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. (View Highlight)
You can think of PyTorch Lightning as a lightweight wrapper around PyTorch. (View Highlight)
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. (View Highlight)
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. (View Highlight)
