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493 lines
14 KiB
493 lines
14 KiB
# Copyright (c) 2019 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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from __future__ import print_function
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__all__ = [
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'Role', 'RoleMakerBase', 'MPISymetricRoleMaker', 'UserDefinedRoleMaker',
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'UserDefinedCollectiveRoleMaker', 'PaddleCloudRoleMaker'
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]
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import os
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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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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 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 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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class MultiProcessRoleMaker(RoleMakerBase):
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"""
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MultiProcessRoleMaker is a default role maker for multi-process
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GPU training. It works with paddle.distributed.lanuch.py by-design
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"""
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def __init__(self):
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super(MultiProcessRoleMaker, self).__init__()
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self._role_is_generated = False
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def generate_role(self):
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import os
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if not self._role_is_generated:
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self._current_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
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self._num_trainers = 1
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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._current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
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if self._worker_endpoints:
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self._worker_endpoints = self._worker_endpoints.split(",")
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self._num_trainers = len(self._worker_endpoints)
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self._role_is_generated = True
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def is_worker(self):
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return True
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def is_server(self):
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return False
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def is_first_worker(self):
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return self._current_id == 0
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def worker_index(self):
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return self._current_id
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def worker_num(self):
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return self._worker_num
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class MPIRoleMaker(RoleMakerBase):
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"""
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MPIRoleMaker is a MPI-API based role maker which is a counter-part of K8SRoleMaker
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mpi4py will be used if a developer inherits MPIRoleMaker
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"""
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def __init__(self):
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super(MPIRoleMaker, self).__init__()
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from mpi4py import MPI
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self.MPI = MPI
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self._comm = MPI.COMM_WORLD
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self._node_type_comm = None
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self._ips = None
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self._ip = None
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def _get_rank(self):
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"""
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return rank
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"""
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self._rank = self._comm.Get_rank()
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return self._rank
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def _get_size(self):
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"""
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return size
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"""
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self._size = self._comm.Get_size()
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return self._size
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def _all_gather(self, obj):
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"""
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all_gather(obj) will call MPI's allgather function
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"""
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self._barrier_all()
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return self._comm.allgather(obj)
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def _worker_gather(self, obj):
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"""
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worker_gather(obj) will call MPI's allgather function
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"""
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if self.is_worker():
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self._node_type_comm.barrier()
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return self._node_type_comm.allgather(obj)
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return None
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def _barrier_all(self):
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"""
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barrier_all() will call MPI's barrier_all function
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"""
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self._comm.barrier()
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def _finalize(self):
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"""
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finalize the current MPI instance.
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"""
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self.MPI.Finalize()
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def _get_ips(self):
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"""
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collect current distributed job's ip list
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"""
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if not self._ips:
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self._ips = self._comm.allgather(self.get_local_ip())
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return self._ips
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def get_local_ip(self):
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"""
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return get local ip
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"""
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import socket
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self._ip = socket.gethostbyname(socket.gethostname())
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return self._ip
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def generate_role(self):
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"""
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generate_role() should be called to identify current process's role
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"""
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raise NotImplementedError("Please implement this method in child class")
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class MPISymetricRoleMaker(MPIRoleMaker):
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"""
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MPISymetricRoleMaker is designed for worker and server assignment
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under MPI. Typically, a worker and a server node will be appointed
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on each physical node. This role maker can be only used under MPI.
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"""
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def __init__(self):
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super(MPISymetricRoleMaker, self).__init__()
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self._node_type = None
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self._proc_per_node = 2
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def _check_role_generation(self):
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if not self._role_is_generated:
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raise NameError("generate_role() should be called first")
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return True
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def is_first_worker(self):
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"""
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return whether current process is the first worker assigned by role maker
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"""
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if self._check_role_generation():
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return self.is_worker() and 0 == self.worker_index()
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return False
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def worker_num(self):
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return self._worker_num()
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def is_worker(self):
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"""
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return whether current process is worker assigned by role maker
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"""
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if self._check_role_generation():
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return self._node_type == 1
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return False
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def is_server(self):
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"""
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return whether current process is server assigned by role maker
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"""
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if self._check_role_generation():
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return self._node_type == 0
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return False
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def _worker_num(self):
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"""
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return the current number of worker
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"""
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if self._check_role_generation():
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if self.is_worker():
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return self._get_size() / 2
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return 0
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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 self._check_role_generation():
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if self.is_server():
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return self._get_size() / 2
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return 0
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def worker_index(self):
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"""
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return the index of worker
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"""
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if self._check_role_generation():
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return self._rank / self._proc_per_node
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return 0
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def server_index(self):
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"""
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return the index of server
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"""
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if self._check_role_generation():
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return self._rank / self._proc_per_node
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return 0
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def _barrier_worker(self):
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"""
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barrier all workers in current distributed job
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"""
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if self._check_role_generation():
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if self.is_worker():
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self._node_type_comm.barrier()
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def _barrier_server(self):
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"""
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barrier all servers in current distributed job
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"""
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if self._check_role_generation():
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if self.is_server():
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self._node_type_comm.barrier()
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def generate_role(self):
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"""
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generate currently process's role
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"""
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if not self._role_is_generated:
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# TODO(guru4elephant): only allow to be called once
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self._worker_endpoints = self._get_ips()[1::2]
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self._server_endpoints = self._get_ips()[::2]
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if 0 == self._get_rank() % self._proc_per_node % 2:
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self._node_type = 0
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else:
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self._node_type = 1
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self._node_type_comm = self._comm.Split(self._node_type)
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self._role_is_generated = True
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class PaddleCloudRoleMaker(RoleMakerBase):
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def __init__(self):
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super(PaddleCloudRoleMaker, self).__init__()
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self._role_is_generated = False
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def generate_role(self):
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if not self._role_is_generated:
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self.port = os.getenv("PADDLE_PORT", "6174")
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self.pserver_ips = os.getenv("PADDLE_PSERVERS", "")
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eplist = []
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for ip in self.pserver_ips.split(","):
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eplist.append(':'.join([ip, self.port]))
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self.endpoints = ",".join(eplist)
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self._trainers = int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
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self.current_endpoint = os.getenv("POD_IP",
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"localhost") + ":" + self.port
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self.role = os.getenv("TRAINING_ROLE", "TRAINER")
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self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
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self.eplist = eplist
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print("PaddleCloudRoleMaker() endpoints: %s" % self.endpoints)
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self.endpoints = self.endpoints.split(",")
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self._server_endpoints = self.endpoints
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self._worker_endpoints = self.endpoints
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if self.role.upper() == "PSERVER":
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self._current_id = self.endpoints.index(self.current_endpoint)
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self._role = Role.SERVER
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else:
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self._current_id = self.trainer_id
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self._role = Role.WORKER
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self._role_is_generated = True
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def is_worker(self):
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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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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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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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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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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 worker_num(self):
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if not self._role_is_generated:
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self.generate_role()
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return self._trainers
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class UserDefinedRoleMaker(RoleMakerBase):
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def __init__(self,
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current_id=0,
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role=Role.WORKER,
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worker_num=0,
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server_endpoints=None):
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"""
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UserDefinedRoleMaker is designed for worker and server assignment
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under manual. Typically, a worker and a server node will be appointed
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on each physical node, It can be assign by user.
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"""
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super(UserDefinedRoleMaker, self).__init__()
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if not isinstance(current_id, int):
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raise TypeError("current_id must be as int")
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else:
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if current_id < 0:
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raise ValueError("current_id must be gather or equal 0")
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self._current_id = current_id
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if role != Role.WORKER and role != Role.SERVER:
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raise TypeError("role must be as Role")
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else:
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self._role = role
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if not isinstance(worker_num, int):
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raise TypeError("worker_num must be as int")
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else:
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if worker_num < 0:
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raise ValueError("worker_num must be gather or equal 0")
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self._worker_num = worker_num
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if not isinstance(server_endpoints, list):
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raise TypeError("server_endpoints must be as string list")
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else:
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self._server_endpoints = server_endpoints
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def generate_role(self):
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self._role_is_generated = True
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def is_worker(self):
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return self._role == Role.WORKER
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def is_server(self):
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return self._role == Role.SERVER
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def is_first_worker(self):
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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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return self._current_id
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def server_index(self):
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return self._current_id
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def worker_num(self):
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return self._worker_num
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class UserDefinedCollectiveRoleMaker(RoleMakerBase):
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def __init__(self, current_id=0, worker_endpoints=None):
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"""
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UserDefinedCollectiveRoleMaker is designed for worker assignment
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under manual for collective mode.
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"""
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super(UserDefinedCollectiveRoleMaker, self).__init__()
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if not isinstance(current_id, int):
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raise TypeError("current_id must be as int")
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else:
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if current_id < 0:
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raise ValueError("current_id must be greater or equal 0")
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self._current_id = current_id
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if not isinstance(worker_endpoints, list):
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raise TypeError("worker_endpoints must be as string list")
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else:
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self._worker_endpoints = worker_endpoints
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self._worker_num = len(self._worker_endpoints)
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def generate_role(self):
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self._role_is_generated = True
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def is_worker(self):
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return True
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def is_first_worker(self):
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return self._current_id == 0
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def worker_index(self):
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return self._current_id
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def worker_num(self):
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return self._worker_num
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