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Sparse GSoC 2021 Final Report
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gsoc.md

About the project:

PyData/Sparse aims to replace scipy.sparse as the choice for sparse arrays in the Python ecosystem. Sparse is currently using the research done by the TACO team (https://github.com/tensor-compiler/taco) to perform Sparse array computations efficiently. TACO has a Python API, called pytaco, that makes the C++ code available in Python by wrapping it up in pybind11. Sparse aims to use the pytaco in a numpy.ndarray compatible API.

A summary of my work done so far:

My project roughly consists of three parts

  1. Complete revamp of the Sparse test suite by moving the tests to hypothesis, which uses property based testing.
  2. Update the cmake code of TACO so that it can be used by downstream users. This was mostly done by John Lee and is in this repository (https://github.com/leej3/taco/tree/cmake_changes).
  3. Creation of the ndarray compatible API mentioned. This is unfinished work in progress.

My proposal:

https://docs.google.com/document/d/1pH3h-TFY4s0BVA7sfC1bLPQDAmo4GUeDP7ovgkoSAs8/edit?usp=sharing

PR:

  1. pydata/sparse#492
  2. pydata/sparse#472

Closed PR:

  1. pydata/sparse#509
  2. pydata/sparse#491

Issues:

  1. pydata/sparse#505
  2. pydata/sparse#510
  3. pydata/sparse#506
  4. pydata/sparse#500
  5. pydata/sparse#503
  6. pydata/sparse#500

Blog:

https://blogs.python-gsoc.org/en/sayandip18s-blog/

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