pytorch-lightning/docs/index.md

PYTORCH-LIGHTNING DOCUMENTATION

Main Docs

• LightningModule
• Trainer

New project Quick Start

1. Define a LightningModule
2. Pick a trainer
   • Basic CPU Trainer
   • GPU cluster Trainer

Quick start examples

• CPU example
• Single GPU example
• Multi-gpu example
• SLURM cluster grid search example

Checkpointing

• Model saving
• Model loading

######Computing cluster (SLURM)

• Automatic checkpointing
• Automatic saving, loading
• 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

######Distributed training

• 16-bit mixed precision
• Multi-GPU
• Multi-node
• Single GPU
• Self-balancing architecture

######Experiment Logging

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

######Training loop

• Accumulate gradients
• Anneal Learning rate
• Force training for min or max epochs
• Force disable early stop
• Use multiple optimizers (like GANs)
• Set how much of the training set to check (1-100%)

######Validation loop

• Check validation every n epochs
• 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