Common Workflows

Customize and extend Lightning for things like custom hardware or distributed strategies.

• Avoid overfitting — Add a training and test loop.

• Build a model — Steps to build a model.

• Configure hyperparameters from the CLI — Enable basic CLI with Lightning.

• Customize the progress bar — Change the progress bar behavior.

• Deploy models into production — Deploy models with different levels of scale.

• Effective Training Techniques — Explore advanced training techniques.

• Eliminate config boilerplate — Control your training via CLI and YAML.

• Find bottlenecks in your code — Learn to find bottlenecks in your code.

• Finetune a model — Learn to use pretrained models

• Manage Experiments — Learn to track and visualize experiments

• Run on a multi-node cluster — Learn to run 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 — Enable half-precision to train faster and save memory.

• Train models with billions of parameters — Scale GPU training to 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 GPUs.

• Train on single or multiple TPUs — Train models faster with TPUs.

• Track and Visualize Experiments — Learn to track and visualize experiments

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