How does PyTorch Lightning help speed up experiments on cloud GPUs compared to classic PyTorch?

PyTorch Lightning offers a higher-level interface to PyTorch, designed to streamline and accelerate the development and training process, particularly beneficial when using cloud GPUs, where billing is time-dependent. Unlike classic PyTorch, which requires manual coding of training loops and device management, Lightning abstracts these tasks, allowing developers to focus more on model improvements rather than boilerplate coding. This automation not only reduces coding errors and debugging time but also supports easy integration of multi-GPU setups, thus facilitating faster experimentation cycles. While Lightning's abstraction might introduce minimal overhead, it generally maintains comparable training speed to well-optimized PyTorch scripts. The productivity benefits, such as quicker iterations and easier scalability, translate to cost savings on cloud platforms like Runpod, where users can efficiently manage resources and track experiments. Despite its advantages, certain complex or non-standard training procedures may still necessitate classic PyTorch's flexibility, although Lightning remains a powerful tool for most research and applied settings.