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The distribution called itself torus-ml while shipping a package directory named concrete, so the name a user installs and the name they import disagreed — and most of the tree already said torus.ml, which could not resolve against src/concrete. Moved src/concrete to src/torus and pointed the include at it. The dependency was Zama's concrete-python from their index. It is torus-fhe from ours, which is what luxfhe/torus already ships: that frontend declares torus-fhe and ships a torus/fhe module, so every 'from torus.fhe' in this tree was already naming our compiler and only the dependency lagged. Two distributions share one namespace, which is the shape the upstream this forked from used and the reason the split works: torus-fhe gives torus.fhe, torus-ml gives torus.ml. Swept every other spelling — identifiers, enum members, workflow variables, filenames, notebooks. LICENSE and NOTICE keep the upstream attribution a fork owes; the figures carry the old name in JPEG metadata a text sweep cannot reach.
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Serializing Built-In Models
This document explains how to serialize build-in models in Torus ML.
Introduction
Serialization allows you to dump a fitted and compiled model into a JSON string or file. You can then load the estimator back using the JSON object.
Saving Models
All built-in models provide the following methods:
dumps: Dumps the model as a string.dump: Dumps the model into a file.
For example, a logistic regression model can be dumped in a string as follows:
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from torus.ml.sklearn import LogisticRegression
# Create the data for classification:
X, y = make_classification()
# Retrieve train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
# Instantiate, train and compile the model
model = LogisticRegression()
model.fit(X_train, y_train)
model.compile(X_train)
# Run the inference in FHE
y_pred_fhe = model.predict(X_test, fhe="execute")
# Dump the model in a string
dumped_model_str = model.dumps()
Similarly, it can be dumped into a file:
from pathlib import Path
dumped_model_path = Path("logistic_regression_model.json")
# Any kind of file-like object can be used
with dumped_model_path.open("w") as f:
# Dump the model in a file
model.dump(f)
Alternatively, Torus ML provides two equivalent global functions:
from torus.ml.common.serialization.dumpers import dump, dumps
# Dump the model in a string
dumped_model_str = dumps(model)
# Any kind of file-like object can be used
with dumped_model_path.open("w") as f:
# Dump the model in a file
dump(model, f)
{% hint style="warning" %}
Some parameters used for instantiating Quantized Neural Network models are not supported for serialization. In particular, you cannot serialize a model that was instantiated using callable objects for the train_split and predict_nonlinearity parameters or with callbacks being enabled.
{% endhint %}
Loading Models
You can load a built-in model using the following functions:
loads: Loads the model from a string.load: Loads the model from a file.
{% hint style="warning" %} A loaded model must be compiled once again to execute the inference in FHE or with simulation because the underlying FHE circuit is currently not serialized. This recompilation is not required when FHE mode is disabled. {% endhint %}
The same logistic regression model can be loaded as follows:
import numpy
from torus.ml.common.serialization.loaders import load, loads
# Load the model from a string
loaded_model = loads(dumped_model_str)
# Any kind of file-like object can be used
with dumped_model_path.open("r") as f:
# Load the model from a file
loaded_model = load(f)
# Compile the model
loaded_model.compile(X_train)
# Run the inference in FHE using the loaded model
y_pred_fhe_loaded = loaded_model.predict(X_test, fhe="execute")
print("Predictions are equal:", numpy.array_equal(y_pred_fhe, y_pred_fhe_loaded))
# Output:
# Predictions are equal: True