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@ -46,15 +46,16 @@ Lightning disentangles PyTorch code to decouple the science from the engineering
## Lightning Philosophy
Lightning is designed with these principles in mind:
1. Enable maximal flexibility.
2. Abstract away unecessary boilerplate.
3. Systems should be self-contained (ie: optimizers, computation code, etc).
4. Deep learning code should be organized into 4 distinct categories
Principle 1: Enable maximal flexibility.
Principle 2: Abstract away unecessary boilerplate, but make it accessible when needed.
Principle 3: Systems should be self-contained (ie: optimizers, computation code, etc).
- Research code (the LightningModule).
- Engineering code (you delete, and is handled by the Trainer).
- Non-essential research code (logging, etc... this goes in Callbacks).
- Data (use PyTorch Dataloaders or organize them into a LightningDataModule).
Principle 4: Finally, Deep learning code should be organized into 4 distinct categories
- Research code (the LightningModule).
- Engineering code (you delete, and is handled by the Trainer).
- Non-essential research code (logging, etc... this goes in Callbacks).
- Data (use PyTorch Dataloaders or organize them into a LightningDataModule).
Once you do this, you can train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code!