pynabled 0.0.10

Ndarray-native linear algebra and ML library - Python bindings for nabled

pynabled

pynabled is the Python package for nabled, an ndarray-native numerical library for dense linear algebra, decompositions, sparse workflows, tensor routines, and selected ML/statistics operations.

Install

Install the published wheel:

pip install pynabled

pynabled requires Python 3.10+ and NumPy.

Optional features

Published PyPI wheels use the default feature set.

Default wheels include the Rust arrow feature. Python Arrow workflows still require pyarrow at runtime.

Optional provider and accelerator support are source-build workflows that use the same Cargo feature names as the Rust facade:

• openblas-system
• openblas-static
• netlib-system
• netlib-static
• magma-system
• accelerator-rayon
• accelerator-wgpu

For pip / uv source builds, the package exposes friendly build settings:

PYNABLED_PROVIDER="openblas-system magma-system" python -m pip install --no-binary pynabled pynabled
PYNABLED_ACCELERATORS=rayon python -m pip install --no-binary pynabled pynabled

You can pass the same knobs through frontend config settings with pynabled-provider, pynabled-accelerators, and pynabled-features.

Builds can be inspected at runtime with pynabled.build_features().

Build guide: https://github.com/MontOpsInc/nabled/blob/main/BUILD.md

Quick example

import numpy as np
import pynabled

a = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float64)
result = pynabled.svd_decompose(a)

print(result.singular_values)

What it includes

• Dense vector and matrix kernels over numpy.ndarray
• Decompositions including SVD, QR, LU, Cholesky, eigen, Schur, and polar
• Sparse matrix carriers and solver/preconditioner workflows
• Tensor decomposition and reconstruction helpers
• PCA, regression, iterative solvers, Jacobian helpers, and optimization configs
• Optional Arrow and ndarrow interop when built with arrow

API and behavior

numpy.ndarray is the canonical CPU array carrier. Where the Rust API admits borrowed views, pynabled preserves that contract at the Python boundary instead of forcing extra copies.

Structured results are returned as typed Python objects such as SvdResult, QrResult, LuResult, PcaResult, and tensor-specific result types.

Some higher-level convenience APIs still materialize owned arrays internally where the current Rust API shape requires it. Callback-driven Jacobian and optimization helpers also cross back into Python on each evaluation, so they do not have the same performance contract as the direct array-in/array-out kernels.

Project links

• Repository: https://github.com/MontOpsInc/nabled
• Python package docs: https://github.com/MontOpsInc/nabled/blob/main/python/README.md
• Build guide: https://github.com/MontOpsInc/nabled/blob/main/BUILD.md
• Changelog: https://github.com/MontOpsInc/nabled/blob/main/CHANGELOG.md