Concrete ML has support for serializing all available built-in models. Using this feature, one can dump a fitted and compiled model into a JSON string or file. The estimator can then be loaded 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 below.
from sklearn.datasets import make_classificationfrom sklearn.model_selection import train_test_splitfrom concrete.ml.sklearn import LogisticRegression# Create the data for classification:X, y =make_classification()# Retrieve train and test setsX_train, X_test, y_train, y_test =train_test_split(X, y, test_size=0.4)# Instantiate, train and compile the modelmodel =LogisticRegression()model.fit(X_train, y_train)model.compile(X_train)# Run the inference in FHEy_pred_fhe = model.predict(X_test, fhe="execute")# Dump the model in a stringdumped_model_str = model.dumps()
Similarly, it can be dumped into a file.
from pathlib import Pathdumped_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, Concrete ML provides two equivalent global functions.
from concrete.ml.common.serialization.dumpers import dump, dumps# Dump the model in a stringdumped_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 filedump(model, f)
Some parameters used for instantiating Quantized Neural Network models are not supported for serialization. In particular, one cannot serialize a model that was instantiated using callable objects for the train_split and predict_nonlinearity parameters or with callbacks being enabled.
Loading Models
Loading a built-in model is possible through the following functions:
loads: loads the model from a string.
load: loads the model from a file.
A loaded model is required to be compiled once again in order for a user to be able to execute the inference in FHE or with simulation. This is because the underlying FHE circuit is currently not serialized. There is not required when FHE mode is disabled.
The above logistic regression model can therefore be loaded as below.
import numpyfrom concrete.ml.common.serialization.loaders import load, loads# Load the model from a stringloaded_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 modelloaded_model.compile(X_train)# Run the inference in FHE using the loaded modely_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