Compiling Python code using LLVM
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.
- Developed at devel:languages:python:numeric
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osc -A https://api.opensuse.org checkout openSUSE:Factory/python-numba && cd $_
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Source Files
Filename | Size | Changed |
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_multibuild | 0000000123 123 Bytes | |
numba-0.59.1.tar.gz | 0002652730 2.53 MB | |
python-numba.changes | 0000068395 66.8 KB | |
python-numba.spec | 0000006529 6.38 KB | |
skip-failing-tests.patch | 0000002524 2.46 KB |
Latest Revision
- 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
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