Lightning is also part of the PyTorch ecosystem which requires projects to have solid testing, documentation and support.
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10+ core contributors who are all a mix of professional engineers, Research Scientists, and Ph.D.In the Lightning v1.5 release, LightningLite now enables you to leverage all the capabilities of PyTorch Lightning Accelerators without any refactoring to your training loop. Minimal running speed overhead (about 300 ms per epoch compared with pure PyTorch).We test every combination of PyTorch and Python supported versions, every OS, multi GPUs and even TPUs. Lightning has dozens of integrations with popular machine learning tools.Keeps all the flexibility (LightningModules are still PyTorch modules), but removes a ton of boilerplate.Make fewer mistakes because lightning handles the tricky engineering.
Code is clear to read because engineering code is abstracted away.manual_backward( loss_b, opt_b, retain_graph = True) # access your optimizers with use_pl_optimizer=False. automatic_optimization = False def training_step( self, batch, batch_idx): TPU p圓.7 means we support Colab and Kaggle env.Ĭlass LitAutoEncoder( pl. Current build statuses System / PyTorch ver. Lightning is rigorously tested across multiple CPUs, GPUs, TPUs, IPUs, and HPUs and against major Python and PyTorch versions. Once you do this, you can train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code! Data (use PyTorch DataLoaders or organize them into a LightningDataModule).Non-essential research code (logging, etc.Engineering code (you delete, and is handled by the Trainer).Lightning forces the following structure to your code which makes it reusable and shareable: