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535 lines
18 KiB
535 lines
18 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Defination of Role Makers."""
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import os
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import numpy as np
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from multiprocessing import Process, Manager
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import paddle.fluid as fluid
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__all__ = ['RoleMakerBase', 'UserDefinedRoleMaker', 'PaddleCloudRoleMaker']
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class Role:
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WORKER = 1
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SERVER = 2
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class RoleMakerBase(object):
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"""
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RoleMakerBase is a base class for assigning a role to current process
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in distributed training.
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A paddle developer can implement RoleMakerBase to design a role maker
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for worker or pserver assignment.
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"""
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def __init__(self):
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self._worker_endpoints = []
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self._server_endpoints = []
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self._role_is_generated = False
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self._role = None
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self._current_id = -1
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self._node_type = None
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self._node_type_comm = None
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self._all_comm = None
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def is_worker(self):
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"""
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return is_worker() of current process
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"""
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raise NotImplementedError("Please implement this method in child class")
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def is_server(self):
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"""
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return is_server() of current process
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"""
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raise NotImplementedError("Please implement this method in child class")
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def is_first_worker(self):
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"""
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Check whether the node is the first instance of worker.
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Returns:
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bool: True if this is the first node of worker,
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False if not.
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"""
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raise NotImplementedError("Please implement this method in child class")
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def worker_num(self):
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"""
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Get current total worker number.
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Returns:
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int: worker number
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"""
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raise NotImplementedError("Please implement this method in child class")
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def server_num(self):
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"""
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Get current total server number.
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Returns:
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int: server number
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"""
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raise NotImplementedError("Please implement this method in child class")
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def worker_index(self):
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"""
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Get current worker id.
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Returns:
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int: node id
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"""
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raise NotImplementedError("Please implement this method in child class")
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def server_index(self):
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"""
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Get current server id.
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Returns:
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int: node id
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"""
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raise NotImplementedError("Please implement this method in child class")
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def role_id(self):
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"""
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Get current id.
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Returns:
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int: node id
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"""
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raise NotImplementedError("Please implement this method in child class")
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def get_trainer_endpoints(self):
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"""
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return trainer endpoints
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"""
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return self._worker_endpoints
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def get_pserver_endpoints(self):
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"""
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return pserver endpoints
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"""
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return self._server_endpoints
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def to_string(self):
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return "role: {}, current_id: {}, worker_endpoints: {}, server_endpoints: {}".format(
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self._role, self._current_id, self._worker_endpoints,
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self._server_endpoints)
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def _all_gather(self, comm_world, input):
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"""
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Args:
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input(int|float): input value
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Returns:
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return a list of values
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"""
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print("warning: RoleMakerBase does not have all gather.")
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return None
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def _all_reduce(self, comm_world, input, mode="sum"):
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"""
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Args:
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input(list/numpy.array): array of one dim
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output(list/numpy.array): array of one dim
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mode(str): "sum" or "min" or "max"
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"""
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print("warning: RoleMakerBase does not have all reduce worker.")
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return None
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def _barrier(self, comm_world):
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"""
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barrier between trainers if current role is TRAINER
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"""
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print("warning: RoleMakerBase does not have barrier worker.")
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class PaddleCloudRoleMaker(RoleMakerBase):
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def __init__(self, is_collective=False, init_gloo=True, **kwargs):
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super(PaddleCloudRoleMaker, self).__init__()
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self._is_collective = is_collective
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self._init_gloo = init_gloo
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self._kwargs = kwargs
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self._role_is_generated = False
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self._server_endpoints = None
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self._worker_endpoints = None
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self._node_type_comm = None
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self._all_comm = None
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if not self._is_collective:
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self._hdfs_name = kwargs.get("hdfs_name", "")
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self._hdfs_ugi = kwargs.get("hdfs_ugi", "")
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self._hdfs_path = kwargs.get("path", "").rstrip("/")
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self._init_timeout_seconds = kwargs.get("init_timeout_seconds",
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3600)
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self._run_timeout_seconds = kwargs.get("run_timeout_seconds",
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9999999)
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ip_port = kwargs.get("http_ip_port", "")
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self._http_ip_port = []
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self._http_server = None
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# if ip_port is not empty, it will use http instead of hdfs
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if ip_port != "":
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self._http_ip_port = ip_port.split(":")
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# it's for communication between processes
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self._manager = Manager()
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# global dict to store status
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self._http_server_d = self._manager.dict()
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# set running status of http server
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self._http_server_d["running"] = False
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self._iface = self.__get_default_iface()
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# this environment variable can be empty
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self._prefix = os.getenv("SYS_JOB_ID", "")
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def _barrier(self, comm_world):
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if comm_world:
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comm_world.barrier()
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def _all_gather(self, comm_world, input):
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if comm_world:
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self._barrier(comm_world)
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output = comm_world.all_gather(input)
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return output
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else:
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return None
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def _all_reduce(self, comm_world, input, mode="sum"):
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if not comm_world:
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return None
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input = np.array(input)
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input_shape = input.shape
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input_list = input.reshape(-1).tolist()
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self._barrier(comm_world)
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ans = comm_world.all_reduce(input_list, mode)
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output = np.array(ans).reshape(input_shape)
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return output
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def is_worker(self):
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"""
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whether current process is worker
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"""
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if not self._role_is_generated:
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self.generate_role()
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return self._role == Role.WORKER
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def is_server(self):
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"""
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whether current process is server
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"""
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if not self._role_is_generated:
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self.generate_role()
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return self._role == Role.SERVER
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def is_first_worker(self):
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"""
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whether current process is worker of rank 0
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"""
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if not self._role_is_generated:
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self.generate_role()
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return self._role == Role.WORKER and self._current_id == 0
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def worker_index(self):
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"""
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get index of current worker
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"""
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if not self._role_is_generated:
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self.generate_role()
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return self._current_id
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def server_index(self):
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"""
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get index of current server
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"""
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if not self._role_is_generated:
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self.generate_role()
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return self._current_id
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def role_id(self):
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"""
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get index of current node
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"""
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if self.is_server():
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return self.server_index()
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elif self.is_worker():
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return self.worker_index()
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def worker_num(self):
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"""
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retrun the current number of worker
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"""
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if not self._role_is_generated:
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self.generate_role()
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return self._trainers_num
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def server_num(self):
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"""
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return the current number of server
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"""
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if not self._role_is_generated:
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self.generate_role()
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return self._trainers_num
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def get_trainer_endpoints(self):
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"""
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get endpoint of all trainers
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"""
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if not self._role_is_generated:
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self.generate_role()
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return self._worker_endpoints
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def get_pserver_endpoints(self):
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"""
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get endpoint of all pservers
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"""
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if not self._role_is_generated:
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self.generate_role()
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return self._server_endpoints
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def _get_rank(self):
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"""
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get current rank in all workers and pservers
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"""
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if not self._role_is_generated:
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self.generate_role()
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return self._rank
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def _get_size(self):
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"""
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get total num of all workers and pservers
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"""
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if not self._role_is_generated:
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self.generate_role()
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return self._size
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def _ps_env(self):
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try:
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# Environment variable PADDLE_PSERVERS_IP_PORT_LIST must be set
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# format: string(ip:port), eg. 127.0.0.1:6001
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self._server_endpoints = os.environ[
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"PADDLE_PSERVERS_IP_PORT_LIST"].split(",")
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self._worker_endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS",
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"").split(",")
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trainers_num = int(os.environ["PADDLE_TRAINERS_NUM"])
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training_role = os.environ["TRAINING_ROLE"]
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if training_role not in ["TRAINER", "PSERVER"]:
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raise ValueError("TRAINING_ROLE must be PSERVER or TRAINER")
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if training_role == "TRAINER":
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role = Role.WORKER
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current_id = int(os.environ["PADDLE_TRAINER_ID"])
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if len(self._worker_endpoints) > 0:
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self._cur_endpoint = self._worker_endpoints[current_id]
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elif training_role == "PSERVER":
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role = Role.SERVER
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port = os.environ["PADDLE_PORT"]
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ip = os.environ["POD_IP"]
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self._cur_endpoint = ip + ":" + port
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current_id = self._server_endpoints.index(self._cur_endpoint)
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else:
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raise ValueError("TRAINING_ROLE must be PSERVER or TRAINER")
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except ValueError as ve:
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raise ValueError(
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"something wrong with PaddleCloud, please check environment")
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self._trainers_num = trainers_num
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self._role = role
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self._current_id = current_id
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def _collective_env(self):
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self._current_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
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self._training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
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assert (self._training_role == "TRAINER")
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self._worker_endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS")
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self._cur_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
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assert self._worker_endpoints is not None, "can't find PADDLE_TRAINER_ENDPOINTS"
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self._worker_endpoints = self._worker_endpoints.split(",")
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self._trainers_num = len(self._worker_endpoints)
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def _init_gloo_env(self):
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def init_gloo_instance(role="trainer"):
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role = role.lower()
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assert role in ["trainer", "pserver", "all"]
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if role == "trainer":
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all_list = self._worker_endpoints
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rank = self._current_id
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elif role == "pserver":
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all_list = self._server_endpoints
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rank = self._current_id
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else:
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all_list = self._worker_endpoints + self._server_endpoints
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rank = all_list.index(self._cur_endpoint)
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gloo = fluid.core.Gloo()
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gloo.set_rank(rank)
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gloo.set_size(len(all_list))
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gloo.set_prefix(self._prefix)
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gloo.set_iface(self._iface)
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gloo.set_timeout_seconds(self._init_timeout_seconds,
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self._run_timeout_seconds)
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if len(self._http_ip_port) != 0:
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gloo.set_http_store(self._http_ip_port[0],
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int(self._http_ip_port[1]), role)
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else:
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gloo.set_hdfs_store(self._hdfs_path + "/" + role,
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self._hdfs_name, self._hdfs_ugi)
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gloo.init()
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return gloo
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# paddlecloud support gloo
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if self._role == Role.WORKER:
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if self._current_id == 0 and len(self._http_ip_port) != 0:
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size_d = {
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"trainer": len(self._worker_endpoints),
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"pserver": len(self._server_endpoints),
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"all":
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len(self._worker_endpoints) + len(self._server_endpoints)
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}
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# child process for http server
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self._http_server = Process(
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target=self.__start_kv_server,
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args=(self._http_server_d, size_d))
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self._http_server.daemon = True
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# set running status to True
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self._http_server_d["running"] = True
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# start child process
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self._http_server.start()
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self._node_type = 1
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gloo = init_gloo_instance("trainer")
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self._node_type_comm = gloo
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else:
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assert self._role == Role.SERVER
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self._node_type = 0
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gloo = init_gloo_instance("pserver")
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self._node_type_comm = gloo
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all_list = self._worker_endpoints + self._server_endpoints
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self._rank = all_list.index(self._cur_endpoint)
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self._size = len(all_list)
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gloo = init_gloo_instance("all")
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self._all_comm = gloo
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if self._http_server is not None:
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# set running status to False
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self._http_server_d["running"] = False
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# wait until child process exits
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self._http_server.join()
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def generate_role(self):
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"""
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generate role for role maker
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"""
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if not self._role_is_generated:
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if not self._is_collective:
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self._ps_env()
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if self._init_gloo:
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self._init_gloo_env()
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else:
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self._collective_env()
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self._role_is_generated = True
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def __get_default_iface(self):
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"""
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get default physical interface
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"""
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default1 = self.__get_default_iface_from_gateway()
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default2 = self.__get_default_iface_from_interfaces()
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return default2 if default1 == "lo" else default1
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def __get_default_iface_from_gateway(self):
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"""
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get default physical interface
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"""
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import netifaces
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gateways = netifaces.gateways()
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if gateways.get(netifaces.AF_INET) != None:
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gateway = gateways[netifaces.AF_INET]
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if len(gateway) > 0 and len(gateway[0]) > 1:
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return gateway[0][1]
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return "lo"
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def __get_default_iface_from_interfaces(self):
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"""
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get default physical interface
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"""
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import netifaces
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for intf_name in netifaces.interfaces():
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addresses = netifaces.ifaddresses(intf_name)
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if netifaces.AF_INET in addresses:
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ipv4_addresses = addresses[netifaces.AF_INET]
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for ipv4_address in ipv4_addresses:
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if 'broadcast' in ipv4_address:
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return intf_name
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return "lo"
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def __start_kv_server(self, http_server_d, size_d):
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from paddle.fleet.utils import KVServer
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http_server = KVServer(int(self._http_ip_port[1]), size_d)
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http_server.start()
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wait_seconds = 5
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while http_server_d.get("running",
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False) and not http_server.shoud_stop():
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time.sleep(wait_seconds)
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http_server.stop()
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class UserDefinedRoleMaker(PaddleCloudRoleMaker):
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def __init__(self, is_collective=False, init_gloo=False, **kwargs):
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super(UserDefinedRoleMaker, self).__init__(
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is_collective=is_collective, init_gloo=init_gloo, **kwargs)
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def _user_defined_ps_env(self):
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self._server_endpoints = self._kwargs.get("server_endpoints")
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self._worker_endpoints = self._kwargs.get("worker_endpoints", [])
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self._trainers_num = self._kwargs.get("worker_num", 0)
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if self._trainers_num == 0:
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assert (len(self._worker_endpoints) > 0)
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self._trainers_num = len(self._worker_endpoints)
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self._role = self._kwargs.get("role")
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self._current_id = self._kwargs.get("current_id")
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if self._role == Role.WORKER and len(
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self._worker_endpoints) > self._current_id:
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self._cur_endpoint = self._worker_endpoints[self._current_id]
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elif self._role == Role.SERVER:
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self._cur_endpoint = self._server_endpoints[self._current_id]
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def _user_defined_collective_env(self):
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self._worker_endpoints = self._kwargs.get("worker_endpoints")
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self._current_id = self._kwargs.get("current_id")
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self._trainers_num = len(self._worker_endpoints)
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self._training_role = Role.Worker
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def generate_role(self):
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"""
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generate role for role maker
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"""
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if not self._role_is_generated:
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if not self._is_collective:
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self._user_defined_ps_env()
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if self._init_gloo:
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self._init_gloo_env()
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else:
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self._user_defined_collective_env()
|
|
self._role_is_generated = True
|