# Numba Meeting: 2023-04-18

Attendees: Siu Kwan Lam, brandon willard, Guilherme, Ianna Osborne, Jim Pivarski, stuart, Todd A. Anderson, Luk, Andre Masella
FPOC (last week): Guilherme
FPOC (incoming): Andre

NOTE: All communication is subject to the [Numba Code of Conduct](https://github.com/numba/numba-governance/blob/accepted/code-of-conduct.md).

Please refer to [this calendar](https://calendar.google.com/calendar/embed?src=5rqnddm4gjrdfjm31fsv182epk%40group.calendar.google.com) for the next meeting date.

## 0. Discussion

- Issue #8897: [Replicate old generated_jit behavior on basis of overload for only-jitted variants of functions](https://github.com/numba/numba/issues/8897)
- `generated_jit` was an older less flexible version of `overload` and the plan was to deprecated and migrate
- there was an unhandled case discovered after migration; if a Python function, with an overloaded stub, and you want to call it from Python, you need to provide a Python implementation; however, the Python interpreted version will be run, not the JITted version.
- ultimately, we have deprecated something without a direct analogue
- Options: 1) try to reimplement existing functionality, 2) say not to use it; we'd prefer to try to implement `generated_jit` using `overload`
- One possible prototype was developed by Stuart: https://github.com/numba/numba/issues/8466#issuecomment-1274593340
- caveats include: kwargs and using disable JIT will break in unexpected ways
- it is unlikely to get something that will work the same and the problems are likely not fixable (and the bugs were part of the motivations for migration)
- do we only care about the case where we can call JITed function from CPython; is that sufficient?
- this functionality is useful elsewhere
- what are the motivating use cases?
- having the ability to call a complex math function from CPython and also via the JIT(?)
- a concrete example where multiple dispatch at the CPython level
- some boilerplate likely required
- plan is to figure out examples for boilerplate to make this work for users without bringing back or reimplementing `generated_jit` and see if that satisfied everyone's needs. (Stuart)
- xDSL (https://xdsl.dev/)
- lots of potential uses for Numba and lots of potential contributions to xDSL from Numba
- MLIR has a high barrier to entry due to C++ and tablegen; xDSL managed to communicate with C++ and has a much lower barrier
- Umbrella projects (No discussion)
- Numba-Scipy maintainers

## New "Ready for Review" PRs

## 1. New Issues
- [numba#8891](https://github.com/numba/numba/issues/8891) - Error when calling `nb.generated_jit` from CUDA
- [numba#8894](https://github.com/numba/numba/issues/8894) - I cannot install numba with pip
- [numba#8897](https://github.com/numba/numba/issues/8897) - Replicate old generated_jit behavior on basis of overload for only-jitted variants of functions
- [numba#8898](https://github.com/numba/numba/issues/8898) - numba 0.57.0rc1 segfaults: missing ldexpf for np.exp2 on Windows
- [numba#8899](https://github.com/numba/numba/issues/8899) - Unable to install shap on python 3.11 doubt to numba cannot install on Python version 3.11.2
- [numba#8900](https://github.com/numba/numba/issues/8900) - 0.57.0rc1 UnsupportedError when number of keyword arguments in CALL_FUNCTION_EX is 16 or more
- [numba#8901](https://github.com/numba/numba/issues/8901) - IntEnum get item by value
- [numba#8903](https://github.com/numba/numba/issues/8903) - 0.57.0rc1 NumbaDeprecationWarning: `@guvectorize` even with `nopython` keyword argument
- [numba#8904](https://github.com/numba/numba/issues/8904) - IndexError and TypeError when overloading a function with keyword-only arguments

### Closed Issues

## 2. New PRs
- [numba#8893](https://github.com/numba/numba/pull/8893) - Support NVRTC using the ctypes binding
- [numba#8895](https://github.com/numba/numba/pull/8895) - CUDA: Enable caching functions that use CG
- [numba#8902](https://github.com/numba/numba/pull/8902) - Enable CALL_FUNCTION_EX test for py3.11
- [numba#8905](https://github.com/numba/numba/pull/8905) - FIX: Add missing `**targetoptions` to `jit` in `GUFuncBuilder`
- [numba#8906](https://github.com/numba/numba/pull/8906) - Add support for reflected dunder methods in jitclass
- [numba#8907](https://github.com/numba/numba/pull/8907) - Add tests for reproducing issue #8898.
- [llvmlite#934](https://github.com/numba/llvmlite/pull/934) - Expose TargetLibraryInfo pass
- [llvmlite#935](https://github.com/numba/llvmlite/pull/935) - Disable zlib for LLVM on Windows

### Closed PRs
- merged - [numba#8892](https://github.com/numba/numba/pull/8892) - Add support for __*matmul__ methods in jitclass
- merged - [numba#8896](https://github.com/numba/numba/pull/8896) - Remove codecov install (now deleted from PyPI

## 3. Next Release: Version 0.57.0/0.40.0, RC Jan 2023