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UC Berkeley (BIDS) sprint, May 28 Jun 2 2018

Juan Nunez-Iglesias edited this page May 10, 2018 · 1 revision

This page collects ideas and issues for the scikit-image side of the joint scikit-learn/scikit-image/dask sprint at UC Berkeley.

  • work on the parallelization of some algorithms to improve performance, focussing maybe first on the case of machines with several cores (rather than a distributed architecture, which most users don't use I guess). This would need some benchmarking and we could test several solutions such as dask (with Matt Rocklin, we could test and maybe improve our apply_parallel) as well as joblib and its different backends (with Gaël and Olivier). Emma can provide some tomography datasets for this, it would be better if we had a machine with 10-20 cores to ssh on for benchmarking, maybe with AWS? Or a BIDS machine?
  • discussing all GitHub "needs decision" PRs/issues.
  • get nD transforms, based on NumPy array coordinates, working. (with Kira Evans)
  • figure out whether we can reliably distribute Numba, and if so, do we want to get that dependency.
  • implement flood-fill with support for lowlevelcallables (see
  • fix data types and ranges. (See #3009.)
  • talk about release schedules, governance, funding, leadership, outreach, and a few more general topics. (Though Juan thinks these things should happen in the evenings over dinner etc. ;)

Stuff that we want to happen before the sprint:

  • announce the sprint on the mailing list to see whether anyone wants to join remotely
  • get airspeed velocity working (though maybe this needs to happen before the sprint anyway)
  • draw up scikit-image 1.0 roadmap (including emailing mailing list for community discussion)
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