renewable-huber-native-cpu
Optional Rust/Rayon CPU engine for
renewable-huber. Version
0.7.1 requires exactly renewable-huber==0.7.1; the base package owns the
public estimator API, validation and portable checkpoint format.
python -m pip install renewable-huber-native-cpu==0.7.1
Release wheels support CPython 3.10–3.13 on Windows x86-64, manylinux2014 x86-64/aarch64 and macOS x86-64/Apple Silicon. Installing a wheel does not require Rust.
from renewable_huber import RenewableHuberRegressor
model = RenewableHuberRegressor(backend="native_cpu", n_jobs=-1)
model.fit(X, y)
n_jobs=None uses the shared Rayon pool, -1 uses all available logical CPUs,
and a positive integer creates an estimator-local pool of that size. When an
outer joblib or GridSearchCV layer is parallel, use n_jobs=1 to avoid nested
oversubscription.
CPU backend="auto" may select this engine for a sufficiently large workload
when a bounded, host-local runtime calibration demonstrates a conservative
advantage. It never reads a CPU model string or persists a machine-specific
crossover map. Explicit backend="native_cpu" never silently falls back.
Inputs are dense float32/float64 host arrays and predictions are NumPy arrays. See the project support matrix for the complete contract.
1 maintainer
Metadata
1 maintainer
Release files for renewable-huber-native-cpu 0.7.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 8.3 MB
Release files / renewable_huber_native_cpu-0.7.1-cp313-cp313-win_amd64.whl
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| Tags | CPython 3.13 Windows x86-64 |
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Transparency logRelease files / renewable_huber_native_cpu-0.7.1-cp310-cp310-macosx_10_12_x86_64.whl
| Download URL | renewable_huber_native_cpu-0.7.1-cp310-cp310-macosx_10_12_x86_64.whl |
|---|---|
| Size | 436.4 kB |
| Tags | CPython 3.10 macOS 10.12+ x86-64 |
|
SHA-256 checksum How to use checksums |
64249d0c14b435c6a842f4289bf17898435a28bb021b375cfa201ef80b7c8514
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BLAKE2b-256 checksum How to use checksums |
fbbdc483e938225e29693617e5dd0c70ed54282eeda0fa8a2b7d2499fcab8015
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
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Transparency log
