Review on QReserve by Darren Cole
CUDA out of memory. Tried to allocate 18.00 MiB (GPU 0; 15.74 GiB total capacity; 13.33 GiB already allocated; 9.56 MiB free; 13.86 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.
See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF CUDA out of memory. Tried to allocate 36.00 MiB (GPU 0; 15.74 GiB total capacity; 13.58 GiB already allocated; 9.56 MiB free; 13.86 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.
Pros
- Good integration between Python and GPUs, easy to get started with.
- Can handle very large datasets without having to be memory-limited.
Cons
- Python needs to be installed on the system where you want to run it, so that you can import its libraries.
- The platform cannot handle data directly, so it must be prepared first.
- There is no solution for this at the moment.
