ghosthamlet on Aug 6, 2019 | parent | context | favorite | on: Rapid research framework for PyTorch, The research... See Pytorch Lightning vs PyTorch Ignite vs ...
03.12.2021 · Both Lightning and Ignite are good in their own ways. If you’re looking for flexibility, then Ignite is good because you can use conventional Pytorch to design your architecture, optimizers, and experiment as a whole. Ignite will help you assemble different components in a particular function.
PyTorch Ignite and Pytorch Lightning were both created to give the researchers as much flexibility by requiring them to define functions for what happens in the training loop and validation loop. Lightning has two additional, more ambitious motivations: reproducibility and democratizing best practices which only PyTorch power-users would implement (Distributed …
To learn more about the advantages of Lightning you should check out William Falcon 's post on the subject. Pytorch Lightning vs PyTorch Ignite vs Fast.ai.
05.08.2019 · PyTorch Ignite and Pytorch Lightning were both created to give the researchers as much flexibility by requiring them to define functions for what happens in the training loop and validation loop. Lightning has two additional, more ambitious motivations: reproducibility and democratizing best practices which only PyTorch power-users would implement (Distributed …
10.06.2020 · I have used PyTorch Lightning. (While I can’t compare the two, as I haven’t used Ignite). It has been the smoothest experience as far as I have come across, w.r.t multi-GPU training. Changing from a single GPU to a multi-GPU setup is as simple as setting num_gpus in trainer.fit() to as many as you’d like to use.
04.08.2019 · At a base level, lightning implements distributed GPU, multi-node, 16-bit, auto-checkpoint and resubmit for cluster training. The learning curve with lightning is also lower since it doesn’t make unnecessary wrappings. Further, lightning is also very thoroughly documented and well tested as is ignite.
However, if the individuality is too strong, it can be difficult to share with other people or reuse it between competitions (ex. Oreore Trainer, which is often ...