A Just-In-Time Compiler for Numerical Functions in Python
Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax.
Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
For more information about Numba, see the Numba homepage: https://numba.pydata.org
Supported Platforms
Operating systems and CPUs:
Linux: x86 (32-bit), x86_64, ppc64le (POWER8 and 9), ARMv7 (32-bit), ARMv8 (64-bit)
Windows: x86, x86_64
macOS: x86_64, (M1/Arm64, unofficial support only)
*BSD: (unofficial support only)
(Optional) Accelerators and GPUs:
NVIDIA GPUs (Kepler architecture or later) via CUDA driver on Linux, Windows, macOS (< 10.14)
AMD GPUs via ROCm driver on Linux
Dependencies
Python versions: 3.6-3.9
llvmlite 0.36.*
NumPy >=1.15 (can build with 1.11 for ABI compatibility)
Optionally:
SciPy >=1.0.0 (for numpy.linalg support)
Installing
The easiest way to install Numba and get updates is by using the Anaconda Distribution: https://www.anaconda.com/download
$ conda install numba
For more options, see the Installation Guide: https://numba.readthedocs.io/en/stable/user/installing.html
Documentation
Mailing Lists
Join the Numba mailing list numba-users@continuum.io: https://groups.google.com/a/continuum.io/d/forum/numba-users
Some old archives are at: http://librelist.com/browser/numba/
Continuous Integration
5 maintainers
Release files for numba 0.53.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| numba-0.53.1.tar.gz | 2.2 MB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| numba-0.53.1-cp39-cp39-win_amd64.whl | CPython 3.9 | CPython 3.9 | Windows x86-64 | Details |
| numba-0.53.1-cp39-cp39-win32.whl | CPython 3.9 | CPython 3.9 | Windows x86-32 | Details |
| numba-0.53.1-cp39-cp39-manylinux2014_x86_64.whl | CPython 3.9 | CPython 3.9 | Linux glibc 2.17+ x86-64 | Details |
| numba-0.53.1-cp39-cp39-manylinux2014_i686.whl | CPython 3.9 | CPython 3.9 | Linux glibc 2.17+ x86-32 | Details |
| numba-0.53.1-cp39-cp39-macosx_10_14_x86_64.whl | CPython 3.9 | CPython 3.9 | macOS 10.14+ x86-64 | Details |
| numba-0.53.1-cp38-cp38-win_amd64.whl | CPython 3.8 | CPython 3.8 | Windows x86-64 | Details |
| numba-0.53.1-cp38-cp38-win32.whl | CPython 3.8 | CPython 3.8 | Windows x86-32 | Details |
| numba-0.53.1-cp38-cp38-manylinux2014_x86_64.whl | CPython 3.8 | CPython 3.8 | Linux glibc 2.17+ x86-64 | Details |
| numba-0.53.1-cp38-cp38-manylinux2014_i686.whl | CPython 3.8 | CPython 3.8 | Linux glibc 2.17+ x86-32 | Details |
| numba-0.53.1-cp38-cp38-macosx_10_14_x86_64.whl | CPython 3.8 | CPython 3.8 | macOS 10.14+ x86-64 | Details |
| numba-0.53.1-cp37-cp37m-win_amd64.whl | CPython 3.7 | CPython 3.7 pymalloc | Windows x86-64 | Details |
| numba-0.53.1-cp37-cp37m-win32.whl | CPython 3.7 | CPython 3.7 pymalloc | Windows x86-32 | Details |
| numba-0.53.1-cp37-cp37m-manylinux2014_x86_64.whl | CPython 3.7 | CPython 3.7 pymalloc | Linux glibc 2.17+ x86-64 | Details |
| numba-0.53.1-cp37-cp37m-manylinux2014_i686.whl | CPython 3.7 | CPython 3.7 pymalloc | Linux glibc 2.17+ x86-32 | Details |
| numba-0.53.1-cp37-cp37m-macosx_10_14_x86_64.whl | CPython 3.7 | CPython 3.7 pymalloc | macOS 10.14+ x86-64 | Details |
| numba-0.53.1-cp36-cp36m-win_amd64.whl | CPython 3.6 | CPython 3.6 pymalloc | Windows x86-64 | Details |
| numba-0.53.1-cp36-cp36m-win32.whl | CPython 3.6 | CPython 3.6 pymalloc | Windows x86-32 | Details |
| numba-0.53.1-cp36-cp36m-manylinux2014_x86_64.whl | CPython 3.6 | CPython 3.6 pymalloc | Linux glibc 2.17+ x86-64 | Details |
| numba-0.53.1-cp36-cp36m-manylinux2014_i686.whl | CPython 3.6 | CPython 3.6 pymalloc | Linux glibc 2.17+ x86-32 | Details |
| numba-0.53.1-cp36-cp36m-macosx_10_14_x86_64.whl | CPython 3.6 | CPython 3.6 pymalloc | macOS 10.14+ x86-64 | Details |
Total release size: 55.4 MB
Release files / numba-0.53.1.tar.gz
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Release files / numba-0.53.1-cp39-cp39-win_amd64.whl
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Release files / numba-0.53.1-cp39-cp39-win32.whl
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Release files / numba-0.53.1-cp39-cp39-manylinux2014_x86_64.whl
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Release files / numba-0.53.1-cp39-cp39-manylinux2014_i686.whl
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Release files / numba-0.53.1-cp39-cp39-macosx_10_14_x86_64.whl
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Release files / numba-0.53.1-cp38-cp38-win_amd64.whl
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Release files / numba-0.53.1-cp38-cp38-manylinux2014_x86_64.whl
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Release files / numba-0.53.1-cp38-cp38-manylinux2014_i686.whl
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Release files / numba-0.53.1-cp38-cp38-macosx_10_14_x86_64.whl
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Release files / numba-0.53.1-cp37-cp37m-win_amd64.whl
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Release files / numba-0.53.1-cp37-cp37m-manylinux2014_x86_64.whl
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Release files / numba-0.53.1-cp37-cp37m-macosx_10_14_x86_64.whl
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Release files / numba-0.53.1-cp36-cp36m-win_amd64.whl
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Release files / numba-0.53.1-cp36-cp36m-manylinux2014_x86_64.whl
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Release files / numba-0.53.1-cp36-cp36m-manylinux2014_i686.whl
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| Size | 3.1 MB |
| Tags | CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.17+ x86-32 |
|
SHA-256 checksum How to use checksums |
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| Uploaded via |
twine/2.0.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.2.0.post20200714 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.5
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Release files / numba-0.53.1-cp36-cp36m-macosx_10_14_x86_64.whl
| Download URL | numba-0.53.1-cp36-cp36m-macosx_10_14_x86_64.whl |
|---|---|
| Size | 2.2 MB |
| Tags | CPython 3.6 CPython 3.6 pymalloc macOS 10.14+ x86-64 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/2.0.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.2.0.post20200714 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.5
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