lightning/docs/index.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).

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Templates

1. MNIST LightningModule
2. Trainer
   • Basic CPU, GPU Trainer Template
   • GPU cluster Trainer Template

Docs shortcuts

• LightningModule
• Trainer

Quick start examples

• CPU example
• Hyperparameter search on single GPU
• Hyperparameter search on multiple GPUs on same node
• [Hyperparameter search on a SLURM HPC cluster](examples/Examples/#Hyperparameter search on a SLURM HPC cluster)

Checkpointing

• Checkpoint callback
• Model saving
• Model loading
• Restoring training session

Computing cluster (SLURM)

• Running grid search on a cluster
• Walltime auto-resubmit

Debugging

• Fast dev run
• Inspect gradient norms
• Log GPU usage
• Make model overfit on subset of data
• Print the parameter count by layer
• Pring which gradients are nan
• Print input and output size of every module in system

Distributed training

• Implement Your Own Distributed (DDP) training
• 16-bit mixed precision
• Multi-GPU
• Multi-node
• Single GPU
• Self-balancing architecture

Experiment Logging

• Display metrics in progress bar
• Log metric row every k batches
• Process position
• Tensorboard support
• Save a snapshot of all hyperparameters
• Snapshot code for a training run
• Write logs file to csv every k batches

Training loop

• Accumulate gradients
• Force training for min or max epochs
• Early stopping callback
• Force disable early stop
• Gradient Clipping
• Hooks
• Learning rate scheduling
• Use multiple optimizers (like GANs)
• Set how much of the training set to check (1-100%)
• Step optimizers at arbitrary intervals

Validation loop

• Check validation every n epochs
• Hooks
• Set how much of the validation set to check
• Set how much of the test set to check
• Set validation check frequency within 1 training epoch
• Set the number of validation sanity steps

Testing loop

• Run test set