has a specific compute capability, and CUDA functions must be compiled to support that capability.
Understanding Compute Capability
Compute capability is a property of the GPU that indicates its features and supported operations. You can find the compute capability of your GPU on the NVIDIA CUDA GPUs page.
Steps to Fix the Issue
Step 1: Verify CUDA Version
First, ensure that the CUDA version installed on your system is compatible with your GPU. You can check the installed CUDA version by running:
nvcc --version
Compare this version with the supported versions for your GPU on the CUDA Toolkit Archive.
Step 2: Check PyTorch and CUDA Compatibility
Ensure that the PyTorch version you are using is compatible with the installed CUDA version. You can find compatibility information on the PyTorch Previous Versions page.
Step 3: Reinstall PyTorch with Correct CUDA Version
If there is a mismatch, reinstall PyTorch with the correct CUDA version. You can do this by specifying the desired CUDA version when installing PyTorch:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cuXX
Replace cuXX with the appropriate CUDA version, such as cu117 for CUDA 11.7.
Step 4: Recompile Custom CUDA Extensions
If you are using custom CUDA extensions, ensure they are compiled for the correct compute capability. You can specify the compute capability during compilation:
TORCH_CUDA_ARCH_LIST="compute_capability" python setup.py install
Replace compute_capability with the appropriate value for your GPU, such as 7.5 for a Tesla T4.
Conclusion
By following these steps, you should be able to resolve the RuntimeError: CUDA error: invalid device function in PyTorch. Ensuring compatibility between your GPU, CUDA version, and PyTorch installation is crucial for leveraging GPU acceleration effectively. For further assistance, consider visiting the PyTorch Forums for community support.
Master
PyTorch
debugging in Minutes
— Grab the Ultimate Cheatsheet
(Perfect for DevOps & SREs)
PyTorch
Cheatsheet
(Perfect for DevOps & SREs)
MORE ISSUES
Backed by
