14ef38b834
Federated learning plugin for xgboost: * A gRPC server to aggregate MPI-style requests (allgather, allreduce, broadcast) from federated workers. * A Rabit engine for the federated environment. * Integration test to simulate federated learning. Additional followups are needed to address GPU support, better security, and privacy, etc.
79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
#!/usr/bin/python
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import multiprocessing
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import sys
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import time
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import xgboost as xgb
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import xgboost.federated
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SERVER_KEY = 'server-key.pem'
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SERVER_CERT = 'server-cert.pem'
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CLIENT_KEY = 'client-key.pem'
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CLIENT_CERT = 'client-cert.pem'
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def run_server(port: int, world_size: int) -> None:
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xgboost.federated.run_federated_server(port, world_size, SERVER_KEY, SERVER_CERT,
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CLIENT_CERT)
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def run_worker(port: int, world_size: int, rank: int) -> None:
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# Always call this before using distributed module
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rabit_env = [
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f'federated_server_address=localhost:{port}',
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f'federated_world_size={world_size}',
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f'federated_rank={rank}',
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f'federated_server_cert={SERVER_CERT}',
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f'federated_client_key={CLIENT_KEY}',
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f'federated_client_cert={CLIENT_CERT}'
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]
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xgb.rabit.init([e.encode() for e in rabit_env])
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# Load file, file will not be sharded in federated mode.
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dtrain = xgb.DMatrix('agaricus.txt.train-%02d' % rank)
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dtest = xgb.DMatrix('agaricus.txt.test-%02d' % rank)
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# Specify parameters via map, definition are same as c++ version
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param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
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# Specify validations set to watch performance
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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num_round = 20
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# Run training, all the features in training API is available.
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# Currently, this script only support calling train once for fault recovery purpose.
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bst = xgb.train(param, dtrain, num_round, evals=watchlist, early_stopping_rounds=2)
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# Save the model, only ask process 0 to save the model.
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if xgb.rabit.get_rank() == 0:
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bst.save_model("test.model.json")
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xgb.rabit.tracker_print("Finished training\n")
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# Notify the tracker all training has been successful
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# This is only needed in distributed training.
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xgb.rabit.finalize()
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def run_test() -> None:
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port = 9091
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world_size = int(sys.argv[1])
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server = multiprocessing.Process(target=run_server, args=(port, world_size))
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server.start()
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time.sleep(1)
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if not server.is_alive():
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raise Exception("Error starting Federated Learning server")
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workers = []
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for rank in range(world_size):
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worker = multiprocessing.Process(target=run_worker, args=(port, world_size, rank))
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workers.append(worker)
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worker.start()
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for worker in workers:
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worker.join()
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server.terminate()
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if __name__ == '__main__':
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run_test()
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