pytorch-lightning/docs/source/intro.md

New project Quick Start

To start a new project define two files, a LightningModule and a Trainer file.
To illustrate Lightning power and 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.

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_nb):
        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.

class CoolerNotBERT(pl.LightningModule):
    def __init__(self):
        self.net = ...
        
    def training_step(self, batch, batch_nb):
        # 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.

if use_bert:
    model = BERT()
else:
    model = CoolerNotBERT()
    
trainer = Trainer(gpus=4, use_amp=True)
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 all of the capabilities below (without coding or testing yourself).