def test_basic_rpc(self):
channel_addr = "127.0.0.1:50052"
xgb.init_client(user_name=username, sym_key_file=sym_key_file, priv_key_file=priv_key_file, cert_file=cert_file, remote_addr=channel_addr)
xgb.attest(verify=False)
dtrain = xgb.DMatrix({username: dpath + 'agaricus.txt.train.enc'})
dtest = xgb.DMatrix({username: dpath + 'agaricus.txt.test.enc'})
# Set training parameters
params = {
"tree_method": "hist",
"n_gpus": "0",
"objective": "binary:logistic",
"min_child_weight": "1",
"gamma": "0.1",
"max_depth": "5",
"verbosity": "0"
}
num_rounds = 2
booster = xgb.train(params, dtrain, num_rounds)
predictions, num_preds = booster.predict(dtest, decrypt=False)
preds = booster.decrypt_predictions(predictions, num_preds)
ten_preds = preds[:10]
labels = [0, 1, 0, 0, 0, 0, 1, 0, 1, 0]
err = sum(1 for i in range(len(ten_preds))
if int(ten_preds[i] > 0.5) != labels[i]) / float(len(ten_preds))
# error must be smaller than 10%
assert err < 0.1
def run(channel_addr, sym_key_file, priv_key_file, cert_file):
xgb.init_client(user_name=username, client_list=["user1", username], sym_key_file=sym_key_file, priv_key_file=priv_key_file, cert_file=cert_file, remote_addr=channel_addr)
xgb.rabit.init()
# Remote attestation
print("Remote attestation")
# Note: Simulation mode does not support attestation
# pass in `verify=False` to attest()
xgb.attest()
print("Report successfully verified")
print("Load training matrices")
dtrain = xgb.DMatrix({"user1": HOME_DIR + "demo/python/multiclient-cluster-remote-control/data/c1_train.enc", username: HOME_DIR + "demo/python/multiclient-cluster-remote-control/data/c2_train.enc"}, encrypted=True)
print("Creating test matrix")
dtest1 = xgb.DMatrix({"user1": HOME_DIR + "demo/python/multiclient-cluster-remote-control/data/c1_test.enc"})
dtest2 = xgb.DMatrix({username: HOME_DIR + "demo/python/multiclient-cluster-remote-control/data/c2_test.enc"})
print("Beginning Training")
# Set training parameters
params = {
"tree_method": "hist",
"n_gpus": "0",
"objective": "binary:logistic",
"min_child_weight": "1",
"gamma": "0.1",
"max_depth": "3",
"verbosity": "0"
}
# Train and evaluate
num_rounds = 10
print("Training...")
booster = xgb.train(params, dtrain, num_rounds)
# Enable the other party to get its predictions
_, _ = booster.predict(dtest1, decrypt=False)
# Get our predictions
predictions, num_preds = booster.predict(dtest2, decrypt=False)
# Decrypt predictions
print("Predictions: ", booster.decrypt_predictions(predictions, num_preds)[:10])
# Get fscores of model
print("\nModel Feature Importance: ")
print(booster.get_fscore())
xgb.rabit.finalize()
"DMLC_NUM_SERVER": os.environ.get("DMLC_NUM_SERVER"),
"DMLC_TRACKER_URI": os.environ.get("DMLC_TRACKER_URI"),
"DMLC_TRACKER_PORT": os.environ.get("DMLC_TRACKER_PORT"),
"DMLC_ROLE": os.environ.get("DMLC_ROLE"),
"DMLC_NODE_HOST": os.environ.get("DMLC_NODE_HOST")
}
rargs = [str.encode(str(k) + "=" + str(v)) for k, v in rabit_args.items()]
xgb.rabit.init(rargs)
# Remote Attestation
print("Remote attestation")
# Note: Simulation mode does not support attestation
# pass in `verify=False` to attest()
xgb.attest()
print("Creating training matrix from encrypted file")
dtrain = xgb.DMatrix({username: HOME_DIR + "demo/data/agaricus.txt.train.enc"})
print("Creating test matrix from encrypted file")
dtest = xgb.DMatrix({username: HOME_DIR + "demo/data/agaricus.txt.test.enc"})
print("Beginning Training")
# Set training parameters
params = {
"tree_method": "hist",
"n_gpus": "0",
"objective": "binary:logistic",
"min_child_weight": "1",
import os
import securexgboost as xgb
username = "******"
HOME_DIR = os.path.dirname(os.path.realpath(__file__)) + "/../../"
sym_key_file = HOME_DIR + "demo/data/key_zeros.txt"
priv_key_file = HOME_DIR + "config/user1.pem"
cert_file = HOME_DIR + "config/user1.crt"
xgb.init_client(user_name=username,
sym_key_file=sym_key_file,
priv_key_file=priv_key_file,
cert_file=cert_file)
xgb.init_server(enclave_image=HOME_DIR +
"build/enclave/xgboost_enclave.signed")
xgb.attest(verify=False)
def run(channel_addr, sym_key_file, priv_key_file, cert_file):
# Remote attestation
print("Remote attestation")
xgb.init_client(user_name=username,
sym_key_file=sym_key_file,
priv_key_file=priv_key_file,
cert_file=cert_file,
remote_addr=channel_addr)
# Note: Simulation mode does not support attestation
# pass in `verify=False` to attest()
xgb.attest()
print("Report successfully verified")
print("Creating training matrix")
dtrain = xgb.DMatrix(
{username: HOME_DIR + "demo/python/remote-control/data/train.enc"})
if not dtrain:
print("Error creating dtrain")
return
print("dtrain: " + dtrain.handle.value.decode("utf-8"))
print("Creating test matrix")
dtest = xgb.DMatrix(
{username: HOME_DIR + "demo/python/remote-control/data/test.enc"})
if not dtest:
print("Error creating dtest")
return
print("dtest: " + dtest.handle.value.decode("utf-8"))
print("Beginning Training")
# Set training parameters
params = {
"tree_method": "hist",
"n_gpus": "0",
"objective": "binary:logistic",
"min_child_weight": "1",
"gamma": "0.1",
"max_depth": "3",
"verbosity": "0"
}
# Train and evaluate
num_rounds = 5
print("Training...")
booster = xgb.train(params, dtrain, num_rounds)
print("booster: " + booster.handle.value.decode("utf-8"))
booster.save_model(HOME_DIR +
"demo/python/remote-control/client/modelfile.model")
# Get encrypted predictions
print("\nModel Predictions: ")
predictions, num_preds = booster.predict(dtest, decrypt=False)
# Decrypt predictions
print(booster.decrypt_predictions(predictions, num_preds))
# Get fscores of model
print("\nModel Feature Importance: ")
print(booster.get_fscore())
