skeights 0.1.1

Serialize fitted scikit-learn models to safetensors + JSON

skeights

Serialize fitted scikit-learn models to safetensors + JSON.

No pickle. No joblib. Just weights and config.

Why?

Pickle is the default way to save sklearn models, but it's:

• Insecure: arbitrary code execution on load
• Fragile: breaks across sklearn versions, Python versions, and platforms
• Opaque: you can't inspect what's inside

skops solves the security problem by replacing pickle with a safe binary format, but the output is still a single opaque blob; you can't easily inspect the hyperparameters or diff two versions of a model.

skeights separates structure from weights:

• .json: hyperparameters and scalar fitted state, human-readable and diffable
• .safetensors: numeric arrays (weights, fitted params) in a safe, fast, widely-supported format

Install

pip install skeights

Usage

import skeights
from sklearn.linear_model import Ridge
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

# Fit your model as usual
pipe = Pipeline([
    ("scaler", StandardScaler()),
    ("model", Ridge(alpha=0.1)),
])
pipe.fit(X_train, y_train)

# Save
skeights.save(pipe, "model.safetensors", "model.json")

# Load and predict
loaded = skeights.load("model.safetensors", "model.json")
predictions = loaded.predict(X_test)

Supported estimators

• Linear models: Ridge, Lasso, LinearRegression, LogisticRegression, etc.
• MLPRegressor / Classifier: multi-layer perceptron
• DecisionTreeRegressor / Classifier: full tree serialization
• RandomForestRegressor / Classifier: full tree serialization
• GradientBoostingRegressor / Classifier: including init estimator
• HistGradientBoostingRegressor / Classifier: including bin mapper state
• LGBMRegressor / Classifier: via booster model string
• XGBRegressor / Classifier: via booster JSON
• GaussianProcessRegressor / Classifier: including composite kernels
• TransformedTargetRegressor: target scaling wrappers
• Scalers: StandardScaler, MinMaxScaler, RobustScaler
• Pipelines: any Pipeline composed of supported estimators

Compatibility

skeights requires scikit-learn >= 1.5 and tests against 1.5, 1.6, and latest in CI.

Saved models are forward-compatible on a best-effort basis: we test loading sklearn 1.5 fixtures on newer versions, but don't guarantee cross-version compatibility.

When loading a model saved with a different sklearn version, skeights will emit a warning.

License

MIT