1 Level
1318 Review
31 Karma
Review on JXL by Jeffrey Sanders
CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 14.62 GiB total capacity; 13.51 GiB already allocated; 14.00 MiB free; 13.65 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.
CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 14.62 GiB total capacity; 13.49 GiB already allocated; 14.00 MiB free; 13.65 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 20.00 MiB (GPU 0; 14.62 GiB total capacity; 13.51 GiB already allocated; 14.00 MiB free; 13.65 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.
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Pros
- Support for distributed training on clusters.
- Easy to integrate with Keras API.
- Simple codebase and easy to customize.
Cons
- Documentation is scattered across many files.
- Performance issues when running deep learning models on large datasets.
- A little bit difficult to learn due to its complexity.
