Log Message:
py-numba: updated to 0.49.0

Version 0.49.0:

This release is very large in terms of code changes. Large scale removal of \ 
unsupported Python and NumPy versions has taken place along with a significant \ 
amount of refactoring to simplify the Numba code base to make it easier for \ 
contributors. Numba’s intermediate representation has also undergone some \ 
important changes to solve a number of long standing issues. In addition some \ 
new features have been added and a large number of bugs have been fixed!

IMPORTANT: In this release Numba’s internals have moved about a lot. A \ 
backwards compatibility “shim” is provided for this release so as to not \ 
immediately break projects using Numba’s internals. If a module is imported \ 
from a moved location the shim will issue a deprecation warning and suggest how \ 
to update the import statement for the new location. The shim will be removed in \ 
0.50.0!

Highlights of core feature changes include:

Removal of all Python 2 related code and also updating the minimum supported \ 
Python version to 3.6, the minimum supported NumPy version to 1.15 and the \ 
minimum supported SciPy version to 1.0.
Refactoring of the Numba code base. The code is now organised into submodules by \ 
functionality. This cleans up Numba’s top level namespace.
Introduction of an ir.Del free static single assignment form for Numba’s \ 
intermediate representation
An OpenMP-like thread masking API has been added for use with code using the \ 
parallel CPU backends
For the CUDA target, all kernel launches now require a configuration, this \ 
preventing accidental launches of kernels with the old default of a single \ 
thread in a single block. The hard-coded autotuner is also now removed, such \ 
tuning is deferred to CUDA API calls that provide the same functionality
The CUDA target also gained an External Memory Management plugin interface to \ 
allow Numba to use another CUDA-aware library for all memory allocations and \ 
deallocations
The Numba Typed List container gained support for construction from iterables
Experimental support was added for first-class function types