concrete-ml-encrypted-logreg / play_with_endpoint.py
binoua's picture
chore: be closer to our template
1bb01e9
raw
history blame
4.09 kB
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__"])
API_URL = "https://puqif7goarh132kl.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")
# Logistic regression in FHE
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
evaluation_keys_remaining = evaluation_keys[:]
uid = None
is_first = True
is_finished = False
i = 0
packet_size = 1024 * 1024 * 100
while not is_finished:
# Send by packets of 100M
if sys.getsizeof(evaluation_keys_remaining) > packet_size:
evaluation_keys_piece = evaluation_keys_remaining[:packet_size]
evaluation_keys_remaining = evaluation_keys_remaining[packet_size:]
else:
evaluation_keys_piece = evaluation_keys_remaining
is_finished = True
print(
f"Sending {i}-th piece of the key (remaining size is {sys.getsizeof(evaluation_keys_remaining) / 1024:.2f} kbytes)"
)
i += 1
if is_first:
is_first = False
payload = {
"inputs": "fake",
"evaluation_keys": to_json(evaluation_keys_piece),
"method": "save_key",
}
uid = query(payload)["uid"]
print(f"Storing the key in the database under {uid=}")
else:
payload = {
"inputs": "fake",
"evaluation_keys": to_json(evaluation_keys_piece),
"method": "append_key",
"uid": uid,
}
query(payload)
# 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:.2f} 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")