2017-05-27 13:14:35 +02:00
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#
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2021-11-18 21:40:38 +01:00
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# spec file
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2017-05-27 13:14:35 +02:00
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#
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2022-01-10 18:36:04 +01:00
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# Copyright (c) 2022 SUSE LLC
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2017-05-27 13:14:35 +02:00
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#
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# All modifications and additions to the file contributed by third parties
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# remain the property of their copyright owners, unless otherwise agreed
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# upon. The license for this file, and modifications and additions to the
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# file, is the same license as for the pristine package itself (unless the
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# license for the pristine package is not an Open Source License, in which
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# case the license is the MIT License). An "Open Source License" is a
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# license that conforms to the Open Source Definition (Version 1.9)
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# published by the Open Source Initiative.
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2018-10-27 04:31:23 +02:00
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# Please submit bugfixes or comments via https://bugs.opensuse.org/
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2017-05-27 13:14:35 +02:00
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#
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2022-01-15 14:04:10 +01:00
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%{?!python_module:%define python_module() python3-%{**}}
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2020-02-21 13:35:24 +01:00
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%define skip_python2 1
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2022-01-10 18:36:04 +01:00
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%define plainpython python
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2022-07-12 13:50:45 +02:00
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# upper bound is exclusive: min-numpy_ver <= numpy < max_numpy_ver
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%define min_numpy_ver 1.18
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2022-10-10 14:53:04 +02:00
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%define max_numpy_ver 1.24
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2020-04-06 10:03:09 +02:00
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%global flavor @BUILD_FLAVOR@%{nil}
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%if "%{flavor}" == "test"
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%define psuffix -test
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%bcond_without test
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%else
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%define psuffix %{nil}
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%bcond_with test
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%endif
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Name: python-numba%{psuffix}
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2022-10-03 15:07:28 +02:00
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Version: 0.56.2
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2017-05-27 13:14:35 +02:00
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Release: 0
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2018-10-27 04:31:23 +02:00
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Summary: NumPy-aware optimizing compiler for Python using LLVM
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2017-05-27 13:14:35 +02:00
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License: BSD-2-Clause
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Accepting request 880602 from home:bnavigator:branches:devel:languages:python:numeric
- Update to 0.53.0
* Support for Python 3.9
* Function sub-typing
* Initial support for dynamic gufuncs (i.e. from @guvectorize)
* Parallel Accelerator (@njit(parallel=True) now supports
Fortran ordered arrays
* Full release notes at
https://numba.readthedocs.io/en/0.53.0/release-notes.html
- Don't unpin-llvmlite.patch. It really need to be the correct
version.
- Refresh skip-failing-tests.patch
- Add packaging-ignore-setuptools-deprecation.patch
gh#numba/numba#6837
- Add numba-pr6851-llvm-timings.patch gh#numba/numba#6851 in order
to fix 32-bit issues gh#numba/numba#6832
- Update to 0.52.0
https://numba.readthedocs.io/en/stable/release-notes.html
This release focuses on performance improvements, but also adds
some new features and contains numerous bug fixes and stability
improvements.
Highlights of core performance improvements include:
* Intel kindly sponsored research and development into producing
a new reference count pruning pass. This pass operates at the
LLVM level and can prune a number of common reference counting
patterns. This will improve performance for two primary
reasons:
- There will be less pressure on the atomic locks used to do
the reference counting.
- Removal of reference counting operations permits more
inlining and the optimisation passes can in general do more
with what is present.
(Siu Kwan Lam).
* Intel also sponsored work to improve the performance of the
numba.typed.List container, particularly in the case of
__getitem__ and iteration (Stuart Archibald).
* Superword-level parallelism vectorization is now switched on
and the optimisation pipeline has been lightly analysed and
tuned so as to be able to vectorize more and more often
(Stuart Archibald).
Highlights of core feature changes include:
* The inspect_cfg method on the JIT dispatcher object has been
significantly enhanced and now includes highlighted output and
interleaved line markers and Python source (Stuart Archibald).
* The BSD operating system is now unofficially supported (Stuart
Archibald).
* Numerous features/functionality improvements to NumPy support,
including support for:
- np.asfarray (Guilherme Leobas)
- “subtyping” in record arrays (Lucio Fernandez-Arjona)
- np.split and np.array_split (Isaac Virshup)
- operator.contains with ndarray (@mugoh).
- np.asarray_chkfinite (Rishabh Varshney).
- NumPy 1.19 (Stuart Archibald).
- the ndarray allocators, empty, ones and zeros, accepting a
dtype specified as a string literal (Stuart Archibald).
* Booleans are now supported as literal types (Alexey Kozlov).
* On the CUDA target:
* CUDA 9.0 is now the minimum supported version (Graham Markall).
* Support for Unified Memory has been added (Max Katz).
* Kernel launch overhead is reduced (Graham Markall).
* Cudasim support for mapped array, memcopies and memset has
been * added (Mike Williams).
* Access has been wired in to all libdevice functions (Graham
Markall).
* Additional CUDA atomic operations have been added (Michae
Collison).
* Additional math library functions (frexp, ldexp, isfinite)
(Zhihao * Yuan).
* Support for power on complex numbers (Graham Markall).
Deprecations to note:
* There are no new deprecations. However, note that
“compatibility” mode, which was added some 40 releases ago to
help transition from 0.11 to 0.12+, has been removed! Also,
the shim to permit the import of jitclass from Numba’s top
level namespace has now been removed as per the deprecation
schedule.
- NEP 29: Skip python36 build. Python 3.6 is dropped by NumPy 1.20
OBS-URL: https://build.opensuse.org/request/show/880602
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-numba?expand=0&rev=47
2021-03-22 23:18:24 +01:00
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URL: https://numba.pydata.org/
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2017-05-27 13:14:35 +02:00
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Source: https://files.pythonhosted.org/packages/source/n/numba/numba-%{version}.tar.gz
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Accepting request 880602 from home:bnavigator:branches:devel:languages:python:numeric
- Update to 0.53.0
* Support for Python 3.9
* Function sub-typing
* Initial support for dynamic gufuncs (i.e. from @guvectorize)
* Parallel Accelerator (@njit(parallel=True) now supports
Fortran ordered arrays
* Full release notes at
https://numba.readthedocs.io/en/0.53.0/release-notes.html
- Don't unpin-llvmlite.patch. It really need to be the correct
version.
- Refresh skip-failing-tests.patch
- Add packaging-ignore-setuptools-deprecation.patch
gh#numba/numba#6837
- Add numba-pr6851-llvm-timings.patch gh#numba/numba#6851 in order
to fix 32-bit issues gh#numba/numba#6832
- Update to 0.52.0
https://numba.readthedocs.io/en/stable/release-notes.html
This release focuses on performance improvements, but also adds
some new features and contains numerous bug fixes and stability
improvements.
Highlights of core performance improvements include:
* Intel kindly sponsored research and development into producing
a new reference count pruning pass. This pass operates at the
LLVM level and can prune a number of common reference counting
patterns. This will improve performance for two primary
reasons:
- There will be less pressure on the atomic locks used to do
the reference counting.
- Removal of reference counting operations permits more
inlining and the optimisation passes can in general do more
with what is present.
(Siu Kwan Lam).
* Intel also sponsored work to improve the performance of the
numba.typed.List container, particularly in the case of
__getitem__ and iteration (Stuart Archibald).
* Superword-level parallelism vectorization is now switched on
and the optimisation pipeline has been lightly analysed and
tuned so as to be able to vectorize more and more often
(Stuart Archibald).
Highlights of core feature changes include:
* The inspect_cfg method on the JIT dispatcher object has been
significantly enhanced and now includes highlighted output and
interleaved line markers and Python source (Stuart Archibald).
* The BSD operating system is now unofficially supported (Stuart
Archibald).
* Numerous features/functionality improvements to NumPy support,
including support for:
- np.asfarray (Guilherme Leobas)
- “subtyping” in record arrays (Lucio Fernandez-Arjona)
- np.split and np.array_split (Isaac Virshup)
- operator.contains with ndarray (@mugoh).
- np.asarray_chkfinite (Rishabh Varshney).
- NumPy 1.19 (Stuart Archibald).
- the ndarray allocators, empty, ones and zeros, accepting a
dtype specified as a string literal (Stuart Archibald).
* Booleans are now supported as literal types (Alexey Kozlov).
* On the CUDA target:
* CUDA 9.0 is now the minimum supported version (Graham Markall).
* Support for Unified Memory has been added (Max Katz).
* Kernel launch overhead is reduced (Graham Markall).
* Cudasim support for mapped array, memcopies and memset has
been * added (Mike Williams).
* Access has been wired in to all libdevice functions (Graham
Markall).
* Additional CUDA atomic operations have been added (Michae
Collison).
* Additional math library functions (frexp, ldexp, isfinite)
(Zhihao * Yuan).
* Support for power on complex numbers (Graham Markall).
Deprecations to note:
* There are no new deprecations. However, note that
“compatibility” mode, which was added some 40 releases ago to
help transition from 0.11 to 0.12+, has been removed! Also,
the shim to permit the import of jitclass from Numba’s top
level namespace has now been removed as per the deprecation
schedule.
- NEP 29: Skip python36 build. Python 3.6 is dropped by NumPy 1.20
OBS-URL: https://build.opensuse.org/request/show/880602
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-numba?expand=0&rev=47
2021-03-22 23:18:24 +01:00
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# PATCH-FIX-OPENSUSE skip tests failing due to OBS specifics
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2022-10-03 15:07:28 +02:00
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Patch2: fix-cli-test.patch
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Accepting request 880602 from home:bnavigator:branches:devel:languages:python:numeric
- Update to 0.53.0
* Support for Python 3.9
* Function sub-typing
* Initial support for dynamic gufuncs (i.e. from @guvectorize)
* Parallel Accelerator (@njit(parallel=True) now supports
Fortran ordered arrays
* Full release notes at
https://numba.readthedocs.io/en/0.53.0/release-notes.html
- Don't unpin-llvmlite.patch. It really need to be the correct
version.
- Refresh skip-failing-tests.patch
- Add packaging-ignore-setuptools-deprecation.patch
gh#numba/numba#6837
- Add numba-pr6851-llvm-timings.patch gh#numba/numba#6851 in order
to fix 32-bit issues gh#numba/numba#6832
- Update to 0.52.0
https://numba.readthedocs.io/en/stable/release-notes.html
This release focuses on performance improvements, but also adds
some new features and contains numerous bug fixes and stability
improvements.
Highlights of core performance improvements include:
* Intel kindly sponsored research and development into producing
a new reference count pruning pass. This pass operates at the
LLVM level and can prune a number of common reference counting
patterns. This will improve performance for two primary
reasons:
- There will be less pressure on the atomic locks used to do
the reference counting.
- Removal of reference counting operations permits more
inlining and the optimisation passes can in general do more
with what is present.
(Siu Kwan Lam).
* Intel also sponsored work to improve the performance of the
numba.typed.List container, particularly in the case of
__getitem__ and iteration (Stuart Archibald).
* Superword-level parallelism vectorization is now switched on
and the optimisation pipeline has been lightly analysed and
tuned so as to be able to vectorize more and more often
(Stuart Archibald).
Highlights of core feature changes include:
* The inspect_cfg method on the JIT dispatcher object has been
significantly enhanced and now includes highlighted output and
interleaved line markers and Python source (Stuart Archibald).
* The BSD operating system is now unofficially supported (Stuart
Archibald).
* Numerous features/functionality improvements to NumPy support,
including support for:
- np.asfarray (Guilherme Leobas)
- “subtyping” in record arrays (Lucio Fernandez-Arjona)
- np.split and np.array_split (Isaac Virshup)
- operator.contains with ndarray (@mugoh).
- np.asarray_chkfinite (Rishabh Varshney).
- NumPy 1.19 (Stuart Archibald).
- the ndarray allocators, empty, ones and zeros, accepting a
dtype specified as a string literal (Stuart Archibald).
* Booleans are now supported as literal types (Alexey Kozlov).
* On the CUDA target:
* CUDA 9.0 is now the minimum supported version (Graham Markall).
* Support for Unified Memory has been added (Max Katz).
* Kernel launch overhead is reduced (Graham Markall).
* Cudasim support for mapped array, memcopies and memset has
been * added (Mike Williams).
* Access has been wired in to all libdevice functions (Graham
Markall).
* Additional CUDA atomic operations have been added (Michae
Collison).
* Additional math library functions (frexp, ldexp, isfinite)
(Zhihao * Yuan).
* Support for power on complex numbers (Graham Markall).
Deprecations to note:
* There are no new deprecations. However, note that
“compatibility” mode, which was added some 40 releases ago to
help transition from 0.11 to 0.12+, has been removed! Also,
the shim to permit the import of jitclass from Numba’s top
level namespace has now been removed as per the deprecation
schedule.
- NEP 29: Skip python36 build. Python 3.6 is dropped by NumPy 1.20
OBS-URL: https://build.opensuse.org/request/show/880602
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-numba?expand=0&rev=47
2021-03-22 23:18:24 +01:00
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Patch3: skip-failing-tests.patch
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2022-10-03 15:07:28 +02:00
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# PATCH-FIX-OPENSUSE update-tbb-backend-calls-2021.6.patch, based on gh#numba/numba#7608
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Patch4: update-tbb-backend-calls-2021.6.patch
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2022-01-15 14:04:10 +01:00
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BuildRequires: %{python_module devel >= 3.7}
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2022-07-12 13:50:45 +02:00
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BuildRequires: %{python_module numpy-devel >= %{min_numpy_ver} with %python-numpy-devel < %{max_numpy_ver}}
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2020-04-06 10:03:09 +02:00
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BuildRequires: %{python_module setuptools}
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2017-10-09 19:43:11 +02:00
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BuildRequires: fdupes
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BuildRequires: gcc-c++
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BuildRequires: python-rpm-macros
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2022-10-03 15:07:28 +02:00
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BuildRequires: (tbb-devel >= 2021)
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Requires: (python-llvmlite >= 0.39 with python-llvmlite < 0.40)
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2022-07-12 13:50:45 +02:00
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Requires: (python-numpy >= %{min_numpy_ver} with python-numpy < %{max_numpy_ver})
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2019-12-17 23:30:55 +01:00
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Requires(post): update-alternatives
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2022-07-12 13:50:45 +02:00
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Requires(postun):update-alternatives
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2019-10-24 02:59:28 +02:00
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Recommends: python-Jinja2
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Recommends: python-Pygments
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Recommends: python-cffi
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2022-01-15 14:04:10 +01:00
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Recommends: python-scipy > 1.0
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2019-10-24 02:59:28 +02:00
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Recommends: python-tbb
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2020-04-27 21:40:16 +02:00
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%if %{with test}
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BuildRequires: %{python_module Jinja2}
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BuildRequires: %{python_module PyYAML}
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BuildRequires: %{python_module Pygments}
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BuildRequires: %{python_module cffi}
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BuildRequires: %{python_module ipython}
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2022-07-12 13:50:45 +02:00
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BuildRequires: %{python_module numba = %{version}}
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BuildRequires: %{python_module numba-devel = %{version}}
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2020-11-03 14:11:54 +01:00
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BuildRequires: %{python_module pip}
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Accepting request 880602 from home:bnavigator:branches:devel:languages:python:numeric
- Update to 0.53.0
* Support for Python 3.9
* Function sub-typing
* Initial support for dynamic gufuncs (i.e. from @guvectorize)
* Parallel Accelerator (@njit(parallel=True) now supports
Fortran ordered arrays
* Full release notes at
https://numba.readthedocs.io/en/0.53.0/release-notes.html
- Don't unpin-llvmlite.patch. It really need to be the correct
version.
- Refresh skip-failing-tests.patch
- Add packaging-ignore-setuptools-deprecation.patch
gh#numba/numba#6837
- Add numba-pr6851-llvm-timings.patch gh#numba/numba#6851 in order
to fix 32-bit issues gh#numba/numba#6832
- Update to 0.52.0
https://numba.readthedocs.io/en/stable/release-notes.html
This release focuses on performance improvements, but also adds
some new features and contains numerous bug fixes and stability
improvements.
Highlights of core performance improvements include:
* Intel kindly sponsored research and development into producing
a new reference count pruning pass. This pass operates at the
LLVM level and can prune a number of common reference counting
patterns. This will improve performance for two primary
reasons:
- There will be less pressure on the atomic locks used to do
the reference counting.
- Removal of reference counting operations permits more
inlining and the optimisation passes can in general do more
with what is present.
(Siu Kwan Lam).
* Intel also sponsored work to improve the performance of the
numba.typed.List container, particularly in the case of
__getitem__ and iteration (Stuart Archibald).
* Superword-level parallelism vectorization is now switched on
and the optimisation pipeline has been lightly analysed and
tuned so as to be able to vectorize more and more often
(Stuart Archibald).
Highlights of core feature changes include:
* The inspect_cfg method on the JIT dispatcher object has been
significantly enhanced and now includes highlighted output and
interleaved line markers and Python source (Stuart Archibald).
* The BSD operating system is now unofficially supported (Stuart
Archibald).
* Numerous features/functionality improvements to NumPy support,
including support for:
- np.asfarray (Guilherme Leobas)
- “subtyping” in record arrays (Lucio Fernandez-Arjona)
- np.split and np.array_split (Isaac Virshup)
- operator.contains with ndarray (@mugoh).
- np.asarray_chkfinite (Rishabh Varshney).
- NumPy 1.19 (Stuart Archibald).
- the ndarray allocators, empty, ones and zeros, accepting a
dtype specified as a string literal (Stuart Archibald).
* Booleans are now supported as literal types (Alexey Kozlov).
* On the CUDA target:
* CUDA 9.0 is now the minimum supported version (Graham Markall).
* Support for Unified Memory has been added (Max Katz).
* Kernel launch overhead is reduced (Graham Markall).
* Cudasim support for mapped array, memcopies and memset has
been * added (Mike Williams).
* Access has been wired in to all libdevice functions (Graham
Markall).
* Additional CUDA atomic operations have been added (Michae
Collison).
* Additional math library functions (frexp, ldexp, isfinite)
(Zhihao * Yuan).
* Support for power on complex numbers (Graham Markall).
Deprecations to note:
* There are no new deprecations. However, note that
“compatibility” mode, which was added some 40 releases ago to
help transition from 0.11 to 0.12+, has been removed! Also,
the shim to permit the import of jitclass from Numba’s top
level namespace has now been removed as per the deprecation
schedule.
- NEP 29: Skip python36 build. Python 3.6 is dropped by NumPy 1.20
OBS-URL: https://build.opensuse.org/request/show/880602
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-numba?expand=0&rev=47
2021-03-22 23:18:24 +01:00
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BuildRequires: %{python_module psutil}
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2020-04-27 21:40:16 +02:00
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BuildRequires: %{python_module pytest}
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2022-01-15 14:04:10 +01:00
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BuildRequires: %{python_module scipy >= 1.0}
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2020-04-27 21:40:16 +02:00
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BuildRequires: %{python_module tbb}
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%endif
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2022-01-31 21:44:49 +01:00
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# Tests fail on ppc64 big endian, not resolvable on s390x
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# Supported Platforms: https://numba.pydata.org/numba-doc/dev/user/installing.html#compatibility
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ExclusiveArch: x86_64 %ix86 ppc64le %arm aarch64
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2017-05-27 13:14:35 +02:00
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%python_subpackages
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%description
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Numba is a NumPy-aware optimizing compiler for Python. It uses the
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LLVM compiler infrastructure to compile Python syntax to
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machine code.
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It is aware of NumPy arrays as typed memory regions and so can speed-up
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code using NumPy arrays. Other, less well-typed code will be translated
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to Python C-API calls, effectively removing the "interpreter", but not removing
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the dynamic indirection.
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Numba is also not a tracing JIT. It *compiles* your code before it gets
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run, either using run-time type information or type information you provide
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in the decorator.
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Numba is a mechanism for producing machine code from Python syntax and typed
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data structures such as those that exist in NumPy.
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2019-10-24 02:59:28 +02:00
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%package devel
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2017-05-27 13:14:35 +02:00
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Summary: Development files for numba applications
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Requires: %{name} = %{version}
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Requires: python-devel
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2022-01-10 18:36:04 +01:00
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Requires: %{plainpython}(abi) = %{python_version}
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2022-07-12 13:50:45 +02:00
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Requires: (python-numpy-devel >= %{min_numpy_ver} with python-numpy-devel < %{max_numpy_ver})
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2017-05-27 13:14:35 +02:00
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2019-10-24 02:59:28 +02:00
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%description devel
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2017-05-27 13:14:35 +02:00
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This package contains files for developing applications using numba.
|
|
|
|
|
|
|
|
|
|
%prep
|
2022-01-15 14:04:10 +01:00
|
|
|
%autosetup -p1 -n numba-%{version}
|
|
|
|
|
sed -i -e '1{/env python/ d}' numba/misc/appdirs.py
|
2017-05-27 13:14:35 +02:00
|
|
|
|
2022-01-15 14:04:10 +01:00
|
|
|
# random timeouts in OBS
|
2020-04-06 12:03:22 +02:00
|
|
|
rm numba/tests/test_typedlist.py
|
Accepting request 880602 from home:bnavigator:branches:devel:languages:python:numeric
- Update to 0.53.0
* Support for Python 3.9
* Function sub-typing
* Initial support for dynamic gufuncs (i.e. from @guvectorize)
* Parallel Accelerator (@njit(parallel=True) now supports
Fortran ordered arrays
* Full release notes at
https://numba.readthedocs.io/en/0.53.0/release-notes.html
- Don't unpin-llvmlite.patch. It really need to be the correct
version.
- Refresh skip-failing-tests.patch
- Add packaging-ignore-setuptools-deprecation.patch
gh#numba/numba#6837
- Add numba-pr6851-llvm-timings.patch gh#numba/numba#6851 in order
to fix 32-bit issues gh#numba/numba#6832
- Update to 0.52.0
https://numba.readthedocs.io/en/stable/release-notes.html
This release focuses on performance improvements, but also adds
some new features and contains numerous bug fixes and stability
improvements.
Highlights of core performance improvements include:
* Intel kindly sponsored research and development into producing
a new reference count pruning pass. This pass operates at the
LLVM level and can prune a number of common reference counting
patterns. This will improve performance for two primary
reasons:
- There will be less pressure on the atomic locks used to do
the reference counting.
- Removal of reference counting operations permits more
inlining and the optimisation passes can in general do more
with what is present.
(Siu Kwan Lam).
* Intel also sponsored work to improve the performance of the
numba.typed.List container, particularly in the case of
__getitem__ and iteration (Stuart Archibald).
* Superword-level parallelism vectorization is now switched on
and the optimisation pipeline has been lightly analysed and
tuned so as to be able to vectorize more and more often
(Stuart Archibald).
Highlights of core feature changes include:
* The inspect_cfg method on the JIT dispatcher object has been
significantly enhanced and now includes highlighted output and
interleaved line markers and Python source (Stuart Archibald).
* The BSD operating system is now unofficially supported (Stuart
Archibald).
* Numerous features/functionality improvements to NumPy support,
including support for:
- np.asfarray (Guilherme Leobas)
- “subtyping” in record arrays (Lucio Fernandez-Arjona)
- np.split and np.array_split (Isaac Virshup)
- operator.contains with ndarray (@mugoh).
- np.asarray_chkfinite (Rishabh Varshney).
- NumPy 1.19 (Stuart Archibald).
- the ndarray allocators, empty, ones and zeros, accepting a
dtype specified as a string literal (Stuart Archibald).
* Booleans are now supported as literal types (Alexey Kozlov).
* On the CUDA target:
* CUDA 9.0 is now the minimum supported version (Graham Markall).
* Support for Unified Memory has been added (Max Katz).
* Kernel launch overhead is reduced (Graham Markall).
* Cudasim support for mapped array, memcopies and memset has
been * added (Mike Williams).
* Access has been wired in to all libdevice functions (Graham
Markall).
* Additional CUDA atomic operations have been added (Michae
Collison).
* Additional math library functions (frexp, ldexp, isfinite)
(Zhihao * Yuan).
* Support for power on complex numbers (Graham Markall).
Deprecations to note:
* There are no new deprecations. However, note that
“compatibility” mode, which was added some 40 releases ago to
help transition from 0.11 to 0.12+, has been removed! Also,
the shim to permit the import of jitclass from Numba’s top
level namespace has now been removed as per the deprecation
schedule.
- NEP 29: Skip python36 build. Python 3.6 is dropped by NumPy 1.20
OBS-URL: https://build.opensuse.org/request/show/880602
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-numba?expand=0&rev=47
2021-03-22 23:18:24 +01:00
|
|
|
# if we reduced the amount of tests too much:
|
2021-11-18 21:40:38 +01:00
|
|
|
# sed -i -e '/check_testsuite_size/ s/5000/3000/' numba/tests/test_runtests.py
|
2020-04-06 11:06:45 +02:00
|
|
|
|
2017-05-27 13:14:35 +02:00
|
|
|
%build
|
2021-11-18 21:40:38 +01:00
|
|
|
%if !%{with test}
|
2019-10-24 02:59:28 +02:00
|
|
|
export CFLAGS="%{optflags} -fPIC"
|
2017-05-27 13:14:35 +02:00
|
|
|
%python_build
|
2021-11-18 21:40:38 +01:00
|
|
|
%endif
|
2017-05-27 13:14:35 +02:00
|
|
|
|
|
|
|
|
%install
|
2020-04-06 10:03:09 +02:00
|
|
|
%if !%{with test}
|
2017-05-27 13:14:35 +02:00
|
|
|
%python_install
|
Accepting request 880602 from home:bnavigator:branches:devel:languages:python:numeric
- Update to 0.53.0
* Support for Python 3.9
* Function sub-typing
* Initial support for dynamic gufuncs (i.e. from @guvectorize)
* Parallel Accelerator (@njit(parallel=True) now supports
Fortran ordered arrays
* Full release notes at
https://numba.readthedocs.io/en/0.53.0/release-notes.html
- Don't unpin-llvmlite.patch. It really need to be the correct
version.
- Refresh skip-failing-tests.patch
- Add packaging-ignore-setuptools-deprecation.patch
gh#numba/numba#6837
- Add numba-pr6851-llvm-timings.patch gh#numba/numba#6851 in order
to fix 32-bit issues gh#numba/numba#6832
- Update to 0.52.0
https://numba.readthedocs.io/en/stable/release-notes.html
This release focuses on performance improvements, but also adds
some new features and contains numerous bug fixes and stability
improvements.
Highlights of core performance improvements include:
* Intel kindly sponsored research and development into producing
a new reference count pruning pass. This pass operates at the
LLVM level and can prune a number of common reference counting
patterns. This will improve performance for two primary
reasons:
- There will be less pressure on the atomic locks used to do
the reference counting.
- Removal of reference counting operations permits more
inlining and the optimisation passes can in general do more
with what is present.
(Siu Kwan Lam).
* Intel also sponsored work to improve the performance of the
numba.typed.List container, particularly in the case of
__getitem__ and iteration (Stuart Archibald).
* Superword-level parallelism vectorization is now switched on
and the optimisation pipeline has been lightly analysed and
tuned so as to be able to vectorize more and more often
(Stuart Archibald).
Highlights of core feature changes include:
* The inspect_cfg method on the JIT dispatcher object has been
significantly enhanced and now includes highlighted output and
interleaved line markers and Python source (Stuart Archibald).
* The BSD operating system is now unofficially supported (Stuart
Archibald).
* Numerous features/functionality improvements to NumPy support,
including support for:
- np.asfarray (Guilherme Leobas)
- “subtyping” in record arrays (Lucio Fernandez-Arjona)
- np.split and np.array_split (Isaac Virshup)
- operator.contains with ndarray (@mugoh).
- np.asarray_chkfinite (Rishabh Varshney).
- NumPy 1.19 (Stuart Archibald).
- the ndarray allocators, empty, ones and zeros, accepting a
dtype specified as a string literal (Stuart Archibald).
* Booleans are now supported as literal types (Alexey Kozlov).
* On the CUDA target:
* CUDA 9.0 is now the minimum supported version (Graham Markall).
* Support for Unified Memory has been added (Max Katz).
* Kernel launch overhead is reduced (Graham Markall).
* Cudasim support for mapped array, memcopies and memset has
been * added (Mike Williams).
* Access has been wired in to all libdevice functions (Graham
Markall).
* Additional CUDA atomic operations have been added (Michae
Collison).
* Additional math library functions (frexp, ldexp, isfinite)
(Zhihao * Yuan).
* Support for power on complex numbers (Graham Markall).
Deprecations to note:
* There are no new deprecations. However, note that
“compatibility” mode, which was added some 40 releases ago to
help transition from 0.11 to 0.12+, has been removed! Also,
the shim to permit the import of jitclass from Numba’s top
level namespace has now been removed as per the deprecation
schedule.
- NEP 29: Skip python36 build. Python 3.6 is dropped by NumPy 1.20
OBS-URL: https://build.opensuse.org/request/show/880602
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-numba?expand=0&rev=47
2021-03-22 23:18:24 +01:00
|
|
|
%{python_expand #
|
|
|
|
|
%fdupes %{buildroot}%{$python_sitearch}
|
|
|
|
|
find %{buildroot}%{$python_sitearch} -name '*.[ch]' > devel-files0-%{$python_bin_suffix}.files
|
|
|
|
|
sed 's|^%{buildroot}||' devel-files0-%{$python_bin_suffix}.files > devel-files-%{$python_bin_suffix}.files
|
|
|
|
|
sed 's|^%{buildroot}|%%exclude |' devel-files0-%{$python_bin_suffix}.files > devel-files-exclude-%{$python_bin_suffix}.files
|
|
|
|
|
}
|
2017-05-27 13:14:35 +02:00
|
|
|
|
|
|
|
|
%python_clone -a %{buildroot}%{_bindir}/numba
|
|
|
|
|
%python_clone -a %{buildroot}%{_bindir}/pycc
|
2020-04-06 10:03:09 +02:00
|
|
|
%endif
|
2017-05-27 13:14:35 +02:00
|
|
|
|
2018-05-29 10:29:51 +02:00
|
|
|
%check
|
2020-04-06 10:03:09 +02:00
|
|
|
%if %{with test}
|
2022-01-15 14:04:10 +01:00
|
|
|
# test the installed package, not the source without compiled modules
|
2022-10-10 14:53:04 +02:00
|
|
|
mkdir emptytestdir
|
|
|
|
|
pushd emptytestdir
|
2022-01-15 14:04:10 +01:00
|
|
|
%{python_expand # numbatests: check specific tests with `osc build -M test --define="numbatests <testnames>"`
|
2020-04-06 10:08:48 +02:00
|
|
|
%{_bindir}/numba-%{$python_bin_suffix} -s
|
2022-01-15 14:04:10 +01:00
|
|
|
$python -m numba.runtests -v -b --exclude-tags='long_running' -m %{_smp_build_ncpus} -- %{?!numbatests:numba.tests}%{?numbatests}
|
2019-03-20 11:23:52 +01:00
|
|
|
}
|
2022-01-15 14:04:10 +01:00
|
|
|
popd
|
2020-04-06 10:03:09 +02:00
|
|
|
%endif
|
2018-05-29 10:29:51 +02:00
|
|
|
|
2020-04-06 10:03:09 +02:00
|
|
|
%if !%{with test}
|
2017-05-27 13:14:35 +02:00
|
|
|
%post
|
2022-07-12 13:50:45 +02:00
|
|
|
%python_install_alternative numba pycc
|
2017-05-27 13:14:35 +02:00
|
|
|
|
2021-11-18 21:40:38 +01:00
|
|
|
%postun
|
2017-05-27 13:14:35 +02:00
|
|
|
%python_uninstall_alternative numba
|
|
|
|
|
|
Accepting request 880602 from home:bnavigator:branches:devel:languages:python:numeric
- Update to 0.53.0
* Support for Python 3.9
* Function sub-typing
* Initial support for dynamic gufuncs (i.e. from @guvectorize)
* Parallel Accelerator (@njit(parallel=True) now supports
Fortran ordered arrays
* Full release notes at
https://numba.readthedocs.io/en/0.53.0/release-notes.html
- Don't unpin-llvmlite.patch. It really need to be the correct
version.
- Refresh skip-failing-tests.patch
- Add packaging-ignore-setuptools-deprecation.patch
gh#numba/numba#6837
- Add numba-pr6851-llvm-timings.patch gh#numba/numba#6851 in order
to fix 32-bit issues gh#numba/numba#6832
- Update to 0.52.0
https://numba.readthedocs.io/en/stable/release-notes.html
This release focuses on performance improvements, but also adds
some new features and contains numerous bug fixes and stability
improvements.
Highlights of core performance improvements include:
* Intel kindly sponsored research and development into producing
a new reference count pruning pass. This pass operates at the
LLVM level and can prune a number of common reference counting
patterns. This will improve performance for two primary
reasons:
- There will be less pressure on the atomic locks used to do
the reference counting.
- Removal of reference counting operations permits more
inlining and the optimisation passes can in general do more
with what is present.
(Siu Kwan Lam).
* Intel also sponsored work to improve the performance of the
numba.typed.List container, particularly in the case of
__getitem__ and iteration (Stuart Archibald).
* Superword-level parallelism vectorization is now switched on
and the optimisation pipeline has been lightly analysed and
tuned so as to be able to vectorize more and more often
(Stuart Archibald).
Highlights of core feature changes include:
* The inspect_cfg method on the JIT dispatcher object has been
significantly enhanced and now includes highlighted output and
interleaved line markers and Python source (Stuart Archibald).
* The BSD operating system is now unofficially supported (Stuart
Archibald).
* Numerous features/functionality improvements to NumPy support,
including support for:
- np.asfarray (Guilherme Leobas)
- “subtyping” in record arrays (Lucio Fernandez-Arjona)
- np.split and np.array_split (Isaac Virshup)
- operator.contains with ndarray (@mugoh).
- np.asarray_chkfinite (Rishabh Varshney).
- NumPy 1.19 (Stuart Archibald).
- the ndarray allocators, empty, ones and zeros, accepting a
dtype specified as a string literal (Stuart Archibald).
* Booleans are now supported as literal types (Alexey Kozlov).
* On the CUDA target:
* CUDA 9.0 is now the minimum supported version (Graham Markall).
* Support for Unified Memory has been added (Max Katz).
* Kernel launch overhead is reduced (Graham Markall).
* Cudasim support for mapped array, memcopies and memset has
been * added (Mike Williams).
* Access has been wired in to all libdevice functions (Graham
Markall).
* Additional CUDA atomic operations have been added (Michae
Collison).
* Additional math library functions (frexp, ldexp, isfinite)
(Zhihao * Yuan).
* Support for power on complex numbers (Graham Markall).
Deprecations to note:
* There are no new deprecations. However, note that
“compatibility” mode, which was added some 40 releases ago to
help transition from 0.11 to 0.12+, has been removed! Also,
the shim to permit the import of jitclass from Numba’s top
level namespace has now been removed as per the deprecation
schedule.
- NEP 29: Skip python36 build. Python 3.6 is dropped by NumPy 1.20
OBS-URL: https://build.opensuse.org/request/show/880602
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-numba?expand=0&rev=47
2021-03-22 23:18:24 +01:00
|
|
|
%files %{python_files} -f devel-files-exclude-%{python_bin_suffix}.files
|
2018-05-16 11:39:35 +02:00
|
|
|
%license LICENSE
|
2020-04-27 21:40:16 +02:00
|
|
|
%doc CHANGE_LOG README.rst
|
2017-05-27 13:14:35 +02:00
|
|
|
%python_alternative %{_bindir}/numba
|
|
|
|
|
%python_alternative %{_bindir}/pycc
|
|
|
|
|
%{python_sitearch}/numba/
|
|
|
|
|
%{python_sitearch}/numba-%{version}-py*.egg-info
|
Accepting request 880602 from home:bnavigator:branches:devel:languages:python:numeric
- Update to 0.53.0
* Support for Python 3.9
* Function sub-typing
* Initial support for dynamic gufuncs (i.e. from @guvectorize)
* Parallel Accelerator (@njit(parallel=True) now supports
Fortran ordered arrays
* Full release notes at
https://numba.readthedocs.io/en/0.53.0/release-notes.html
- Don't unpin-llvmlite.patch. It really need to be the correct
version.
- Refresh skip-failing-tests.patch
- Add packaging-ignore-setuptools-deprecation.patch
gh#numba/numba#6837
- Add numba-pr6851-llvm-timings.patch gh#numba/numba#6851 in order
to fix 32-bit issues gh#numba/numba#6832
- Update to 0.52.0
https://numba.readthedocs.io/en/stable/release-notes.html
This release focuses on performance improvements, but also adds
some new features and contains numerous bug fixes and stability
improvements.
Highlights of core performance improvements include:
* Intel kindly sponsored research and development into producing
a new reference count pruning pass. This pass operates at the
LLVM level and can prune a number of common reference counting
patterns. This will improve performance for two primary
reasons:
- There will be less pressure on the atomic locks used to do
the reference counting.
- Removal of reference counting operations permits more
inlining and the optimisation passes can in general do more
with what is present.
(Siu Kwan Lam).
* Intel also sponsored work to improve the performance of the
numba.typed.List container, particularly in the case of
__getitem__ and iteration (Stuart Archibald).
* Superword-level parallelism vectorization is now switched on
and the optimisation pipeline has been lightly analysed and
tuned so as to be able to vectorize more and more often
(Stuart Archibald).
Highlights of core feature changes include:
* The inspect_cfg method on the JIT dispatcher object has been
significantly enhanced and now includes highlighted output and
interleaved line markers and Python source (Stuart Archibald).
* The BSD operating system is now unofficially supported (Stuart
Archibald).
* Numerous features/functionality improvements to NumPy support,
including support for:
- np.asfarray (Guilherme Leobas)
- “subtyping” in record arrays (Lucio Fernandez-Arjona)
- np.split and np.array_split (Isaac Virshup)
- operator.contains with ndarray (@mugoh).
- np.asarray_chkfinite (Rishabh Varshney).
- NumPy 1.19 (Stuart Archibald).
- the ndarray allocators, empty, ones and zeros, accepting a
dtype specified as a string literal (Stuart Archibald).
* Booleans are now supported as literal types (Alexey Kozlov).
* On the CUDA target:
* CUDA 9.0 is now the minimum supported version (Graham Markall).
* Support for Unified Memory has been added (Max Katz).
* Kernel launch overhead is reduced (Graham Markall).
* Cudasim support for mapped array, memcopies and memset has
been * added (Mike Williams).
* Access has been wired in to all libdevice functions (Graham
Markall).
* Additional CUDA atomic operations have been added (Michae
Collison).
* Additional math library functions (frexp, ldexp, isfinite)
(Zhihao * Yuan).
* Support for power on complex numbers (Graham Markall).
Deprecations to note:
* There are no new deprecations. However, note that
“compatibility” mode, which was added some 40 releases ago to
help transition from 0.11 to 0.12+, has been removed! Also,
the shim to permit the import of jitclass from Numba’s top
level namespace has now been removed as per the deprecation
schedule.
- NEP 29: Skip python36 build. Python 3.6 is dropped by NumPy 1.20
OBS-URL: https://build.opensuse.org/request/show/880602
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-numba?expand=0&rev=47
2021-03-22 23:18:24 +01:00
|
|
|
|
2022-10-10 14:53:04 +02:00
|
|
|
%files %{python_files devel} -f devel-files-%{python_bin_suffix}.files
|
Accepting request 880602 from home:bnavigator:branches:devel:languages:python:numeric
- Update to 0.53.0
* Support for Python 3.9
* Function sub-typing
* Initial support for dynamic gufuncs (i.e. from @guvectorize)
* Parallel Accelerator (@njit(parallel=True) now supports
Fortran ordered arrays
* Full release notes at
https://numba.readthedocs.io/en/0.53.0/release-notes.html
- Don't unpin-llvmlite.patch. It really need to be the correct
version.
- Refresh skip-failing-tests.patch
- Add packaging-ignore-setuptools-deprecation.patch
gh#numba/numba#6837
- Add numba-pr6851-llvm-timings.patch gh#numba/numba#6851 in order
to fix 32-bit issues gh#numba/numba#6832
- Update to 0.52.0
https://numba.readthedocs.io/en/stable/release-notes.html
This release focuses on performance improvements, but also adds
some new features and contains numerous bug fixes and stability
improvements.
Highlights of core performance improvements include:
* Intel kindly sponsored research and development into producing
a new reference count pruning pass. This pass operates at the
LLVM level and can prune a number of common reference counting
patterns. This will improve performance for two primary
reasons:
- There will be less pressure on the atomic locks used to do
the reference counting.
- Removal of reference counting operations permits more
inlining and the optimisation passes can in general do more
with what is present.
(Siu Kwan Lam).
* Intel also sponsored work to improve the performance of the
numba.typed.List container, particularly in the case of
__getitem__ and iteration (Stuart Archibald).
* Superword-level parallelism vectorization is now switched on
and the optimisation pipeline has been lightly analysed and
tuned so as to be able to vectorize more and more often
(Stuart Archibald).
Highlights of core feature changes include:
* The inspect_cfg method on the JIT dispatcher object has been
significantly enhanced and now includes highlighted output and
interleaved line markers and Python source (Stuart Archibald).
* The BSD operating system is now unofficially supported (Stuart
Archibald).
* Numerous features/functionality improvements to NumPy support,
including support for:
- np.asfarray (Guilherme Leobas)
- “subtyping” in record arrays (Lucio Fernandez-Arjona)
- np.split and np.array_split (Isaac Virshup)
- operator.contains with ndarray (@mugoh).
- np.asarray_chkfinite (Rishabh Varshney).
- NumPy 1.19 (Stuart Archibald).
- the ndarray allocators, empty, ones and zeros, accepting a
dtype specified as a string literal (Stuart Archibald).
* Booleans are now supported as literal types (Alexey Kozlov).
* On the CUDA target:
* CUDA 9.0 is now the minimum supported version (Graham Markall).
* Support for Unified Memory has been added (Max Katz).
* Kernel launch overhead is reduced (Graham Markall).
* Cudasim support for mapped array, memcopies and memset has
been * added (Mike Williams).
* Access has been wired in to all libdevice functions (Graham
Markall).
* Additional CUDA atomic operations have been added (Michae
Collison).
* Additional math library functions (frexp, ldexp, isfinite)
(Zhihao * Yuan).
* Support for power on complex numbers (Graham Markall).
Deprecations to note:
* There are no new deprecations. However, note that
“compatibility” mode, which was added some 40 releases ago to
help transition from 0.11 to 0.12+, has been removed! Also,
the shim to permit the import of jitclass from Numba’s top
level namespace has now been removed as per the deprecation
schedule.
- NEP 29: Skip python36 build. Python 3.6 is dropped by NumPy 1.20
OBS-URL: https://build.opensuse.org/request/show/880602
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-numba?expand=0&rev=47
2021-03-22 23:18:24 +01:00
|
|
|
%license LICENSE
|
2020-04-06 10:03:09 +02:00
|
|
|
%endif
|
2017-05-27 13:14:35 +02:00
|
|
|
|
|
|
|
|
%changelog
|
