1. Introduction to PyTorch Lightning
Welcome! My name is Sergiy Tkachuk, and I'll be your instructor. In this video, we’ll explore how PyTorch Lightning simplifies AI model development, focusing on its core components: LightningModule and Trainer.
2. PyTorch & PyTorch Lightning
Standard PyTorch is powerful but requires defining explicit training loops, GPU/TPU handling, logging, and checkpointing.
3. PyTorch & PyTorch Lightning
PyTorch Lightning, a library built on top of PyTorch, simplifies this by automating training, checkpointing, logging, and managing distributed computing and mixed precision training.
This reduces boilerplate, improves scalability and reproducibility, and keeps the focus on model quality over infrastructure.
4. Overview of PyTorch Lightning
Imagine a global e-commerce platform on a mission to improve visual search—customers want faster results, but the development process is cluttered with boilerplate code. Here's where PyTorch Lightning can step in, streamlining experimentation and deployment.
At its core are the LightningModule and Trainer, two modules that strip away the complexities of defining models and training them, so that developers can focus on what really matters: innovation.
5. Lightning structure
The LightningModule defines a model’s logic, handling initialization, training steps, and optimization.
6. Lightning structure
The Trainer orchestrates the training workflow, managing everything from distributed training on multiple GPUs to callbacks and logging.
7. Lightning structure
To make model development even more efficient, Lightning offers a few more components: the DataModule, handling data loading and preprocessing; `Callbacks`, automating responses to training events; and the `Logger`, tracking and recording experimental metrics and insights. Together, these modules collaborate to build scalable AI solutions.
8. LightningModule in action
Let's take a look at how these modules work together in practice. In defining a model, we instantiate it as an instance of pl.LightningModule. The LightningModule encapsulates the __init__ method to initialize our model, loss criterion, and optimizer; forward to describe how data flows through the model; and training_step to define the training behavior.
9. Lightning Trainer in action
We can then define the model similarly to how we initialize standard PyTorch models. The Trainer efficiently manages the training loop, supports distributed training—from a single GPU to multiple nodes—and handles callbacks and logging. Plus, it optimizes resource usage so we can focus on innovating your model. In the code snippet here we initialize the Trainer with GPU acceleration, sets the maximum epochs, and kicks off the training process.
10. Introducing the Afro-MNIST dataset
Throughout the course we will use Afro-MNIST datasets to practice learnt concepts. The set consists of synthetic MNIST-style datasets for four orthographies used in Afro-Asiatic and Niger-Congo languages: Geez (Ethiopic), Vai, Osmanya, and N'Ko. Occasionally, we'll use regular MNIST for demonstration.
11. PyTorch Lightning recap
The LightningModule holds all the essential logic for our model initialization, training, and configuration.
These methods fit together forming a workflow for everything from data preparation to calculating losses and metrics. This modular design helps us make our code more maintainable, easier to scale, and "lightning" fast.
12. Let's practice!
Let's now move to the exercises to practice what we've learnt.