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NumPy — State of the project & Roadmap
Ralf Gommers
Scope & Vision
The key thing NumPy offers is:
an array object (N-dimensional, in-memory, on CPUs)
and array computing APIs...
State of the Project
• ~10 active maintainers (but bus factor ~2-3)
• Since Q1-Q2 2018: 2 funded developers
• Positive tre...
State of the Project
Have in place now:
• governance structure
• technical roadmap
• code of conduct
• semi-regular sprint...
Key tech goal: improved interoperability
Make code written for the NumPy API work with:
distributed arrays (e.g. Dask)

sp...
Key tech goal: improved interoperability
Current state of N-dimensional arrays in Python
Key tech goal: improved interoperability
What we aim for
Key tech goal: easier custom dtypes
Enable library authors and advanced users to create arrays
with:
categoricals

variabl...
Sample of other important tech goals
• New random number generation framework
• New indexing methods (vindex/oindex)
• Bac...
Sustainability goal: grow maintainers & leadership
Do whatever we can to make NumPy more sustainable as a
project. E.g.:
•...
Outward focus
Redesign numpy.org — address various audiences:
end users (beginner & advanced)

library developers

package...
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NumPy Roadmap presentation at NumFOCUS Forum

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This presentation is an attempt to summarize the NumPy roadmap and both technical and non-technical ideas for the next 1-2 years to users that heavily rely on NumPy, as well as potential funders.

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NumPy Roadmap presentation at NumFOCUS Forum

  1. 1. NumPy — State of the project & Roadmap Ralf Gommers
  2. 2. Scope & Vision The key thing NumPy offers is: an array object (N-dimensional, in-memory, on CPUs) and array computing APIs NumPy lives at the heart of the numerical Python ecosystem. We want to: evolve while remaining a stable base address bottlenecks that limit how the
 ecosystem can grow
  3. 3. State of the Project • ~10 active maintainers (but bus factor ~2-3) • Since Q1-Q2 2018: 2 funded developers • Positive trend in activity: • Lots of ideas …
 
 … and challenges
  4. 4. State of the Project Have in place now: • governance structure • technical roadmap • code of conduct • semi-regular sprints & in-person meetings
  5. 5. Key tech goal: improved interoperability Make code written for the NumPy API work with: distributed arrays (e.g. Dask)
 sparse arrays (pydata/sparse)
 GPU arrays (e.g. CuPy)
 … without changing that code. How: protocols (__array_ufunc__, __array_function__). Implementation under way
  6. 6. Key tech goal: improved interoperability Current state of N-dimensional arrays in Python
  7. 7. Key tech goal: improved interoperability What we aim for
  8. 8. Key tech goal: easier custom dtypes Enable library authors and advanced users to create arrays with: categoricals
 variable-length strings
 units
 auto-diff functionality
 … In planning stage
  9. 9. Sample of other important tech goals • New random number generation framework • New indexing methods (vindex/oindex) • Backends for fft & linalg • Full rewrite of numpy.fft In planning stage API discussion ongoing In progress Close to done
  10. 10. Sustainability goal: grow maintainers & leadership Do whatever we can to make NumPy more sustainable as a project. E.g.: • Improve Continuous Integration • Make code base more approachable (docs, refactoring) • Pathways beyond volunteer effort
  11. 11. Outward focus Redesign numpy.org — address various audiences: end users (beginner & advanced)
 library developers
 packagers
 educators
 potential contributors
 …
 Dialogue with key stakeholders — your pain points, needs and desires & how to best reach us.

This presentation is an attempt to summarize the NumPy roadmap and both technical and non-technical ideas for the next 1-2 years to users that heavily rely on NumPy, as well as potential funders.

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