creating_models.py

import shutil
import sys
from pathlib import Path

from concrete.ml.deployment import FHEModelDev
from concrete.ml.deployment import FHEModelClient


def compile_and_make_it_deployable(model_dev, X_train):

    path_to_model = Path("compiled_model")

    # Compile into FHE
    model_dev.compile(X_train)

    # Saving the model
    shutil.rmtree(path_to_model, ignore_errors=True)
    fhemodel_dev = FHEModelDev(path_to_model, model_dev)
    fhemodel_dev.save(via_mlir=True)


# BEGIN: insert your ML task here
# Typically
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

from concrete.ml.sklearn import LogisticRegression

x, y = make_classification(n_samples=1000, class_sep=2, n_features=30, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

model_dev = LogisticRegression()
model_dev.fit(X_train, y_train)
# END: insert your ML task here

compile_and_make_it_deployable(model_dev, X_train)
print("Your model is ready to be deployable.")