PyTorch Lightning is a high-level interface for PyTorch, an open-source machine learning library that is popular among researchers in the AI and deep learning communities. As the ultimate PyTorch research framework, PyTorch Lightning aims to simplify the process of scaling models by eliminating much of the boilerplate code typically associated with the development of deep learning models.

With PyTorch Lightning, researchers and data scientists can focus on the core aspects of their projects, such as data parsing, model architecture design, and optimization, among other things. By abstracting away the complexity of training, scaling, and optimizing neural networks, PyTorch Lightning offers a streamlined workflow that encourages efficient, cleaner, and less error-prone code.

Moreover, increasing the scale of a model or switching to a different hardware setup often requires significant code modifications. However, with PyTorch Lightning, these adjustments become virtually seamless, permitting researchers to horizontally scale their models to run on distributed systems without altering their codebase drastically.

The 'no boilerplate' promise of PyTorch Lightning refers to its capability to significantly reduce the amount of repetitive or unnecessary code - the boilerplate - used across similar projects. PyTorch Lightning offers the automation of these tedious components, enabling scientists to focus on the innovative aspect of their research from data collection and manipulation to testing and refining models.

In terms of its architecture, PyTorch Lightning maintains the flexibility of PyTorch but increases its functionality by adding numerous features beneficial to professionals engaged in AI and deep learning. For instance, its built-in functionalities for multi-GPUs, 16-bit precision, and TensorBoard logging, among others, make it a valuable tool in the research framework arsenal.

A key advantage of using PyTorch Lightning is its interoperability with the greater PyTorch ecosystem. It is compatible with almost all major PyTorch libraries, which means it doesn't interrupt any existing workflows while introducing its benefits at the same time.

PyTorch Lightning promotes best practices in research reproducibility and model sharing. As researchers are aided in reducing their code and organizing it into a more standardized structure, they are better equipped to reproduce, inspect, and share their work with peers, thereby fostering collaboration.

In summary, PyTorch Lightning simplifies the research process, especially in complex projects involving deep learning and AI. It reduces the need for boilerplate code, facilitates the scaling of models, and promotes research best practices, thereby helping researchers focus on the essence of research and innovation. It stands as an essential tool in any deep learning practitioner's toolkit.