Scientific Python Ecosystem offers a wide variety of numerical packages, such as NumPy, CuPy, or JAX. One of the domains that also captures a lot of attention in the community is sparse computing.

In this talk, we will present the current landscape of sparse computing in the Python ecosystem and our efforts to revive/expand it. Our main contributions to the Python ecosystem cover: (1) making a novel Finch sparse tensor compiler and Galley scheduler available for the community, (2) standardizing various aspects of sparse computing. We will show how to use the Finch compiler with the PyData/Sparse package and how it outperforms well-established alternatives for multiple kernels, such as MTTKRP or SDDMM.

Real-world use-cases will show you how, step-by-step, Python practitioners can migrate their code to an Array API compatible version and benefit from tensor operator fusion and autoscheduling capabilities offered by the Finch compiler.

Apart from the existing Julia implementation, the number of sparse backends offered by PyData/Sparse will grow in the future to provide a Python-native alternatives for scipy.sparse and Numba solutions.