Level up

Learn enough Lightning to match the level of expertise required by your research or job.

Basic skills

Learn the basics of model development with Lightning. Researchers and machine learning engineers should start here.

Level 1: Train a model

Learn the basics of training a model.

basic

Level 2: Add a validation and test set

Add validation and test sets to avoid over/underfitting.

basic

Level 3: Use pretrained models

Learn how to use pretrained models with Lightning

basic

Level 4: Enable script parameters

Add parameters to your script so you can run from the commandline.

basic

Level 5: Understand and visualize your model

Remove bottlenecks and visualize your model

basic

Level 6: Predict with your model

Use your model for predictions.

basic

Intermediate skills

Learn to scale up your models and enable collaborative model development at academic or industry research labs.

Level 7: Interactive cloud development

Learn how to access GPUs and TPUs on the cloud.

intermediate

Level 8: Modularize your projects

Create DataModules to enable dataset reusability.

intermediate

Level 9: Understand your model

Use advanced visuals to find the best performing model.

intermediate

Level 10: Explore SOTA scaling techniques

Explore SOTA techniques to help convergence, stability and scalability.

intermediate

Level 11: Deploy your models

Learn how to deploy your models with optimizations like ONNX and torchscript.

intermediate

Level 12: Optimize training speed

Use advanced profilers to mixed precision to train bigger models, faster.

intermediate

Level 13: Run on on-prem clusters

Run on a custom on-prem cluster or SLURM cluster.

intermediate

Advanced skills

Configure all aspects of Lightning for advanced usecases.

Level 14: Customize configs to run in production

Enable composable YAMLs

advanced

Level 15: Customize the trainer

Inject custom code into the trainer and modify the progress bar.

advanced

Level 16: Own the training loop

Learn all the ways of owning your raw PyTorch loops with Lightning.

advanced

Level 17: Enable advanced checkpointing

Enable composable or cloud based checkpoints.

advanced

Level 18: Explore HPUs

Explore Havana Gaudi Processing Unit (HPU) for model scaling.

advanced

Level 19: Master TPUs

Master TPUs and run on cloud TPUs.

advanced

Level 20: Train models with billions of parameters

Scale GPU training to models with billions of parameters

advanced

Expert skills

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

Level 21: Extend the Lightning CLI

Extend the functionality of the Lightning CLI.

expert

Level 22: Integrate a custom cluster

Integrate a custom cluster into Lightning.

expert

Level 23: Make your own profiler

Make your own profiler.

expert

Level 24: Add a new accelerator or Strategy

Integrate a new accelerator or distributed strategy.

expert