From PyTorch to PyTorch Lightning
Simplify deep learning model building and training with PyTorch Lightning.
PyTorch has always been my go-to library for building any deep learning model. However, there are many things that I really dislike about PyTorch.
PyTorch Lightning is one of the best solutions I built proficiency in a few years back to address these challenges.
Today, I have published an entirely beginner-friendly deep dive, where you can learn about PyTorch Lightning and how it supercharges deep learning model building and training: A Detailed and Beginner-Friendly Introduction to PyTorch Lightning: The Supercharged PyTorch:
Why PyTorch Lightning?
One of the most significant issues with PyTorch is that one has to manually write its long training loops shown below, which is primarily boilerplate code.
Moreover, if you read the mixed precision training issue (highly recommended if you haven’t yet) I published recently, you might remember the amount of code we wrote to implement it:
Other than that, it is quite a hassle to perform multi-GPU and TPU training with PyTorch:
Logging, profiling, distributed debugging, etc., are some more problems I have almost always faced when using PyTorch.
PyTorch Lightning resolves each of the above-discussed challenges with PyTorch.
You can think of PyTorch Lightning as a lightweight wrapper around PyTorch.
Just like Keras is a wrapper on TensorFlow, PyTorch lightning is a wrapper on PyTorch, but one that makes it much more efficient than the traditional way of training the model.
PyTorch Lightning:
Abstracts away the boilerplate code, which we typically write with PyTorch
Provides elegant and one-liner support for mixed precision training.
Works seamlessly in a distributed setting, again, with just a few lines of code.
Comes with built-in logging and profiling capabilities, and much more.
In this week’s ML deep dive, this is precisely what we are discussing: A Detailed and Beginner-Friendly Introduction to PyTorch Lightning: The Supercharged PyTorch:
We begin with understanding the limitations of PyTorch in detail.
We then write standard PyTorch code, and learn how to convert that into PyTorch Lightning code.
Next, we look at how we use the
Trainer()class from PyTorch Lightning to simplify model training and define various methods for training, validation, testing, and predicting. Here, we also learn how to log model training and integrate various performance metrics during training.Finally, we deep dive into the additional utilities offered by PyTorch Lightning, such as mixed precision training, callbacks, profiling code for optimization, etc.
Read the article here for more details: A Detailed and Beginner-Friendly Introduction to PyTorch Lightning: The Supercharged PyTorch.
Thanks for reading!