Getting Started with PyTorch Lightning - KDnuggets

Libraries like TensorFlow and PyTorch take care of most of the intricacies of building deep learning models that train and infer fast. Predictably, this leaves machine learning engineers spending most of their time on the next level up in abstraction, running hyperparameter search, validating performance, and versioning models and experiments to keep track of everything. If PyTorch and TensorFlow (and now JAX) are the deep learning cake, higher-level libraries are the icing. For years now TensorFlow has had its "icing on the cake" in the high-level Keras API, which became an official part of TensorFlow itself with the release of TF 2.0 in 2019. Similarly, PyTorch users have benefited from the high-level fastai library, which is exceptionally well-suited for efficiency and transfer learning.