Learn enough Lightning to match the level of expertise required by your research or job.
Learn the basics of model development with Lightning. Researchers and machine learning engineers should start here.
Level 2: Add a validation and test set
Add validation and test sets to avoid over/underfitting.
Level 4: Enable script parameters
Add parameters to your script so you can run from the commandline.
Learn to scale up your models and enable collaborative model development at academic or industry research labs.
Level 8: Train in the background on the cloud
Learn how to run models on the cloud in the background.
Level 10: Understand your model
Use advanced visuals to find the best performing model.
Level 11: Explore SOTA scaling techniques
Explore SOTA techniques to help convergence, stability and scalability.
Level 12: Deploy your models
Learn how to deploy your models with optimizations like ONNX and torchscript.
Level 13: Optimize training speed
Use advanced profilers to mixed precision to train bigger models, faster.
Configure all aspects of Lightning for advanced usecases.
Level 16: Customize the trainer
Inject custom code into the trainer and modify the progress bar.
Level 17: Own the training loop
Learn all the ways of owning your raw PyTorch loops with Lighting.
Customize and extend Lightning for things like custom hardware or distributed strategies.