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 an on-prem cluster Learn to run on your own 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 1 trillion+ parameter models Scale GPU training to 1 trillion + parameter models Train on single or multiple GPUs Train models faster with GPUs. Train on single or multiple HPUs Train models faster with HPUs. Train on single or multiple IPUs Train models faster with IPUs. 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.