./math/py-numba, NumPy aware dynamic Python compiler using LLVM

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Branch: CURRENT, Version: 0.41.0, Package name: py27-numba-0.41.0, Maintainer: pkgsrc-users

Numba is an Open Source NumPy-aware optimizing compiler for Python
sponsored by Continuum Analytics, Inc. It uses the
remarkable LLVM compiler infrastructure to compile Python syntax to
machine code.

It is aware of NumPy arrays as typed memory regions and so can speed-up
code using NumPy arrays. Other, less well-typed code will be translated
to Python C-API calls effectively removing the "interpreter" but not removing
the dynamic indirection.

Numba is also not a tracing JIT. It *compiles* your code before it gets
run either using run-time type information or type information you provide
in the decorator.

Numba is a mechanism for producing machine code from Python syntax and typed
data structures such as those that exist in NumPy.


Required to run:
[devel/py-setuptools] [math/py-numpy] [lang/python27] [devel/py-enum34] [devel/py-singledispatch] [devel/py-funcsigs] [devel/py-llvmlite]

Required to build:
[pkgtools/cwrappers]

Master sites:

SHA1: 7b6d35972de33b44f8db0ae7b649b6548594ba46
RMD160: 96cceef83a00128aff88caef7ec5e3b1a223e691
Filesize: 1502.108 KB

Version history: (Expand)


CVS history: (Expand)


   2018-12-09 21:25:12 by Adam Ciarcinski | Files touched by this commit (4) | Package updated
Log message:
py-numba: updated to 0.41.0

Version 0.41.0

This release adds the following major features:
* Diagnostics showing the optimizations done by ParallelAccelerator
* Support for profiling Numba-compiled functions in Intel VTune
* Additional NumPy functions: partition, nancumsum, nancumprod, ediff1d, cov,
  conj, conjugate, tri, tril, triu
* Initial support for Python 3 Unicode strings

General Enhancements:
* armv7 support
* invert mapping b/w binop operators and the operator module
* First attempt at parallel diagnostics
* Adding NUMBA_ENABLE_PROFILING envvar, enabling jit event
* Support for np.partition
* Support for np.nancumsum and np.nancumprod
* Add location information to exceptions.
* Support for np.ediff1d
* Support for np.cov
* Support user pipeline class in with lifting
* string support
* Improve error message for empty imprecise lists.
* Enable overload(operator.getitem)
* Support negative indexing in tuple.
* Refactor Const type
* Optimized usage of alloca out of the loop
* Updates for llvmlite 0.26
* Add support for `np.conj/np.conjugate`.
* np.tri, np.tril, np.triu - default optional args
* Permit dtype argument as sole kwarg in np.eye

CUDA Enhancements:
* Add max_registers Option to cuda.jit

Continuous Integration / Testing:
* CI with Azure Pipelines
* Workaround race condition with apt
* Fix issues with Azure Pipelines
* Fix `RuntimeWarning: 'numba.runtests' found in sys.modules`
* Disable openmp in wheel building
* Azure Pipelines templates
* Fix cuda tests and error reporting in test discovery
* Prevent faulthandler installation on armv7l
* Fix CUDA test that used negative indexing behaviour that's fixed.
* Start Flake8 checking of Numba source

Fixes:
* Fix dispatcher to only consider contiguous-ness.
* Fix 3119, raise for 0d arrays in reductions
* Reduce redundant module linking
* Fix AOT on windows.
* Fix memory management of __cuda_array_interface__ views.
* Fix typo in error name.
* Fix the default unboxing logic
* Allow non-global reference to objmode() context-manager
* Fix global reference in objmode for dynamically created function
* CUDA_ERROR_MISALIGNED_ADDRESS Using Multiple Const Arrays
* Correctly handle very old versions of colorama
* Add 32bit package guard for non-32bit installs
* Fix with-objmode warning
* Fix label offset in call inline after parfor pass
* Fixes raising of user defined exceptions for exec(<string>).
* Fix error due to function naming in CI in py2.7
* Fixed TBB's single thread execution and test added for
* Allow matching non-array objects in find_callname()
* Change getiter and iternext to not be pure.
* Make ir.UndefinedType singleton class.
* Fix np.random.shuffle sideeffect
* Raise unsupported for kwargs given to `print()`
* Remove dead script.
* Fix stencil support for boolean as return type
* Fix handling make_function literals
* Add missing unicode != unicode
* Fix complex math sqrt implementation for large -ve values
* This adds arg an check for the pattern supplied to Parfors.
* Sets list dtor linkage to `linkonce_odr` to fix visibility in AOT

Documentation Updates:
* Update 0.40 changelog with additional PRs
* Tweak spacing to avoid search box wrapping onto second line
* Add note about memory leaks with exceptions to docs.
* Add FAQ on CUDA + fork issue.
* Update docs for argsort, kind kwarg partially supported.
* Added mention of njit in 5minguide.rst
* Fix parallel reduction example in docs.
* Fix broken link and mark up problem.
* Size Numba logo in docs in em units.
* just two typos
* Document string support
* Documentation for parallel diagnostics.
   2018-09-04 01:47:44 by Min Sik Kim | Files touched by this commit (3)
Log message:
math/py-numba: Add ALTERNATIVES
   2018-08-28 14:06:42 by Adam Ciarcinski | Files touched by this commit (3) | Package updated
Log message:
py-numba: updated to 0.39.0

Version 0.39.0
Here are the highlights for the Numba 0.39.0 release.

This is the first version that supports Python 3.7.
With help from Intel, we have fixed the issues with SVML support.
List has gained support for containing reference-counted types like NumPy arrays \ 
and list. Note, list still cannot hold heterogeneous types.
We have made a significant change to the internal calling-convention, which \ 
should be transparent to most users, to allow for a future feature that will \ 
permitting jumping back into python-mode from a nopython-mode function. This \ 
also fixes a limitation to print that disabled its use from nopython functions \ 
that were deep in the call-stack.
For CUDA GPU support, we added a __cuda_array_interface__ following the NumPy \ 
array interface specification to allow Numba to consume externally defined \ 
device arrays. We have opened a corresponding pull request to CuPy to test out \ 
the concept and be able to use a CuPy GPU array.
The Numba dispatcher inspect_types() method now supports the kwarg pretty which \ 
if set to True will produce ANSI/HTML output, showing the annotated types, when \ 
invoked from ipython/jupyter-notebook respectively.
The NumPy functions ndarray.dot, np.percentile and np.nanpercentile, and \ 
np.unique are now supported.
Numba now supports the use of a per-project configuration file to permanently \ 
set behaviours typically set via NUMBA_* family environment variables.
Support for the ppc64le architecture has been added.
   2018-05-18 18:08:49 by Min Sik Kim | Files touched by this commit (4) | Package updated
Log message:
math/py-numba: Import version 0.37.0

Numba is an Open Source NumPy-aware optimizing compiler for Python
sponsored by Continuum Analytics, Inc.  It uses the
remarkable LLVM compiler infrastructure to compile Python syntax to
machine code.

It is aware of NumPy arrays as typed memory regions and so can speed-up
code using NumPy arrays.  Other, less well-typed code will be translated
to Python C-API calls effectively removing the "interpreter" but not \ 
removing
the dynamic indirection.

Numba is also not a tracing JIT.  It *compiles* your code before it gets
run either using run-time type information or type information you provide
in the decorator.

Numba is a mechanism for producing machine code from Python syntax and typed
data structures such as those that exist in NumPy.

Packaged by Kamil Rytarowski for pkgsrc-wip and updated by me.