The RuntimeError: cuda error: an illegal memory access was encountered error occurs when an issue accessing GPU memory in your CUDA application.
The error suggests an out-of-bound memory access similar to a segfault on the CPU, like an indexing error in the low-level code.
Sometimes RuntimeError occurs on the large batch size. It does not occur on smaller batch sizes. The peak memory usage might have caused the OOM issue.
Reasons
- If the memory you are accessing has already been freed.
- If you are trying to access memory outside the bounds of an array.
- Incorrect device-to-host or host-to-device data transfers.
- Trying to read or write to read-only or write-only memory.
How to Fix RuntimeError: cuda error: an illegal memory access was encountered
To fix the RuntimeError: cuda error: an illegal memory access was encountered, set a specific GPU using the device = torch.cuda.set_device(1) function instead of device = torch.device(“cuda:1”) function.
The code device = torch.cuda.set_device(1) sets the current CUDA device used by PyTorch to be the GPU with index 1.
PyTorch supports multiple GPUs, and the set_device() function is used to specify which GPU should be used for computations.
Alternate solutions
- You can check the memory allocation and access patterns in your code.
- You must ensure proper synchronization of host and device memory operations.
- You need to use the CUDA profiler to inspect memory access patterns.
- Consider using tools for memory debugging, such as cuda-memcheck.
- You need to verify compatibility issues with the GPU driver and CUDA version.
After considering all the proposed solutions, one solution might work for you.
CUDA is a parallel computing platform and programming model created by NVIDIA.
CUDA has been widely adopted across consumer and industrial ecosystems to accelerate high-performance computing (HPC) and research applications.
I hope this will help you resolve your error.
