This release is a pre-release and may not be stable for production use.
omle-runtime
Fast inference for classical ML models, with a scikit-learn-style API.
omle-runtime loads .omle models and scores them through a native C++ runtime
with pybind11 bindings. Wheels are self-contained — protobuf and Abseil are
linked in statically, so there is nothing to install alongside.
import numpy as np
import omleruntime as omr
model = omr.load("model.omle", n_threads=4) # thread-safe
X = np.random.randn(1000, 10).astype(np.float32)
scores = model.predict(X) # (n_samples,)
proba = model.predict_proba(X) # (n_samples, n_outputs)
Model implements the scikit-learn estimator interface (predict,
predict_proba, fit, get_params, set_params, n_features_in_,
feature_names_in_), so it can be the final step of a Pipeline:
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
pipe = Pipeline([("scaler", StandardScaler()), ("model", model)])
pipe.predict(X)
For repeated scoring on a thread, create a session once and reuse it:
session = model.create_session() # one per thread
scores = session.predict(X)
Related packages
omle— the model IR and convertersomle-spark— PySpark transformer
License
Apache-2.0
1 maintainer
Release files for omle-runtime 0.1.0rc4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 134.2 MB
Release files / omle_runtime-0.1.0rc4-cp314-cp314-win_amd64.whl
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twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / omle_runtime-0.1.0rc4-cp310-cp310-macosx_11_0_arm64.whl
| Download URL | omle_runtime-0.1.0rc4-cp310-cp310-macosx_11_0_arm64.whl |
|---|---|
| Size | 4.6 MB |
| Tags | CPython 3.10 macOS 11.0+ ARM64 |
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b667fe707fa421c9b4280ca15660f3eb058afd3a333ca7df8caee1cbaca0f296
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twine/7.0.0 CPython/3.13.14
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