Bagua thrives on the diversity of distributed learning algorithms. The great flexibility of the system makes it possible to smoothly incorporate various of SOTA algorithms while providing automatic optimizations for the performance during the execution. For the end user, Bagua provides a wide range of choices of algorithms, which she can easily try out for her tasks. For the algorithm developer, Bagua is a playground where she can be just focused on the algorithm itself (e.g., the logic and control) without reinventing the wheels (e.g., communication primitives and system optimizations) across different algorithms.
In the following tutorials, we will describe several algorithms that have already been implemented within Bagua, including the main ideas of each algorithm and their usage in specific examples. Then we are going to demonstrate how to add a new algorithm into Bagua.
We welcome contributions to add more algorithms!