Quick Start =========== | To start a new project define two files, a LightningModule and a Trainer file. | To illustrate the power of Lightning and its simplicity, here's an example of a typical research flow. Case 1: BERT ------------ | Let's say you're working on something like BERT but want to try different ways of training or even different networks. | You would define a single LightningModule and use flags to switch between your different ideas. .. code-block:: python class BERT(pl.LightningModule): def __init__(self, model_name, task): self.task = task if model_name == 'transformer': self.net = Transformer() elif model_name == 'my_cool_version': self.net = MyCoolVersion() def training_step(self, batch, batch_idx): if self.task == 'standard_bert': # do standard bert training with self.net... # return loss if self.task == 'my_cool_task': # do my own version with self.net # return loss Case 2: COOLER NOT BERT ----------------------- But if you wanted to try something **completely** different, you'd define a new module for that. .. code-block:: python class CoolerNotBERT(pl.LightningModule): def __init__(self): self.net = ... def training_step(self, batch, batch_idx): # do some other cool task # return loss Rapid research flow ------------------- Then you could do rapid research by switching between these two and using the same trainer. .. code-block:: python if use_bert: model = BERT() else: model = CoolerNotBERT() trainer = Trainer(gpus=4, precision=16) trainer.fit(model) **Notice a few things about this flow:** 1. You're writing pure PyTorch... no unnecessary abstractions or new libraries to learn. 2. You get free GPU and 16-bit support without writing any of that code in your model. 3. You also get early stopping, multi-gpu training, 16-bit and MUCH more without coding anything!