lightning/examples
awaelchli fc1559e41c Rename profiler to profilers (#12308)
Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>
Co-authored-by: Akihiro Nitta <nitta@akihironitta.com>
2022-06-22 20:55:39 -04:00
..
convert_from_pt_to_pl Future 2/n: stand-alone examples (#13294) 2022-06-15 08:53:51 -04:00
pl_basics Rename profiler to profilers (#12308) 2022-06-22 20:55:39 -04:00
pl_bug_report Update old PL links (#13349) 2022-06-21 16:38:04 +02:00
pl_domain_templates Update old PL links (#13349) 2022-06-21 16:38:04 +02:00
pl_fault_tolerant Future 2/n: stand-alone examples (#13294) 2022-06-15 08:53:51 -04:00
pl_hpu Future 2/n: stand-alone examples (#13294) 2022-06-15 08:53:51 -04:00
pl_integrations Future 2/n: stand-alone examples (#13294) 2022-06-15 08:53:51 -04:00
pl_ipu Future 2/n: stand-alone examples (#13294) 2022-06-15 08:53:51 -04:00
pl_loops Future 2/n: stand-alone examples (#13294) 2022-06-15 08:53:51 -04:00
README.md Future 2/n: stand-alone examples (#13294) 2022-06-15 08:53:51 -04:00
run_ddp_examples.sh Future 2/n: stand-alone examples (#13294) 2022-06-15 08:53:51 -04:00
run_pl_examples.sh Future 2/n: stand-alone examples (#13294) 2022-06-15 08:53:51 -04:00
test_pl_examples.py Future 2/n: stand-alone examples (#13294) 2022-06-15 08:53:51 -04:00

README.md

Examples

Our most robust examples showing all sorts of implementations can be found in our sister library Lightning Bolts.


MNIST Examples

5 MNIST examples showing how to gradually convert from pure PyTorch to PyTorch Lightning.

The transition through LightningLite from pure PyTorch is optional, but it might be helpful to learn about it.


Basic Examples

In this folder, we have 2 simple examples:


Domain Examples

This folder contains older examples. You should instead use the examples in Lightning Bolts for advanced use cases.


Basic Examples

In this folder, we have 1 simple example:


Loop examples

Contains implementations leveraging loop customization to enhance the Trainer with new optimization routines.

  • K-fold Cross Validation Loop: Implementation of cross validation in a loop and special datamodule.
  • Yield Loop: Enables yielding from the training_step like in a Python generator. Useful for automatic optimization with multiple optimizers.