alp-graphblas 0.8.30

Python bindings for ALP GraphBLAS (minimal package layout)

pyalp (packaged)

This directory contains the Python package layout for the pyalp bindings that expose parts of the ALP GraphBLAS project via pybind11.

Quick start

Create and activate a virtual environment, then install the package (example using PyPI (recommended):

python -m venv venv
source venv/bin/activate
pip install alp-graphblas

If you want to try a pre-release from TestPyPI instead, use the TestPyPI index but install the same package name alp-graphblas (pip will pull the package and its dependencies from the given index):

python -m venv venv
source venv/bin/activate
pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple alp-graphblas

Basic usage

pyalp (packaged)

This directory contains the Python package layout for the pyalp bindings that expose parts of the ALP GraphBLAS project via pybind11.

Quick start

Create and activate a virtual environment, then install the published package alp-graphblas from PyPI or TestPyPI as shown above. Note: the import name inside Python remains pyalp (the package provides the pyalp module), so your code still does import pyalp after installation.

Basic usage

The package exposes a small set of helpers and one or more compiled backend modules. Use these helpers to list and select available backends and to read runtime build metadata:

import pyalp
print('pyalp build metadata:', pyalp.get_build_metadata())
print('available backends:', pyalp.list_backends())

# Import a specific backend module (returns the compiled module)
backend = pyalp.get_backend('pyalp_ref')  # or 'pyalp_omp', 'pyalp_nonblocking'
print('backend module:', backend)

Backends and import caveat

Wheels may include multiple compiled backend modules (for example pyalp_ref, pyalp_omp, pyalp_nonblocking). Historically, importing multiple different compiled backends in the same Python process could raise pybind11 registration errors (types duplicated). The bindings now use py::module_local() for core wrapper types, which reduces collisions, but if you encounter issues importing more than one backend in-process, prefer testing each backend in a separate process (the supplied test runner does this).

Runtime metadata

The package provides a metadata module generated at build time by CMake. Use pyalp.get_build_metadata() to access keys such as:

• version — pyalp package version
• build_type — CMake build type used (e.g., Release)
• alp_version — ALP repository version or tag used to build
• alp_git_commit / alp_git_branch — Git information captured by CI
• license — detected repository license (e.g. Apache-2.0)

pyalp.get_algorithm_metadata() contains algorithm/backends info and a readme key with packaged README contents.

Minimal example — conjugate gradient (small test)

Save the following as test_cg.py and run python test_cg.py after installing pyalp and numpy. The example shows selecting a backend explicitly via pyalp.get_backend() and then using the backend's Matrix, Vector, and conjugate_gradient API.

#!/usr/bin/env python3
"""
Test script for the pyalp backend (example uses the OpenMP backend name
`pyalp_omp`, but you can use `pyalp_ref` or another available backend).

Usage:
		python test_cg.py

Dependencies:
		- numpy
		- pyalp (installed and providing a backend such as pyalp_omp)
"""

import numpy as np
import pyalp

# Choose the backend module (change name if you want a different backend)
pyalp = pyalp.get_backend('pyalp_omp')  # or 'pyalp_ref', 'pyalp_nonblocking'

# Generate a small sparse linear system using numpy arrays
N, M = 5, 5
idata = np.array([0, 1, 2, 3, 3, 4, 2, 3, 3, 4, 1, 4, 1, 4, 4], dtype=np.int32)
jdata = np.array([0, 1, 2, 3, 2, 2, 1, 4, 1, 1, 0, 3, 0, 3, 4], dtype=np.int32)
vdata = np.array([1, 1, 1, 1, 0.5, 2, 1, 4, 4.4, 1, 0, 3.5, 0, 3, 1], dtype=np.float64)
b = np.array([1.0, 1.0, 1.0, 1.0, 1.0], dtype=np.float64)
x = np.array([1.0, 1.0, 0.0, 0.3, -1.0], dtype=np.float64)
r = np.zeros(5, dtype=np.float64)
u = np.zeros(5, dtype=np.float64)
tmp = np.zeros(5, dtype=np.float64)

# Create the pyalp Matrix and Vector objects
alpmatrixA = pyalp.Matrix(5, 5, idata, jdata, vdata)
alpvectorx = pyalp.Vector(5, x)
alpvectorb = pyalp.Vector(5, b)
alpvectorr = pyalp.Vector(5, r)
alpvectoru = pyalp.Vector(5, u)
alpvectortmp = pyalp.Vector(5, tmp)

maxiterations = 2000
verbose = 1

# Solve the linear system using the conjugate gradient method in the backend
iterations, residual = pyalp.conjugate_gradient(
		alpmatrixA,
		alpvectorx,
		alpvectorb,
		alpvectorr,
		alpvectoru,
		alpvectortmp,
		maxiterations,
		verbose,
)
print('iterations =', iterations)
print('residual =', residual)

# Convert the result vector to a numpy array and print it
x_result = alpvectorx.to_numpy()
print('x_result =', x_result)

# Check if the result is close to the expected solution
assert np.allclose(x_result, np.array([1.0, 1.0, 0.0, 0.13598679, -0.88396565])), 'solution mismatch'

Packaging notes (for maintainers)

• The CI uses a top-level CMake configure/build to produce the native shared object and a CMake-configured _metadata.py. The packaging setup.py then copies the built .so and _metadata.py into the wheel.
• The CI passes Git/version information into CMake so the generated metadata is populated even in detached/CI environments.

If you modify the metadata template, update pyalp/src/pyalp/_metadata.py.in.

License

See the repository LICENSE at the project root; the packaging pipeline attempts to detect and embed the license string in runtime metadata.