This release is a pre-release and may not be stable for production use.
fastscipy
Faster SciPy calls. SciPy-compatible results.
Portable Rust kernels and shared thread pools accelerate supported calls. Everything else stays with SciPy.
pip install --pre fastscipy
import fastscipy
fastscipy.patch()
from scipy import ndimage
clean = ndimage.median_filter(image, size=3)
Demo recorded on a MacBook Pro with M5 Pro.
Install and use
Prebuilt wheels for Python 3.12–3.15 · SciPy 1.18; no Rust required. Supports x86_64 and ARM64 on Linux (glibc 2.28+ or musl 1.2+), macOS 14+, and Windows. Platform details and measurements →
Call patch() before importing libraries that use SciPy.
Use patch(threads=4) to choose a thread budget, status() to see what's enabled,
and unpatch() to restore SciPy.
Performance across platforms
Geometric-mean speedups at two threads, with BLAS limited to one. These portability checkpoints use Python 3.13, NumPy 2.5.4, and SciPy 1.18.1; each comparison uses the same machine and inputs.
| Platform | ndimage | stats | linalg |
|---|---|---|---|
| Linux x86_64 · glibc | 15.1× | 5.9× | 3.9× |
| Linux ARM64 · glibc | 13.2× | 6.5× | 4.0× |
| Linux x86_64 · musl | 12.3× | 6.0× | 5.1× |
| Linux ARM64 · musl | 11.8× | 6.4× | 4.0× |
| macOS Apple Silicon | 16.9× | 5.9× | 4.8× |
| macOS Intel | 13.0× | 5.7× | 6.5× |
| Windows x86_64 | 11.7× | 5.9× | 4.7× |
| Windows ARM64 | 11.7× | 6.4× | 4.2× |
The suite covers 407 cases across 13 areas; a case is one function and input combination. The three columns summarize 43 image-processing, 58 statistics, and 49 linear-algebra cases. Results vary by workload, hardware, and SciPy build. Platform measurements and limitations · Original M5 Pro benchmarks
More
Coverage · How it works · Demo source · Contributing
BSD-3-Clause. SciPy ports retain SciPy's license. Independent of the SciPy project.
1 maintainer
Metadata
1 maintainer
Release files for fastscipy 0.1.0a4
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