llvmlite

A lightweight LLVM python binding for writing JIT compilers

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.

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.

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 quite faster than llvmpy's thanks to a much simpler architeture (the Numba test suite is twice faster than it was).

llvmpy Compatibility Layer

The llvmlite.llvmpy namespace provides a minimal llvmpy compatibility layer.

Compatibility

llvmlite works with Python 2.6 or greater (including Python 3.3 or greater).

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 binstar channel <https://binstar.org/numba>. For example:

$ conda install --channel=numba llvmlite

(or, simply, the official llvmlite package provided in the Anaconda distribution)

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