As scientific computing increasingly relies on diverse hardware (CPUs, GPUs, etc) and data structures, libraries face pressure to support multiple backends while maintaining a consistent API. This talk presents practical considerations for adding dispatching to existing libraries, enabling seamless integration with external backends. Using NetworkX and scikit-image as case studies, we demonstrate how they evolved to become a common API with multiple implementations, handle backend-specific behaviors, and ensure robustness through testing and documentation. We also discuss technical challenges, differences in approaches, community adoption strategies, and the broader implications for the SciPy ecosystem.

NetworkX is arguably the most popular graph analytics library available today, but one of its greatest strengths - the pure-python implementation - is also possibly its biggest weakness. If you're a seasoned data scientists or a new student of the fascinating field of graph analytics, you're probably familiar with NetworkX and interested in how to make this extremely easy-to-use library powerful enough to handle realistically large graph workflows that often exceed the limitations of its pure-python implementation.

This talk will describe a relatively new capability of NetworkX; support for accelerated backends, and how they can benefit NetworkX users by allowing it to finally be both easy to use and fast. Through the use of backends, NetworkX can also be incorporated into workflows that take advantage of similar accelerators, such as Accelerated Pandas (cudf.pandas), to finally make these easy to use solutions scale to larger problems.

Attend this talk to learn about how you can leverage the various backends available to NetworkX today to seamlessly run graph analytics on GPUs, use GraphBLAS implementations, and more, all without leaving the comfort and convenience of the most popular graph analytics library available.