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luml.integrations.sklearn.packaging

save_sklearn​

def save_sklearn(
        estimator: "BaseEstimator",
        inputs: Any,
        path: str | None = None,
        dependencies: Literal["default"] | Literal["all"]
    | list[str] = "default",
        extra_dependencies: list[str] | None = None,
        extra_code_modules: list[str] | Literal["auto"] | None = None,
        manifest_model_name: str | None = None,
        manifest_model_version: str | None = None,
        manifest_model_description: str | None = None,
        manifest_extra_producer_tags: list[str] | None = None
) -> ModelReference

Save scikit-learn model to LUML format for deployment.

Packages a trained sklearn estimator with its dependencies and input/output schema for production deployment or model registry.

Arguments:

  • estimator - Trained scikit-learn estimator (must implement .predict()).
  • inputs - Example input data for schema inference. Can be numpy array or pandas DataFrame.
  • path - Output file path. Auto-generated if not provided.
  • dependencies - Dependency management strategy:
    • "default": Include scikit-learn, numpy, scipy, cloudpickle
    • "all": Auto-detect all dependencies
    • list: Custom dependency list
  • extra_dependencies - Additional pip packages to include.
  • extra_code_modules - Local code modules to bundle.
    • None: Don't include local modules
    • "auto": Auto-detect local dependencies
    • list: Specific modules to include
  • manifest_model_name - Model name for metadata.
  • manifest_model_version - Model version for metadata.
  • manifest_model_description - Model description for metadata.
  • manifest_extra_producer_tags - Additional tags for model metadata.

Returns:

  • ModelReference - Reference to the saved model package.

Example:

from sklearn.ensemble import RandomForestClassifier
from luml.integrations.sklearn import save_sklearn
import numpy as np

# Train model
model = RandomForestClassifier()
X_train = np.random.rand(100, 4)
y_train = np.random.randint(0, 2, 100)
model.fit(X_train, y_train)

# Save model
model_ref = save_sklearn(
    model,
    X_train,
    path="my_model.luml",
    manifest_model_name="iris_classifier",
    manifest_model_version="1.0.0"
)