How to

Avoid overfitting — Learn how to add validation and test loops

Build a model — Step by step guide to build your model

Configure hyperparameters from the CLI — Make your experiments modular via command line interface

Customize the progress bar — Change the progress bar monitoring and tracking

Deploy models into production — Deploy models with different levels of scal

Optimize training — Explore advanced training techniques

Find bottlenecks in your code — Learn how to profile your experiments to find bottlenecks

Finetune a model — Learn how to use pretrained models

Manage data — How to use basic to advanced data techniques

Manage experiments — Learn to track and visualize with experiment managers

Organize existing PyTorch into Lightning — Convert your vanila PyTorch to Lightning

Run on a multi-node cluster — Learn to run on multi-node in the cloud or on your cluster

Save and load model progress — Save and load progress with checkpoints

Save memory with half-precision — Use precision techniques to train faster and save memory

Set up large models efficiently — Avoid memory peaks and speed up the initialization of large models

Train models with billions of parameters — Scale GPU training for models with billions of parameters

Train in a notebook — Train models in interactive notebooks (Jupyter, Colab, Kaggle, etc.)

Train on single or multiple GPUs — Train models faster with GPU accelerators

Train on single or multiple TPUs — TTrain models faster with TPU accelerators

Train on MPS — Train models faster with Apple Silicon GPUs

Track and Visualize Experiments — Learn to track and visualize experiments

Use a pretrained model — Improve results with transfer learning on pretrained models

Use a pure PyTorch training loop — Run your pure PyTorch loop with Lightning

Truncated Back-Propagation Through Time — Efficiently step through time when training recurrent models