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Name: python310-numba | Distribution: openSUSE Tumbleweed |
Version: 0.60.0 | Vendor: openSUSE |
Release: 1.1 | Build date: Mon Jul 1 07:10:03 2024 |
Group: Unspecified | Build host: reproducible |
Size: 24476323 | Source RPM: python-numba-0.60.0-1.1.src.rpm |
Packager: http://bugs.opensuse.org | |
Url: https://numba.pydata.org/ | |
Summary: NumPy-aware optimizing compiler for Python using LLVM |
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.
BSD-2-Clause
* 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
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Fabrice Bellet, Sun Aug 18 01:11:36 2024