python311-numba-0.60.0-1.2 RPM for i586

From OpenSuSE Ports Tumbleweed for i586

Numba is a NumPy-aware optimizing compiler for Python. It uses the
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.

Provides

• python311-numba
• python3-numba
• python3.11dist(numba)
• python311-numba(x86-32)
• python3dist(numba)

Requires

• /bin/sh
• /bin/sh
• /usr/bin/python3.11
• ld-linux.so.2
• ld-linux.so.2(GLIBC_2.3)
• libc.so.6
• libc.so.6(GLIBC_2.0)
• libc.so.6(GLIBC_2.1)
• libc.so.6(GLIBC_2.1.3)
• libc.so.6(GLIBC_2.3.2)
• libc.so.6(GLIBC_2.3.4)
• libc.so.6(GLIBC_2.34)
• libc.so.6(GLIBC_2.4)
• libgcc_s.so.1
• libgcc_s.so.1(GCC_3.0)
• libgcc_s.so.1(GCC_3.3.1)
• libgomp.so.1
• libgomp.so.1(GOMP_4.0)
• libgomp.so.1(OMP_1.0)
• libm.so.6
• libm.so.6(GLIBC_2.0)
• libm.so.6(GLIBC_2.1)
• libm.so.6(GLIBC_2.23)
• libm.so.6(GLIBC_2.27)
• libm.so.6(GLIBC_2.29)
• libstdc++.so.6
• libstdc++.so.6(CXXABI_1.3)
• libstdc++.so.6(CXXABI_1.3.15)
• libstdc++.so.6(CXXABI_1.3.8)
• libstdc++.so.6(CXXABI_1.3.9)
• libstdc++.so.6(GLIBCXX_3.4)
• libstdc++.so.6(GLIBCXX_3.4.29)
• libstdc++.so.6(GLIBCXX_3.4.32)
• libtbb.so.12
• python(abi) = 3.11
• rpmlib(CompressedFileNames) <= 3.0.4-1
• rpmlib(FileDigests) <= 4.6.0-1
• rpmlib(PartialHardlinkSets) <= 4.0.4-1
• rpmlib(PayloadFilesHavePrefix) <= 4.0-1
• rpmlib(PayloadIsZstd) <= 5.4.18-1
• rpmlib(RichDependencies) <= 4.12.0-1
• update-alternatives
• update-alternatives

License

BSD-2-Clause

Changelog

* Mon Jul 01 2024 Steve Kowalik <steven.kowalik@suse.com>
  - Update to 0.60.0:
    * NumPy 2.0 Binary Support
    * New Features
      + IEnhance guvectorize support in JIT code
      + IAdd experimental support for ufunc.at
      + IAdd float(<string literal>) ctor
      + IAdd support for math.log2.
      + IAdd math.nextafter support for nopython mode.
      + IAdd support for parfor binop reductions.
    * Improvements
      + Expand isinstance() support for NumPy datetime types
      + Python 3.12 sys.monitoring support is added to Numba's dispatcher.
    * NumPy Support
      + Added support for np.size()
    * CUDA API Changes
      + Support for compilation to LTO-IR
      + Support math.log, math.log2 and math.log10 in CUDA
    * Bug Fixes
      + Fix parfor variable hoisting analysis.
* Tue May 28 2024 Daniel Garcia <daniel.garcia@suse.com>
  - Skip broken test on ppc64le
    bsc#1225394, gh#numba/numba#8489
* Fri Mar 22 2024 Dirk Müller <dmueller@suse.com>
  - update to 0.59.1:
    * Fixed caching of kernels that use target-specific overloads
    * Fixed a performance regression introduced in Numba 0.59 which
      made ``np.searchsorted`` considerably slower.
    * This patch fixes two issues with ``np.searchsorted``. First,
      a regression is fixed in the support of ``np.datetime64``.
      Second, adopt ``NAT``-aware  comparisons to fix mishandling
      of ``NAT`` value.
    * Allow use of Python 3.12 PEP-695 type parameter syntax
* Fri Mar 08 2024 Ben Greiner <code@bnavigator.de>
  - Stop testing python39: dropped since ipython 8.19
* Wed Feb 21 2024 Ben Greiner <code@bnavigator.de>
  - Simplify test flavor logic
  - Prepare for python39 flavor drop: Exclude build in empty test
    flavors
  - Don't test on 32bit-platforms
* Sat Feb 03 2024 Dirk Müller <dmueller@suse.com>
  - update to 0.59.0
    * Python 3.12 support
    * minimum supported version to 3.9
    * Add support for ufunc attributes and reduce
    * Add a config variable to enable / disable the llvmlite memory
      manager
    * see https://numba.readthedocs.io/en/stable/release/0.59.0-notes.html#highlights
* Mon Nov 20 2023 Markéta Machová <mmachova@suse.com>
  - Update to 0.58.1
    * Added towncrier
    * The minimum supported NumPy version is 1.22.
    * Add support for NumPy 1.26
    * Remove NVVM 3.4 and CTK 11.0 / 11.1 support
    * Removal of Windows 32-bit Support
    * The minimum llvmlite version is now 0.41.0.
    * Added RVSDG-frontend
  - Drop merged patches:
    * numba-pr9105-np1.25.patch
    * multiprocessing-context.patch
* Tue Sep 19 2023 Markéta Machová <mmachova@suse.com>
  - Add multiprocessing-context.patch fixing tests for Python 3.11.5
* Mon Aug 21 2023 Ben Greiner <code@bnavigator.de>
  - Add numba-pr9105-np1.25.patch, raise (reintroduced) numpy pin
    * gh#numba/numba#9105
    * Adapted gh#numba/numba#9138
* Mon Aug 14 2023 Dirk Müller <dmueller@suse.com>
  - update to 0.57.1:
    * fix regressions with 0.57.0
  - remove upper bound on numpy - upstream does not have it either
* Fri May 26 2023 Steve Kowalik <steven.kowalik@suse.com>
  - Update to 0.57.0:
    * Support for Python 3.11 (minimum is moved to 3.8)
    * Support for NumPy 1.24 (minimum is moved to 1.21)
    * Python language support enhancements:
      + Exception classes now support arguments that are not compile time
      constant.
      + The built-in functions hasattr and getattr are supported for compile
      time constant attributes.
      + The built-in functions str and repr are now implemented similarly to
      their Python implementations. Custom __str__ and __repr__ functions
      can be associated with types and work as expected.
      + Numba’s unicode functionality in str.startswith now supports kwargs
      start and end.
      + min and max now support boolean types.
      + Support is added for the dict(iterable) constructor.
  - Dropped patches:
    * numba-pr8620-np1.24.patch
    * update-tbb-backend-calls-2021.6.patch
  - Rebased existing patch.
* Wed Apr 12 2023 Steve Kowalik <steven.kowalik@suse.com>
  - Clean up leftover Python 3.8 gubbins, look forward to Python 3.11 support.
* Tue Apr 11 2023 Dominique Leuenberger <dimstar@opensuse.org>
  - Remove test-py38 flavor from multibuild: Python 3.8 is no longer
    supported.
* Tue Jan 03 2023 Ben Greiner <code@bnavigator.de>
  - Split out python flavors into testing multibuilds. Depending on
    the obs worker, the test suite can take almost an hour per
    flavor.
  - Replace allow-numpy-1.24.patch with an updated
    numba-pr8620-np1.24.patch to also work with still present numpy
    1.23 in Factory (discussed upstream in gh#numba/numba#8620)
  - Merge fix-cli-test.patch into skip-failing-tests.patch
* Mon Jan 02 2023 Ben Greiner <code@bnavigator.de>
  - Clean up the specfile
    * restore the multibuild
    * Patch allow-numpy-1.24.patch is the WIP gh#numba/numba#8620
* Sun Jan 01 2023 Matej Cepl <mcepl@suse.com>
  - Update to 0.56.4:
    - This is a bugfix release to fix a regression in the CUDA
      target in relation to the .view() method on CUDA device
      arrays that is present when using NumPy version 1.23.0 or
      later.
    - This is a bugfix release to remove the version restriction
      applied to the setuptools package and to fix a bug in the
      CUDA target in relation to copying zero length device arrays
      to zero length host arrays.
  - Add allow-numpy-1.24.patch to allow work with numpy 1.24
* Mon Oct 10 2022 John Vandenberg <jayvdb@gmail.com>
  - Allow numpy 1.23
* Mon Oct 03 2022 Daniel Garcia <daniel.garcia@suse.com>
  - Update to 0.56.2
    This release continues to add new features, bug fixes and stability
    improvements to Numba. Please note that this will be the last release that
    has support for Python 3.7 as the next release series (Numba 0.57) will
    support Python 3.11! Also note that, this will be the last release to support
    linux-32 packages produced by the Numba team.
  - Remove fix-max-name-size.patch, it's included in the new version.
  - Add update-tbb-backend-calls-2021.6.patch to make it compatible with the
    latest tbb-devel version.
  - Add fix-cli-test.patch to disable one test that fails with OBS.
* Mon Jul 11 2022 Ben Greiner <code@bnavigator.de>
  - Update to 0.55.2
    * This is a maintenance release to support NumPy 1.22 and Apple
      M1.
    * Backport #8027: Support for NumPy 1.22
    * update max NumPy for 0.55.2
    * Backport #8052 Ensure pthread is linked in when building for
      ppc64le.
    * Backport #8102 to fix numpy requirements
    * Backport #8109 Pin TBB support with respect to incompatible
      2021.6 API.
* Sat Jan 29 2022 Ben Greiner <code@bnavigator.de>
  - Update to 0.55.1
    * This is a bugfix release that closes all the remaining issues
      from the accelerated release of 0.55.0 and also any release
      critical regressions discovered since then.
    * CUDA target deprecation notices:
    - Support for CUDA toolkits < 10.2 is deprecated and will be
      removed in Numba 0.56.
    - Support for devices with Compute Capability < 5.3 is
      deprecated and will be removed in Numba 0.56.
  - Drop numba-pr7748-random32bitwidth.patch
  - Explicitly declare supported platforms (avoid failing tests on
    ppc64)
* Fri Jan 14 2022 Ben Greiner <code@bnavigator.de>
  - Update to 0.55.0
    * This release includes a significant number important dependency
      upgrades along with a number of new features and bug fixes.
    * NOTE: Due to NumPy CVE-2021-33430 this release has bypassed the
      usual release process so as to promptly provide a Numba release
      that supports NumPy 1.21. A single release candidate (RC1) was
      made and a few issues were reported, these are summarised as
      follows and will be fixed in a subsequent 0.55.1 release.
    * Known issues with this release:
    - Incorrect result copying array-typed field of structured
      array (#7693)
    - Two issues in DebugInfo generation (#7726, #7730)
    - Compilation failure for hash of floating point values on 32
      bit Windows when using Python 3.10 (#7713).
    * Support for Python 3.10
    * Support for NumPy 1.21
    * The minimum supported NumPy version is raised to 1.18 for
      runtime (compilation however remains compatible with NumPy
      1.11).
    * Experimental support for isinstance.
    * The following functions are now supported:
    - np.broadcast_to
    - np.float_power
    - np.cbrt
    - np.logspace
    - np.take_along_axis
    - np.average
    - np.argmin gains support for the axis kwarg.
    - np.ndarray.astype gains support for types expressed as
      literal strings.
    * For users of the Numba extension API, Numba now has a new error
      handling mode whereby it will treat all exceptions that do not
      inherit from numba.errors.NumbaException as a “hard error” and
      immediately unwind the stack. This makes it much easier to
      debug when writing @overloads etc from the extension API as
      there’s now no confusion between Python errors and Numba
      errors. This feature can be enabled by setting the environment
      variable: NUMBA_CAPTURED_ERRORS='new_style'.
    * The threading layer selection priority can now be changed via
      the environment variable NUMBA_THREADING_LAYER_PRIORITY.
    * Support for NVIDIA’s CUDA Python bindings.
    * Support for 16-bit floating point numbers and their basic
      operations via intrinsics.
    * Streams are provided in the Stream.async_done result, making it
      easier to implement asynchronous work queues.
    * Support for structured types in device arrays, character
      sequences in NumPy arrays, and some array operations on nested
      arrays.
    * Much underlying refactoring to align the CUDA target more
      closely with the CPU target, which lays the groudwork for
      supporting the high level extension API in CUDA in future
      releases.
    * Intel also kindly sponsored research and development into
      native debug (DWARF) support and handling per-function
      compilation flags:
    * Line number/location tracking is much improved.
    * Numba’s internal representation of containers (e.g. tuples,
      arrays) are now encoded as structures.
    * Numba’s per-function compilation flags are encoded into the ABI
      field of the mangled name of the function such that it’s
      possible to compile and differentiate between versions of the
      same function with different flags set.
    * There are no new general deprecations.
    * There are no new CUDA target deprecations.
  - Drop numba-pr7483-numpy1_21.patch
  - Add numba-pr7748-random32bitwidth.patch -- gh#numba/numba#7748
* Sat Jan 08 2022 Ben Greiner <code@bnavigator.de>
  - Numba <0.55 is not compatible with Python 3.10 or NumPy 1.22
    gh#numba/numba#7557
  - Add test skip to numba-pr7483-numpy1_21.patch due to numpy update
    gh#numpy/numpy#20376
* Thu Nov 18 2021 Ben Greiner <code@bnavigator.de>
  - Update to 0.54.1
    * This is a bugfix release for 0.54.0. It fixes a regression in
      structured array type handling, a potential leak on
      initialization failure in the CUDA target, a regression caused
      by Numba’s vendored cloudpickle module resetting dynamic
      classes and a few minor testing/infrastructure related
      problems.
  - Release summary for 0.54.0
    * This release includes a significant number of new features,
      important refactoring, critical bug fixes and a number of
      dependency upgrades.
    * Python language support enhancements:
    - Basic support for f-strings.
    - dict comprehensions are now supported.
    - The sum built-in function is implemented.
    * NumPy features/enhancements, The following functions are now
      supported:
    - np.clip
    - np.iscomplex
    - np.iscomplexobj
    - np.isneginf
    - np.isposinf
    - np.isreal
    - np.isrealobj
    - np.isscalar
    - np.random.dirichlet
    - np.rot90
    - np.swapaxes
    * Also np.argmax has gained support for the axis keyword argument
      and it’s now possible to use 0d NumPy arrays as scalars in
      __setitem__ calls.
    Internal changes:
    * Debugging support through DWARF has been fixed and enhanced.
    * Numba now optimises the way in which locals are emitted to help
      reduce time spend in LLVM’s SROA passes.
    CUDA target changes:
    * Support for emitting lineinfo to be consumed by profiling tools
      such as Nsight Compute
    * Improved fastmath code generation for various trig, division,
      and other functions
    * Faster compilation using lazy addition of libdevice to compiled
      units
    * Support for IPC on Windows
    * Support for passing tuples to CUDA ufuncs
    * Performance warnings:
    - When making implicit copies by calling a kernel on arrays in
      host memory
    - When occupancy is poor due to kernel or ufunc/gufunc
      configuration
    * Support for implementing warp-aggregated intrinsics:
    - Using support for more CUDA functions: activemask(),
      lanemask_lt()
    - The ffs() function now works correctly!
    * Support for @overload in the CUDA target
    Intel kindly sponsored research and development that lead to a
    number of new features and internal support changes:
    * Dispatchers can now be retargetted to a new target via a user
      defined context manager.
    * Support for custom NumPy array subclasses has been added
      (including an overloadable memory allocator).
    * An inheritance based model for targets that permits targets to
      share @overload implementations.
    * Per function compiler flags with inheritance behaviours.
    * The extension API now has support for overloading class methods
      via the @overload_classmethod decorator.
    Deprecations:
    * The ROCm target (for AMD ROC GPUs) has been moved to an
      “unmaintained” status and a seperate repository stub has been
      created for it at: https://github.com/numba/numba-rocm
    CUDA target deprecations and breaking changes:
    * Relaxed strides checking is now the default when computing the
      contiguity of device arrays.
    * The inspect_ptx() method is deprecated. For use cases that
      obtain PTX for further compilation outside of Numba, use
      compile_ptx() instead.
    * Eager compilation of device functions (the case when
      device=True and a signature is provided) is deprecated.
    Version support/dependency changes:
    * LLVM 11 is now supported on all platforms via llvmlite.
    * The minimum supported Python version is raised to 3.7.
    * NumPy version 1.20 is supported.
    * The minimum supported NumPy version is raised to 1.17 for
      runtime (compilation however remains compatible with NumPy
      1.11).
    * Vendor cloudpickle v1.6.0 – now used for all pickle operations.
    * TBB >= 2021 is now supported and all prior versions are
      unsupported (not easily possible to maintain the ABI breaking
      changes).
  - Full release notes;
    https://numba.readthedocs.io/en/0.54.1/release-notes.html
  - Drop patches merged upstream:
    * packaging-ignore-setuptools-deprecation.patch
    * numba-pr6851-llvm-timings.patch
  - Refresh skip-failing-tests.patch, fix-max-name-size.patch
  - Add numba-pr7483-numpy1_21.patch gh#numba/numba#7176,
    gh#numba/numba#7483
* Wed Mar 17 2021 Ben Greiner <code@bnavigator.de>
  - 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
* Wed Feb 17 2021 Ben Greiner <code@bnavigator.de>
  - 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

Files

/etc/alternatives/numba
/usr/bin/numba
/usr/bin/numba-3.11
/usr/lib/python3.11/site-packages/numba
/usr/lib/python3.11/site-packages/numba-0.60.0.dist-info
/usr/lib/python3.11/site-packages/numba-0.60.0.dist-info/INSTALLER
/usr/lib/python3.11/site-packages/numba-0.60.0.dist-info/LICENSE
/usr/lib/python3.11/site-packages/numba-0.60.0.dist-info/LICENSES.third-party
/usr/lib/python3.11/site-packages/numba-0.60.0.dist-info/METADATA
/usr/lib/python3.11/site-packages/numba-0.60.0.dist-info/RECORD
/usr/lib/python3.11/site-packages/numba-0.60.0.dist-info/REQUESTED
/usr/lib/python3.11/site-packages/numba-0.60.0.dist-info/WHEEL
/usr/lib/python3.11/site-packages/numba-0.60.0.dist-info/top_level.txt
/usr/lib/python3.11/site-packages/numba/__init__.py
/usr/lib/python3.11/site-packages/numba/__main__.py
/usr/lib/python3.11/site-packages/numba/__pycache__
/usr/lib/python3.11/site-packages/numba/__pycache__/__init__.cpython-311.opt-1.pyc
/usr/lib/python3.11/site-packages/numba/__pycache__/__init__.cpython-311.pyc
/usr/lib/python3.11/site-packages/numba/__pycache__/__main__.cpython-311.opt-1.pyc
/usr/lib/python3.11/site-packages/numba/__pycache__/__main__.cpython-311.pyc
/usr/lib/python3.11/site-packages/numba/__pycache__/_version.cpython-311.opt-1.pyc
/usr/lib/python3.11/site-packages/numba/__pycache__/_version.cpython-311.pyc
/usr/lib/python3.11/site-packages/numba/__pycache__/extending.cpython-311.opt-1.pyc
/usr/lib/python3.11/site-packages/numba/__pycache__/extending.cpython-311.pyc
/usr/lib/python3.11/site-packages/numba/__pycache__/runtests.cpython-311.opt-1.pyc
/usr/lib/python3.11/site-packages/numba/__pycache__/runtests.cpython-311.pyc
/usr/lib/python3.11/site-packages/numba/_devicearray.cpython-311-i386-linux-gnu.so
/usr/lib/python3.11/site-packages/numba/_dispatcher.cpython-311-i386-linux-gnu.so
/usr/lib/python3.11/site-packages/numba/_dynfunc.cpython-311-i386-linux-gnu.so
/usr/lib/python3.11/site-packages/numba/_helperlib.cpython-311-i386-linux-gnu.so
/usr/lib/python3.11/site-packages/numba/_version.py
/usr/lib/python3.11/site-packages/numba/cext
/usr/lib/python3.11/site-packages/numba/cext/__init__.py
/usr/lib/python3.11/site-packages/numba/cext/__pycache__
/usr/lib/python3.11/site-packages/numba/cext/__pycache__/__init__.cpython-311.opt-1.pyc
/usr/lib/python3.11/site-packages/numba/cext/__pycache__/__init__.cpython-311.pyc
/usr/lib/python3.11/site-packages/numba/cloudpickle
/usr/lib/python3.11/site-packages/numba/cloudpickle/__init__.py
/usr/lib/python3.11/site-packages/numba/cloudpickle/__pycache__
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Fabrice Bellet, Mon Oct 21 00:14:21 2024