We use PyDGN on a daily basis for our internal projects. In this first release there are some major additions to
previous and unreleased version that greatly improve the user experience.
Added
- Progress bars show the status of the experiments (with average completion time) for outer and inner cross validation (
CV).
- Tensorboard visualization is activated by default when using a Plotter.
- A new profiler keeps track of the time spent on each event (see Event engine).
- All experiments can be interrupted at any time and resumed gracefully (the engine looks for the last checkpoint).
Changed
- Removed all models that are not necessary to try and test the library.
Fixed
- Various multiprocessing issues caused by using the `fork` method with Pytorch.
Additional Notes
The library now creates new processes using the `spawn` method. Spawning rather than forking prevents Pytorch from
complaining (see https://github.com/pytorch/pytorch/wiki/Autograd-and-Fork
and https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods). Also, there is a warning on a
leaked semaphore which we will ignore for now. Finally, `spawn` will be useful to implement CUDA
multiprocessing https://pytorch.org/docs/stable/notes/multiprocessing.html#multiprocessing-cuda-note. However, the
Pytorch DataLoader in a child process breaks if `num_workers > 0`. Waiting for Pytorch to address this issue.