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import numpy as np
import time
import os, sys
from pathlib import Path
from concrete.ml.deployment import FHEModelClient
import requests
def to_json(python_object):
if isinstance(python_object, bytes):
return {"__class__": "bytes", "__value__": list(python_object)}
raise TypeError(repr(python_object) + " is not JSON serializable")
def from_json(python_object):
if "__class__" in python_object:
return bytes(python_object["__value__"])
# TODO: put the right link `API_URL` for your entryp point
API_URL = "https://XXXXXXX.us-east-1.aws.endpoints.huggingface.cloud"
headers = {
"Authorization": "Bearer " + os.environ.get("HF_TOKEN"),
"Content-Type": "application/json",
}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
if "error" in response:
assert False, f"Got an error: {response=}"
return response.json()
path_to_model = Path("compiled_model")
# BEGIN: replace this part with your privacy-preserving application
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
x, y = make_classification(n_samples=1000, class_sep=2, n_features=30, random_state=42)
_, X_test, _, Y_test = train_test_split(x, y, test_size=0.2, random_state=42)
# Recover parameters for client side
fhemodel_client = FHEModelClient(path_to_model)
# Generate the keys
fhemodel_client.generate_private_and_evaluation_keys()
evaluation_keys = fhemodel_client.get_serialized_evaluation_keys()
# Save the key in the database
payload = {
"inputs": "fake",
"evaluation_keys": to_json(evaluation_keys),
"method": "save_key",
}
uid = query(payload)["uid"]
print(f"Storing the key in the database under {uid=}")
# Test the handler
nb_good = 0
nb_samples = len(X_test)
verbose = True
time_start = time.time()
duration = 0
is_first = True
for i in range(nb_samples):
# Quantize the input and encrypt it
encrypted_inputs = fhemodel_client.quantize_encrypt_serialize([X_test[i]])
# Prepare the payload
payload = {
"inputs": "fake",
"encrypted_inputs": to_json(encrypted_inputs),
"method": "inference",
"uid": uid,
}
if is_first:
print(f"Size of the payload: {sys.getsizeof(payload) / 1024} kilobytes")
is_first = False
# Run the inference on HF servers
duration -= time.time()
duration_inference = -time.time()
encrypted_prediction = query(payload)
duration += time.time()
duration_inference += time.time()
encrypted_prediction = from_json(encrypted_prediction)
# Decrypt the result and dequantize
prediction_proba = fhemodel_client.deserialize_decrypt_dequantize(encrypted_prediction)[0]
prediction = np.argmax(prediction_proba)
if verbose:
print(
f"for {i}-th input, {prediction=} with expected {Y_test[i]} in {duration_inference:.3f} seconds"
)
# Measure accuracy
nb_good += Y_test[i] == prediction
print(f"Accuracy on {nb_samples} samples is {nb_good * 1. / nb_samples}")
print(f"Total time: {time.time() - time_start:.3f} seconds")
print(f"Duration per inference: {duration / nb_samples:.3f} seconds")
# END: replace this part with your privacy-preserving application
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