cfd2a9f872
- Move test files. - Run spark and dask separately to prevent conflicts. - Gather common code into the testing module.
88 lines
3.0 KiB
Python
88 lines
3.0 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.federated
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import xgboost as xgb
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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, with_ssl: bool) -> None:
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if with_ssl:
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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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else:
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xgboost.federated.run_federated_server(port, world_size)
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def run_worker(port: int, world_size: int, rank: int, with_ssl: bool, with_gpu: bool) -> None:
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communicator_env = {
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'xgboost_communicator': 'federated',
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'federated_server_address': f'localhost:{port}',
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'federated_world_size': world_size,
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'federated_rank': rank
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}
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if with_ssl:
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communicator_env['federated_server_cert'] = SERVER_CERT
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communicator_env['federated_client_key'] = CLIENT_KEY
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communicator_env['federated_client_cert'] = CLIENT_CERT
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# Always call this before using distributed module
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with xgb.collective.CommunicatorContext(**communicator_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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if with_gpu:
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param['tree_method'] = 'gpu_hist'
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param['gpu_id'] = rank
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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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bst = xgb.train(param, dtrain, num_round, evals=watchlist,
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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.collective.get_rank() == 0:
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bst.save_model("test.model.json")
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xgb.collective.communicator_print("Finished training\n")
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def run_federated(with_ssl: bool = True, with_gpu: bool = False) -> 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, with_ssl))
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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,
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args=(port, world_size, rank, with_ssl, with_gpu))
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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_federated(with_ssl=True, with_gpu=False)
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run_federated(with_ssl=False, with_gpu=False)
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run_federated(with_ssl=True, with_gpu=True)
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run_federated(with_ssl=False, with_gpu=True)
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