| Name | llvmlite JSON |
| Version |
0.42.0
JSON |
| download |
| home_page | http://llvmlite.readthedocs.io |
| Summary | lightweight wrapper around basic LLVM functionality |
| upload_time | 2024-01-31 23:01:42 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | BSD |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
========
llvmlite
========
.. image:: https://dev.azure.com/numba/numba/_apis/build/status/numba.llvmlite?branchName=main
:target: https://dev.azure.com/numba/numba/_build/latest?definitionId=2&branchName=main
:alt: Azure Pipelines
.. image:: https://codeclimate.com/github/numba/llvmlite/badges/gpa.svg
:target: https://codeclimate.com/github/numba/llvmlite
:alt: Code Climate
.. image:: https://coveralls.io/repos/github/numba/llvmlite/badge.svg
:target: https://coveralls.io/github/numba/llvmlite
:alt: Coveralls.io
.. image:: https://readthedocs.org/projects/llvmlite/badge/
:target: https://llvmlite.readthedocs.io
:alt: Readthedocs.io
A Lightweight LLVM Python Binding for Writing JIT Compilers
-----------------------------------------------------------
.. _llvmpy: https://github.com/llvmpy/llvmpy
llvmlite is a project originally tailored for Numba_'s needs, using the
following approach:
* A small C wrapper around the parts of the LLVM C++ API we need that are
not already exposed by the LLVM C API.
* A ctypes Python wrapper around the C API.
* A pure Python implementation of the subset of the LLVM IR builder that we
need for Numba.
Why llvmlite
============
The old llvmpy_ binding exposes a lot of LLVM APIs but the mapping of
C++-style memory management to Python is error prone. Numba_ and many JIT
compilers do not need a full LLVM API. Only the IR builder, optimizer,
and JIT compiler APIs are necessary.
Key Benefits
============
* The IR builder is pure Python code and decoupled from LLVM's
frequently-changing C++ APIs.
* Materializing a LLVM module calls LLVM's IR parser which provides
better error messages than step-by-step IR building through the C++
API (no more segfaults or process aborts).
* Most of llvmlite uses the LLVM C API which is small but very stable
(low maintenance when changing LLVM version).
* The binding is not a Python C-extension, but a plain DLL accessed using
ctypes (no need to wrestle with Python's compiler requirements and C++ 11
compatibility).
* The Python binding layer has sane memory management.
* llvmlite is faster than llvmpy thanks to a much simpler architecture
(the Numba_ test suite is twice faster than it was).
Compatibility
=============
llvmlite has been tested with Python 3.9 -- 3.12 and is likely to work with
greater versions.
As of version 0.41.0, llvmlite requires LLVM 14.x.x on all architectures
Historical compatibility table:
================= ========================
llvmlite versions compatible LLVM versions
================= ========================
0.41.0 - ... 14.x.x
0.40.0 - 0.40.1 11.x.x and 14.x.x (12.x.x and 13.x.x untested but may work)
0.37.0 - 0.39.1 11.x.x
0.34.0 - 0.36.0 10.0.x (9.0.x for ``aarch64`` only)
0.33.0 9.0.x
0.29.0 - 0.32.0 7.0.x, 7.1.x, 8.0.x
0.27.0 - 0.28.0 7.0.x
0.23.0 - 0.26.0 6.0.x
0.21.0 - 0.22.0 5.0.x
0.17.0 - 0.20.0 4.0.x
0.16.0 - 0.17.0 3.9.x
0.13.0 - 0.15.0 3.8.x
0.9.0 - 0.12.1 3.7.x
0.6.0 - 0.8.0 3.6.x
0.1.0 - 0.5.1 3.5.x
================= ========================
Documentation
=============
You'll find the documentation at http://llvmlite.pydata.org
Pre-built binaries
==================
We recommend you use the binaries provided by the Numba_ team for
the Conda_ package manager. You can find them in Numba's `anaconda.org
channel <https://anaconda.org/numba>`_. For example::
$ conda install --channel=numba llvmlite
(or, simply, the official llvmlite package provided in the Anaconda_
distribution)
.. _Numba: http://numba.pydata.org/
.. _Conda: http://conda.pydata.org/
.. _Anaconda: http://docs.continuum.io/anaconda/index.html
Other build methods
===================
If you don't want to use our pre-built packages, you can compile
and install llvmlite yourself. The documentation will teach you how:
http://llvmlite.pydata.org/en/latest/install/index.html
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