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- import argparse
- from pipeline.backend.pipeline import PipeLine
- from pipeline.component import DataTransform
- from pipeline.component.hetero_ftl import HeteroFTL
- from pipeline.component.reader import Reader
- from pipeline.interface.data import Data
- from tensorflow.keras import optimizers
- from tensorflow.keras.layers import Dense
- from tensorflow.keras import initializers
- from pipeline.component.evaluation import Evaluation
- from pipeline.utils.tools import load_job_config
- def main(config="../../config.yaml", namespace=""):
-
- if isinstance(config, str):
- config = load_job_config(config)
- parties = config.parties
- guest = parties.guest[0]
- host = parties.host[0]
- guest_train_data = {"name": "nus_wide_guest", "namespace": f"experiment{namespace}"}
- host_train_data = {"name": "nus_wide_host", "namespace": f"experiment{namespace}"}
- pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host)
- reader_0 = Reader(name="reader_0")
- reader_0.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data)
- reader_0.get_party_instance(role='host', party_id=host).component_param(table=host_train_data)
- data_transform_0 = DataTransform(name="data_transform_0")
- data_transform_0.get_party_instance(
- role='guest', party_id=guest).component_param(
- with_label=True, output_format="dense")
- data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=False)
- hetero_ftl_0 = HeteroFTL(name='hetero_ftl_0',
- epochs=10, alpha=1, batch_size=-1, mode='encrypted')
- hetero_ftl_0.add_nn_layer(Dense(units=32, activation='sigmoid',
- kernel_initializer=initializers.RandomNormal(stddev=1.0),
- bias_initializer=initializers.Zeros()))
- hetero_ftl_0.compile(optimizer=optimizers.Adam(lr=0.01))
- evaluation_0 = Evaluation(name='evaluation_0', eval_type="binary")
- pipeline.add_component(reader_0)
- pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
- pipeline.add_component(hetero_ftl_0, data=Data(train_data=data_transform_0.output.data))
- pipeline.add_component(evaluation_0, data=Data(data=hetero_ftl_0.output.data))
- pipeline.compile()
- pipeline.fit()
- if __name__ == "__main__":
- parser = argparse.ArgumentParser("PIPELINE DEMO")
- parser.add_argument("-config", type=str,
- help="config file")
- args = parser.parse_args()
- if args.config is not None:
- main(args.config)
- else:
- main()
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