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py-numba: updated to 0.55.2 Version 0.55.2 (25 May, 2022) ----------------------------- This is a maintenance release to support NumPy 1.22 and Apple M1.
2022-05-27 10:29:57 +02:00
# $NetBSD: Makefile,v 1.31 2022/05/27 08:29:57 adam Exp $
math/py-numba: Import version 0.37.0 Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls effectively removing the "interpreter" but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy. Packaged by Kamil Rytarowski for pkgsrc-wip and updated by me.
2018-05-18 18:08:49 +02:00
py-numba: updated to 0.55.2 Version 0.55.2 (25 May, 2022) ----------------------------- This is a maintenance release to support NumPy 1.22 and Apple M1.
2022-05-27 10:29:57 +02:00
DISTNAME= numba-0.55.2
math/py-numba: Import version 0.37.0 Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls effectively removing the "interpreter" but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy. Packaged by Kamil Rytarowski for pkgsrc-wip and updated by me.
2018-05-18 18:08:49 +02:00
PKGNAME= ${PYPKGPREFIX}-${DISTNAME}
py-numba: updated to 0.39.0 Version 0.39.0 Here are the highlights for the Numba 0.39.0 release. This is the first version that supports Python 3.7. With help from Intel, we have fixed the issues with SVML support. List has gained support for containing reference-counted types like NumPy arrays and list. Note, list still cannot hold heterogeneous types. We have made a significant change to the internal calling-convention, which should be transparent to most users, to allow for a future feature that will permitting jumping back into python-mode from a nopython-mode function. This also fixes a limitation to print that disabled its use from nopython functions that were deep in the call-stack. For CUDA GPU support, we added a __cuda_array_interface__ following the NumPy array interface specification to allow Numba to consume externally defined device arrays. We have opened a corresponding pull request to CuPy to test out the concept and be able to use a CuPy GPU array. The Numba dispatcher inspect_types() method now supports the kwarg pretty which if set to True will produce ANSI/HTML output, showing the annotated types, when invoked from ipython/jupyter-notebook respectively. The NumPy functions ndarray.dot, np.percentile and np.nanpercentile, and np.unique are now supported. Numba now supports the use of a per-project configuration file to permanently set behaviours typically set via NUMBA_* family environment variables. Support for the ppc64le architecture has been added.
2018-08-28 14:06:42 +02:00
CATEGORIES= math python
MASTER_SITES= ${MASTER_SITE_PYPI:=n/numba/}
math/py-numba: Import version 0.37.0 Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls effectively removing the "interpreter" but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy. Packaged by Kamil Rytarowski for pkgsrc-wip and updated by me.
2018-05-18 18:08:49 +02:00
MAINTAINER= pkgsrc-users@NetBSD.org
all: migrate homepages from http to https pkglint -r --network --only "migrate" As a side-effect of migrating the homepages, pkglint also fixed a few indentations in unrelated lines. These and the new homepages have been checked manually.
2020-01-26 18:30:40 +01:00
HOMEPAGE= https://numba.pydata.org/
math/py-numba: Import version 0.37.0 Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls effectively removing the "interpreter" but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy. Packaged by Kamil Rytarowski for pkgsrc-wip and updated by me.
2018-05-18 18:08:49 +02:00
COMMENT= NumPy aware dynamic Python compiler using LLVM
LICENSE= 2-clause-bsd
py-numba: updated to 0.55.0 Version 0.55.0 This release includes a significant number important dependency upgrades along with a number of new features and bug fixes. Version 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. Version 0.53.1 This is a bugfix release for 0.53.0. It contains the following four pull-requests which fix two critical regressions and two build failures reported by the openSuSe team: * Fix regression on gufunc serialization * Fix regression in CUDA: Set stream in mapped and managed array device_setup * Ignore warnings from packaging module when testing import behaviour. * set non-reported llvm timing values to 0.0 Version 0.53.0 This release continues to add new features, bug fixes and stability improvements to Numba. Highlights of core changes: Support for Python 3.9 Function sub-typing Initial support for dynamic gufuncs Parallel Accelerator (@njit(parallel=True) now supports Fortran ordered arrays Version 0.52.0 This release focuses on performance improvements, but also adds some new features and contains numerous bug fixes and stability improvements.
2022-01-14 20:52:24 +01:00
DEPENDS+= ${PYPKGPREFIX}-llvmlite>=0.38.0:../../devel/py-llvmlite
py-numba: updated to 0.51.2 Version 0.51.2 This is a bugfix release for 0.51.1. It fixes a critical performance bug in the CFG back edge computation algorithm that leads to exponential time complexity arising in compilation for use cases with certain pathological properties. Continue. Don’t visit already checked successors Version 0.51.1 This is a bugfix release for 0.51.0, it fixes a critical bug in caching, another critical bug in the CUDA target initialisation sequence and also fixes some compile time performance regressions: Fix objmode cache segfault Fix compilation slowdown due to controlflow analysis CUDA: Don’t make a runtime call on import Fix: Make UnicodeCharSeq into str for comparison. Fix: Failure in test_cuda_submodules
2020-10-02 10:34:39 +02:00
DEPENDS+= ${PYPKGPREFIX}-setuptools-[0-9]*:../../devel/py-setuptools
math/py-numba: Import version 0.37.0 Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls effectively removing the "interpreter" but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy. Packaged by Kamil Rytarowski for pkgsrc-wip and updated by me.
2018-05-18 18:08:49 +02:00
py-numba: updated to 0.55.0 Version 0.55.0 This release includes a significant number important dependency upgrades along with a number of new features and bug fixes. Version 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. Version 0.53.1 This is a bugfix release for 0.53.0. It contains the following four pull-requests which fix two critical regressions and two build failures reported by the openSuSe team: * Fix regression on gufunc serialization * Fix regression in CUDA: Set stream in mapped and managed array device_setup * Ignore warnings from packaging module when testing import behaviour. * set non-reported llvm timing values to 0.0 Version 0.53.0 This release continues to add new features, bug fixes and stability improvements to Numba. Highlights of core changes: Support for Python 3.9 Function sub-typing Initial support for dynamic gufuncs Parallel Accelerator (@njit(parallel=True) now supports Fortran ordered arrays Version 0.52.0 This release focuses on performance improvements, but also adds some new features and contains numerous bug fixes and stability improvements.
2022-01-14 20:52:24 +01:00
# OpenMP is not portable
MAKE_ENV+= NUMBA_DISABLE_OPENMP=1
math/py-numba: Import version 0.37.0 Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls effectively removing the "interpreter" but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy. Packaged by Kamil Rytarowski for pkgsrc-wip and updated by me.
2018-05-18 18:08:49 +02:00
USE_LANGUAGES= c c++
math: Mark some packages incompatible with Python 3.7 due to numpy
2022-04-15 13:27:41 +02:00
PYTHON_VERSIONS_INCOMPATIBLE= 27 37
py-numba: updated to 0.39.0 Version 0.39.0 Here are the highlights for the Numba 0.39.0 release. This is the first version that supports Python 3.7. With help from Intel, we have fixed the issues with SVML support. List has gained support for containing reference-counted types like NumPy arrays and list. Note, list still cannot hold heterogeneous types. We have made a significant change to the internal calling-convention, which should be transparent to most users, to allow for a future feature that will permitting jumping back into python-mode from a nopython-mode function. This also fixes a limitation to print that disabled its use from nopython functions that were deep in the call-stack. For CUDA GPU support, we added a __cuda_array_interface__ following the NumPy array interface specification to allow Numba to consume externally defined device arrays. We have opened a corresponding pull request to CuPy to test out the concept and be able to use a CuPy GPU array. The Numba dispatcher inspect_types() method now supports the kwarg pretty which if set to True will produce ANSI/HTML output, showing the annotated types, when invoked from ipython/jupyter-notebook respectively. The NumPy functions ndarray.dot, np.percentile and np.nanpercentile, and np.unique are now supported. Numba now supports the use of a per-project configuration file to permanently set behaviours typically set via NUMBA_* family environment variables. Support for the ppc64le architecture has been added.
2018-08-28 14:06:42 +02:00
*: set USE_PKG_RESOURCES for more packages
2022-01-05 21:47:34 +01:00
USE_PKG_RESOURCES= yes
math/py-numba: Add ALTERNATIVES
2018-09-04 01:47:44 +02:00
post-install:
.for cmd in numba pycc
py-numba: updated to 0.41.0 Version 0.41.0 This release adds the following major features: * Diagnostics showing the optimizations done by ParallelAccelerator * Support for profiling Numba-compiled functions in Intel VTune * Additional NumPy functions: partition, nancumsum, nancumprod, ediff1d, cov, conj, conjugate, tri, tril, triu * Initial support for Python 3 Unicode strings General Enhancements: * armv7 support * invert mapping b/w binop operators and the operator module * First attempt at parallel diagnostics * Adding NUMBA_ENABLE_PROFILING envvar, enabling jit event * Support for np.partition * Support for np.nancumsum and np.nancumprod * Add location information to exceptions. * Support for np.ediff1d * Support for np.cov * Support user pipeline class in with lifting * string support * Improve error message for empty imprecise lists. * Enable overload(operator.getitem) * Support negative indexing in tuple. * Refactor Const type * Optimized usage of alloca out of the loop * Updates for llvmlite 0.26 * Add support for `np.conj/np.conjugate`. * np.tri, np.tril, np.triu - default optional args * Permit dtype argument as sole kwarg in np.eye CUDA Enhancements: * Add max_registers Option to cuda.jit Continuous Integration / Testing: * CI with Azure Pipelines * Workaround race condition with apt * Fix issues with Azure Pipelines * Fix `RuntimeWarning: 'numba.runtests' found in sys.modules` * Disable openmp in wheel building * Azure Pipelines templates * Fix cuda tests and error reporting in test discovery * Prevent faulthandler installation on armv7l * Fix CUDA test that used negative indexing behaviour that's fixed. * Start Flake8 checking of Numba source Fixes: * Fix dispatcher to only consider contiguous-ness. * Fix 3119, raise for 0d arrays in reductions * Reduce redundant module linking * Fix AOT on windows. * Fix memory management of __cuda_array_interface__ views. * Fix typo in error name. * Fix the default unboxing logic * Allow non-global reference to objmode() context-manager * Fix global reference in objmode for dynamically created function * CUDA_ERROR_MISALIGNED_ADDRESS Using Multiple Const Arrays * Correctly handle very old versions of colorama * Add 32bit package guard for non-32bit installs * Fix with-objmode warning * Fix label offset in call inline after parfor pass * Fixes raising of user defined exceptions for exec(<string>). * Fix error due to function naming in CI in py2.7 * Fixed TBB's single thread execution and test added for * Allow matching non-array objects in find_callname() * Change getiter and iternext to not be pure. * Make ir.UndefinedType singleton class. * Fix np.random.shuffle sideeffect * Raise unsupported for kwargs given to `print()` * Remove dead script. * Fix stencil support for boolean as return type * Fix handling make_function literals * Add missing unicode != unicode * Fix complex math sqrt implementation for large -ve values * This adds arg an check for the pattern supplied to Parfors. * Sets list dtor linkage to `linkonce_odr` to fix visibility in AOT Documentation Updates: * Update 0.40 changelog with additional PRs * Tweak spacing to avoid search box wrapping onto second line * Add note about memory leaks with exceptions to docs. * Add FAQ on CUDA + fork issue. * Update docs for argsort, kind kwarg partially supported. * Added mention of njit in 5minguide.rst * Fix parallel reduction example in docs. * Fix broken link and mark up problem. * Size Numba logo in docs in em units. * just two typos * Document string support * Documentation for parallel diagnostics.
2018-12-09 21:25:12 +01:00
cd ${DESTDIR}${PREFIX}/bin && \
py-numba: updated to 0.44.0 Version 0.44.0 IMPORTANT: In this release a few significant deprecations (and some less significant ones) are being made, users are encouraged to read the related documentation. General enhancements in this release include: - Numba is backed by LLVM 8 on all platforms apart from ppc64le, which, due to bugs, remains on the LLVM 7.x series. - Numba's dictionary support now includes type inference for keys and values. - The .view() method now works for NumPy scalar types. - Newly supported NumPy functions added: np.delete, np.nanquantile, np.quantile, np.repeat, np.shape. In addition considerable effort has been made to fix some long standing bugs and a large number of other bugs, the "Fixes" section is very large this time! Enhancements from user contributed PRs (with thanks!): - Max Bolingbroke added support for the selective use of fastmath flags in 3847. - Rob Ennis made min() and max() work on iterables in 3820 and added np.quantile and np.nanquantile in 3899. - Sergey Shalnov added numerous unicode string related features, zfill in 3978, ljust in 4001, rjust and center in 4044 and strip, lstrip and rstrip in 4048. - Guilherme Leobas added support for np.delete in 3890 - Christoph Deil exposed the Numba CLI via python -m numba in 4066 and made numerous documentation fixes. - Leo Schwarz wrote the bulk of the code for jitclass default constructor arguments in 3852. - Nick White enhanced the CUDA backend to use min/max PTX instructions where possible in 4054. - Lucio Fernandez-Arjona implemented the unicode string __mul__ function in 3952. - Dimitri Vorona wrote the bulk of the code to implement getitem and setitem for jitclass in 3861. General Enhancements: * Min max on iterables * Unicode type iteration * Allow fine-grained control of fastmath flags to partially address 2923 * Add support for np.delete * Support for np.quantile and np.nanquantile * Fix 3457 :: Implements np.repeat * Add .view() method for NumPy scalars * Update icc_rt clone recipe. * __mul__ for strings, initial implementation and tests * Type-inferred dictionary * Create a view for string slicing to avoid extra allocations * zfill operation implementation * ljust operation implementation * Support dict() and {} * Support for llvm 8 * Make type.Optional str more representative * Deprecation warnings * rjust and center operations implementation * strip, lstrip and rstrip operations implementation * Expose numba CLI via python -m numba * Impl np.shape and support function for asarray. * Deprecate the use of iternext_impl without RefType CUDA Enhancements/Fixes: * Adds .nbytes property to CUDA device array objects. * Add .inspect_ptx() to cuda device function * CUDA: Use min/max PTX Instructions * Update env-vars for CUDA libraries lookup Documentation Updates: * Code repository map * adding Joris' Fosdem 2019 presentation * order talks on applications of Numba by date * fix two small typos in vectorize docs * Fixup jitclass docs * mention preprint repo in FAQ. Fixes 3981 * Correct runtests command in contributing.rst * fix typo * Ambiguous Documentation fix for guvectorize. * Remove remaining mentions of autojit in docs * Fix annotate example in docstring * Add FAQ entry explaining Numba project name * Add Documentation for atomicity of typed.Dict * Remove info about CUDA ENVVAR potential replacement Fixes: * Resolves issue 3528. Adds support for slices when not using parallel=True. * Remove dels for known dead vars. * Fix mutable flag transmission in .astype * Fix some minor issues in the C source. * Correct boolean reinterpretation of data * Comments out the appveyor badge * fixes flake8 after merge * Add assert to ir.py to help enforce correct structuring * fix preparfor dtype transform for datetime64 * Prevent mutation of objmode fallback IR. * Updates for llvmlite 0.29 * Use safe_load from pyyaml. * Add tolerance to network errors by permitting conda to retry * Fix casting in namedtuple ctor. * Fix array inliner for multiple array definition. * Cherrypick 3903 to main * Raise better error if unsupported jump opcode found. * Apply flake8 to the numpy related files * Silence DeprecationWarning * Better error message for unknown opcode * Fix typing of ufuncs in parfor conversion * Return variable renaming dict from inline_closurecall * Fix bug in alignment computation of Record.make_c_struct * Fix error with pickling unicode * Unicode split algo versioning * Add handler for unknown locale to numba -s * Permit Optionals in ufunc machinery * Remove assert in type inference causing poor error message. * add is_ascii flag to UnicodeType * Prevent zero division error in np.linalg.cond * Resolves 4007. * Add a more specific error message for invalid write to a global. * Fix handling of titles in record dtype * Do a check if a call is const before saying that an object is multiply defined. * Fix issue 4020. Turn off no_cpython_wrapper flag when compiling for… * [WIP] Fixing wrong dtype of array inside reflected list 4028 * Change IPython cache dir name to numba_cache * Delete examples/notebooks/LinearRegr.py * Catch writes to global typed.Dict and raise. * Check tuple length * Fix missing incref on optional return None * Make the warnings fixer flush work for warning comparing on type. * Fix function definition finding logic for commented def * Fix alignment check on 32-bit. * Use PEP 508 compliant env markers for install deps
2019-06-02 11:04:33 +02:00
${MV} ${cmd} ${cmd}-${PYVERSSUFFIX} || ${TRUE}
math/py-numba: Add ALTERNATIVES
2018-09-04 01:47:44 +02:00
.endfor
py-numba: updated to 0.44.0 Version 0.44.0 IMPORTANT: In this release a few significant deprecations (and some less significant ones) are being made, users are encouraged to read the related documentation. General enhancements in this release include: - Numba is backed by LLVM 8 on all platforms apart from ppc64le, which, due to bugs, remains on the LLVM 7.x series. - Numba's dictionary support now includes type inference for keys and values. - The .view() method now works for NumPy scalar types. - Newly supported NumPy functions added: np.delete, np.nanquantile, np.quantile, np.repeat, np.shape. In addition considerable effort has been made to fix some long standing bugs and a large number of other bugs, the "Fixes" section is very large this time! Enhancements from user contributed PRs (with thanks!): - Max Bolingbroke added support for the selective use of fastmath flags in 3847. - Rob Ennis made min() and max() work on iterables in 3820 and added np.quantile and np.nanquantile in 3899. - Sergey Shalnov added numerous unicode string related features, zfill in 3978, ljust in 4001, rjust and center in 4044 and strip, lstrip and rstrip in 4048. - Guilherme Leobas added support for np.delete in 3890 - Christoph Deil exposed the Numba CLI via python -m numba in 4066 and made numerous documentation fixes. - Leo Schwarz wrote the bulk of the code for jitclass default constructor arguments in 3852. - Nick White enhanced the CUDA backend to use min/max PTX instructions where possible in 4054. - Lucio Fernandez-Arjona implemented the unicode string __mul__ function in 3952. - Dimitri Vorona wrote the bulk of the code to implement getitem and setitem for jitclass in 3861. General Enhancements: * Min max on iterables * Unicode type iteration * Allow fine-grained control of fastmath flags to partially address 2923 * Add support for np.delete * Support for np.quantile and np.nanquantile * Fix 3457 :: Implements np.repeat * Add .view() method for NumPy scalars * Update icc_rt clone recipe. * __mul__ for strings, initial implementation and tests * Type-inferred dictionary * Create a view for string slicing to avoid extra allocations * zfill operation implementation * ljust operation implementation * Support dict() and {} * Support for llvm 8 * Make type.Optional str more representative * Deprecation warnings * rjust and center operations implementation * strip, lstrip and rstrip operations implementation * Expose numba CLI via python -m numba * Impl np.shape and support function for asarray. * Deprecate the use of iternext_impl without RefType CUDA Enhancements/Fixes: * Adds .nbytes property to CUDA device array objects. * Add .inspect_ptx() to cuda device function * CUDA: Use min/max PTX Instructions * Update env-vars for CUDA libraries lookup Documentation Updates: * Code repository map * adding Joris' Fosdem 2019 presentation * order talks on applications of Numba by date * fix two small typos in vectorize docs * Fixup jitclass docs * mention preprint repo in FAQ. Fixes 3981 * Correct runtests command in contributing.rst * fix typo * Ambiguous Documentation fix for guvectorize. * Remove remaining mentions of autojit in docs * Fix annotate example in docstring * Add FAQ entry explaining Numba project name * Add Documentation for atomicity of typed.Dict * Remove info about CUDA ENVVAR potential replacement Fixes: * Resolves issue 3528. Adds support for slices when not using parallel=True. * Remove dels for known dead vars. * Fix mutable flag transmission in .astype * Fix some minor issues in the C source. * Correct boolean reinterpretation of data * Comments out the appveyor badge * fixes flake8 after merge * Add assert to ir.py to help enforce correct structuring * fix preparfor dtype transform for datetime64 * Prevent mutation of objmode fallback IR. * Updates for llvmlite 0.29 * Use safe_load from pyyaml. * Add tolerance to network errors by permitting conda to retry * Fix casting in namedtuple ctor. * Fix array inliner for multiple array definition. * Cherrypick 3903 to main * Raise better error if unsupported jump opcode found. * Apply flake8 to the numpy related files * Silence DeprecationWarning * Better error message for unknown opcode * Fix typing of ufuncs in parfor conversion * Return variable renaming dict from inline_closurecall * Fix bug in alignment computation of Record.make_c_struct * Fix error with pickling unicode * Unicode split algo versioning * Add handler for unknown locale to numba -s * Permit Optionals in ufunc machinery * Remove assert in type inference causing poor error message. * add is_ascii flag to UnicodeType * Prevent zero division error in np.linalg.cond * Resolves 4007. * Add a more specific error message for invalid write to a global. * Fix handling of titles in record dtype * Do a check if a call is const before saying that an object is multiply defined. * Fix issue 4020. Turn off no_cpython_wrapper flag when compiling for… * [WIP] Fixing wrong dtype of array inside reflected list 4028 * Change IPython cache dir name to numba_cache * Delete examples/notebooks/LinearRegr.py * Catch writes to global typed.Dict and raise. * Check tuple length * Fix missing incref on optional return None * Make the warnings fixer flush work for warning comparing on type. * Fix function definition finding logic for commented def * Fix alignment check on 32-bit. * Use PEP 508 compliant env markers for install deps
2019-06-02 11:04:33 +02:00
.include "../../lang/python/egg.mk"
.include "../../math/py-numpy/buildlink3.mk"
math/py-numba: Import version 0.37.0 Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls effectively removing the "interpreter" but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy. Packaged by Kamil Rytarowski for pkgsrc-wip and updated by me.
2018-05-18 18:08:49 +02:00
.include "../../mk/bsd.pkg.mk"
Reference in a new issue Copy permalink