Error Message:
RuntimeError: CUDA error: initialization error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
I encountered a persistent RuntimeError: CUDA error: initialization error while trying to train my model in a Kaggle Notebook, specifically when using the accelerate library for multi-GPU training. This error repeatedly appeared despite various attempts to resolve it.
Initially, I tried to debug by reordering the instantiation of objects, specifically moving the accelerate.Accelerator call to occur before the model and data loaders were instantiated. My thought was to ensure accelerate had full control before any GPU resources were accessed.
Next, I focused on preventing any premature GPU-related calls. I carefully reviewed my Config class and removed methods like torch.cuda.is_available() that might inadvertently try to access CUDA before accelerate was fully set up.
These steps were somewhat successful in preventing early CUDA access, but the core issue persisted when trying to use notebook_launch. I wasn’t able to pinpoint exactly where notebook_launch was causing the conflict.
Ultimately, the solution was to convert the Kaggle Notebook into a standard Python script and run it directly. Surprisingly, this resolved the issue, and the training proceeded without the CUDA initialization error, even with the model and data loaders being called before the Accelerator was instantiated in the script.
What worked?
It appears the CUDA error: initialization error I faced in the Kaggle Notebook environment when using accelerate’s notebook_launch was an environmental or compatibility issue specific to that setup. By converting the notebook to a standalone script, I bypassed whatever underlying conflict was preventing proper CUDA initialization within the Kaggle Notebook execution flow. This suggests that for complex multi-GPU setups with accelerate, running as a script might offer more stable and predictable behavior compared to notebook_launch in certain interactive notebook environments.