def init_env(self): from paddle_fl.paddle_fl.core.scheduler.agent_master import FLScheduler import paddle.fluid as fluid # import paddle_fl as fl from paddle_fl.paddle_fl.core.master.job_generator import JobGenerator from paddle_fl.paddle_fl.core.strategy.fl_distribute_transpiler import FLDistributeTranspiler from paddle_fl.paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory, FedAvgStrategy if self.config['parameter']['model'] == 'resnet': model = ResNet18() # inputs = np.array([np.zeros((3, 224, 224)).astype('float32')]).astype('float32') inputs = fluid.layers.data(name='x', shape=[1, 3, 224, 224], dtype='float32') labels = np.array([0]).astype('float32').reshape(-1, 1) labels = fluid.layers.data(name='label', shape=[1, 1], dtype='float32') model.resnet(inputs, labels) else: inputs = [fluid.layers.data( name=str(slot_id), shape=[5], dtype="float32") for slot_id in range(3)] label = fluid.layers.data( name="label", shape=[1], dtype='int64') model = Model() model.mlp(inputs, label) job_generator = JobGenerator() optimizer = fluid.optimizer.SGD(learning_rate=0.1) job_generator.set_optimizer(optimizer) job_generator.set_losses([model.loss]) job_generator.set_startup_program(model.startup_program) job_generator.set_infer_feed_and_target_names( [x.name for x in inputs], [model.predict.name]) build_strategy = FLStrategyFactory() build_strategy.fed_avg = True build_strategy.inner_step = 1 strategy = build_strategy.create_fl_strategy() endpoints = ['{}:{}'.format(self.config['server']['ip'], self.config['server']['port'])] output = self.config['path']['job_path'] job_generator.generate_fl_job( strategy, server_endpoints=endpoints, worker_num=int(self.config['parameter']['num_users']), output=output) QMessageBox.information(self, 'compile ok', 'compile env done', QMessageBox.Ok) print('finish!') self.worker_num = int(self.config['parameter']['num_users']) self.server_num = 1 # Define the number of worker/server and the port for scheduler self.scheduler = MFLScheduler(self.worker_num, self.server_num, port=int(self.config['scheduler']['port'])) self.scheduler.set_sample_worker_num(self.worker_num) # self.scheduler.set_sample_worker_num(max(1, int(float(self.config['parameter']['frac']) * self.worker_num))) import paddle_fl as fl import paddle.fluid as fluid from paddle_fl.paddle_fl.core.server.fl_server import FLServer from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob self.server = FLServer() server_id = self.id job_path = self.config['path']['job_path'] print(job_path) self.job = FLRunTimeJob() self.job.load_server_job(job_path, server_id) self.job._scheduler_ep = '{}:{}'.format(self.config['scheduler']['ip'], self.config['scheduler']['port']) print(self.job._scheduler_ep) self.server.set_server_job(self.job) self.server._current_ep = '{}:{}'.format(self.config['server']['ip'], self.config['server']['port']) print(self.server._current_ep) self.processLabel.setText('waiting for agents') # self.server.start() self.initThread = threading.Thread(target=self.scheduler.init_env) self.waitThread = threading.Thread(target=self.wait_agent) self.servThread = threading.Thread(target=self.server.start) self.initThread.start() self.servThread.start() self.waitThread.start() # self.servThread.join() # self.waitThread.join() print("init env done.")
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import paddle_fl.paddle_fl as fl import paddle.fluid as fluid from paddle_fl.paddle_fl.core.server.fl_server import FLServer from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob server = FLServer() server_id = 0 job_path = "fl_job_config" job = FLRunTimeJob() job.load_server_job(job_path, server_id) job._scheduler_ep = "127.0.0.1:9091" # IP address for scheduler server.set_server_job(job) server._current_ep = "127.0.0.1:8181" # IP address for server server.start()
class ServerControlFrame(QWidget): def __init__(self, config, id): super(ServerControlFrame, self).__init__() self.config = config self.id = id self.connected_agent = 0 self.initGUI() def initGUI(self): self.resize(800, 500) self.clientGroup = QGroupBox('Client ProgressBar', self) self.clientGroup.resize(350, 350) self.clientGroup.move(25, 25) self.clientGroupList = [] self.cgvBox = QVBoxLayout() # print(self.config['parameter']['num_users']) for i in range(int(self.config['parameter']['num_users'])): cliLabel = QLabel('client {}'.format(i)) cliProBar = QProgressBar() cliProLabel = QLabel('{} %'.format(0)) cliStateLabel = QLabel('disconnected') self.cgvBox.addWidget(cliLabel) self.cgvBox.addWidget(cliProBar) self.cgvBox.addWidget(cliProLabel) self.cgvBox.addWidget(cliStateLabel) self.clientGroupList.append([cliLabel, cliProBar, cliProLabel, cliStateLabel]) # self.serGroupList = [] # serLabel = QLabel('server') # serProBar = QProgressBar() # serProLabel = QLabel('{} %'.format(0)) # self.cgvBox.addWidget(serLabel) # self.cgvBox.addWidget(serProBar) # self.cgvBox.addWidget(serProLabel) # self.clientGroupList.append() self.clientGroup.setLayout(self.cgvBox) self.processLabel = QLabel('noconnect', self) self.processLabel.resize(175, 20) self.processLabel.move(600, 470) self.initBtn = QPushButton('init env', self) self.initBtn.resize(100, 25) self.initBtn.move(25, 400) self.initBtn.clicked.connect(self.init_env) self.startBtn = QPushButton('start', self) self.startBtn.resize(100, 25) self.startBtn.move(150, 400) self.startBtn.setDisabled(True) # train thread self.trainThread = threading.Thread(target=self.start_train) self.startBtn.clicked.connect(self.trainThread.start) self.stopBtn = QPushButton('stop', self) self.stopBtn.resize(100, 25) self.stopBtn.move(275, 400) self.stopBtn.setDisabled(True) self.stopBtn.clicked.connect(self.stop_train) self.testBtn = QPushButton('test', self) self.testBtn.resize(100, 25) self.testBtn.move(550, 450) self.testBtn.clicked.connect(self.open_test_frame) self.lossLabel = QLabel(self) self.lossLabel.resize(300, 300) self.lossLabel.move(425, 25) self.contrLabel = QLabel(self) self.contrLabel.resize(300, 80) self.contrLabel.move(425, 345) self.setWindowTitle('ServerControlFrame {}'.format(self.id)) self.show() def open_test_frame(self): self.testframe = TestFrame(self.config) self.testframe.show() def init_env(self): from paddle_fl.paddle_fl.core.scheduler.agent_master import FLScheduler import paddle.fluid as fluid # import paddle_fl as fl from paddle_fl.paddle_fl.core.master.job_generator import JobGenerator from paddle_fl.paddle_fl.core.strategy.fl_distribute_transpiler import FLDistributeTranspiler from paddle_fl.paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory, FedAvgStrategy if self.config['parameter']['model'] == 'resnet': model = ResNet18() # inputs = np.array([np.zeros((3, 224, 224)).astype('float32')]).astype('float32') inputs = fluid.layers.data(name='x', shape=[1, 3, 224, 224], dtype='float32') labels = np.array([0]).astype('float32').reshape(-1, 1) labels = fluid.layers.data(name='label', shape=[1, 1], dtype='float32') model.resnet(inputs, labels) else: inputs = [fluid.layers.data( name=str(slot_id), shape=[5], dtype="float32") for slot_id in range(3)] label = fluid.layers.data( name="label", shape=[1], dtype='int64') model = Model() model.mlp(inputs, label) job_generator = JobGenerator() optimizer = fluid.optimizer.SGD(learning_rate=0.1) job_generator.set_optimizer(optimizer) job_generator.set_losses([model.loss]) job_generator.set_startup_program(model.startup_program) job_generator.set_infer_feed_and_target_names( [x.name for x in inputs], [model.predict.name]) build_strategy = FLStrategyFactory() build_strategy.fed_avg = True build_strategy.inner_step = 1 strategy = build_strategy.create_fl_strategy() endpoints = ['{}:{}'.format(self.config['server']['ip'], self.config['server']['port'])] output = self.config['path']['job_path'] job_generator.generate_fl_job( strategy, server_endpoints=endpoints, worker_num=int(self.config['parameter']['num_users']), output=output) QMessageBox.information(self, 'compile ok', 'compile env done', QMessageBox.Ok) print('finish!') self.worker_num = int(self.config['parameter']['num_users']) self.server_num = 1 # Define the number of worker/server and the port for scheduler self.scheduler = MFLScheduler(self.worker_num, self.server_num, port=int(self.config['scheduler']['port'])) self.scheduler.set_sample_worker_num(self.worker_num) # self.scheduler.set_sample_worker_num(max(1, int(float(self.config['parameter']['frac']) * self.worker_num))) import paddle_fl as fl import paddle.fluid as fluid from paddle_fl.paddle_fl.core.server.fl_server import FLServer from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob self.server = FLServer() server_id = self.id job_path = self.config['path']['job_path'] print(job_path) self.job = FLRunTimeJob() self.job.load_server_job(job_path, server_id) self.job._scheduler_ep = '{}:{}'.format(self.config['scheduler']['ip'], self.config['scheduler']['port']) print(self.job._scheduler_ep) self.server.set_server_job(self.job) self.server._current_ep = '{}:{}'.format(self.config['server']['ip'], self.config['server']['port']) print(self.server._current_ep) self.processLabel.setText('waiting for agents') # self.server.start() self.initThread = threading.Thread(target=self.scheduler.init_env) self.waitThread = threading.Thread(target=self.wait_agent) self.servThread = threading.Thread(target=self.server.start) self.initThread.start() self.servThread.start() self.waitThread.start() # self.servThread.join() # self.waitThread.join() print("init env done.") # scheduler.start_fl_training() def wait_agent(self): # print('?????????') cli_set = set([]) # print(threading.activeCount()) while self.connected_agent < self.worker_num: # print('{} ? {}'.format(self.connected_agent, self.worker_num)) # print(self.scheduler.fl_workers) # print(self.scheduler.fl_servers) if self.connected_agent != len(self.scheduler.fl_workers): # print(self.scheduler.fl_workers, cli_set) new_cli = set(self.scheduler.fl_workers) - cli_set self.clientGroupList[self.connected_agent][3].setText('connected') # print(new_cli) new_cli = list(new_cli) print('get client {} connection'.format(new_cli)) cli_set = set(self.scheduler.fl_workers) self.connected_agent = len(self.scheduler.fl_workers) QMessageBox.information(self, 'init env', 'init env done', QMessageBox.Ok) self.startBtn.setDisabled(False) def start_train(self): self.stopBtn.setDisabled(False) self.startBtn.setDisabled(True) self.processLabel.setText('training') self.scheduler.start_fl_training_with_round(int(self.config['parameter']['round']), label=self.clientGroupList) print('train ok!') self.stopBtn.setDisabled(True) self.startBtn.setDisabled(False) self.processLabel.setText('finished') QMessageBox.information(self, 'training progress', 'The model has been trained!!', QMessageBox.Ok) def stop_train(self): print(self.trainThread.is_alive())
else: os.system("wget {}/job_config/{}.tar.gz".format(download_url, message)) print(message) break os.system("ls") os.system("gzip -d {}.tar.gz".format(message)) print("gzip finish") os.system("tar -xf {}.tar".format(message)) os.system("ls") zmq_socket.close() print("close socket") #program start if 'server' in message: server = FLServer() server_id = 0 job_path = "job_config" job = FLRunTimeJob() job.load_server_job(job_path, server_id) job._scheduler_ep = scheduler_conf["ENDPOINT"] server.set_server_job(job) server._current_ep = endpoint server.start() else: def reader(): for i in range(1000): data_dict = {} for i in range(3): data_dict[str(i)] = np.random.rand(1, 5).astype('float32')
# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import paddle_fl.paddle_fl as fl import os import paddle.fluid as fluid from paddle_fl.paddle_fl.core.server.fl_server import FLServer from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob import time server = FLServer() server_id = 0 job_path = "fl_job_config" job = FLRunTimeJob() job.load_server_job(job_path, server_id) job._scheduler_ep = os.environ['FL_SCHEDULER_SERVICE_HOST'] + ":" + os.environ[ 'FL_SCHEDULER_SERVICE_PORT_FL_SCHEDULER'] # IP address for scheduler #job._endpoints = os.environ['POD_IP'] + ":" + os.environ['FL_SERVER_SERVICE_PORT_FL_SERVER'] # IP address for server server.set_server_job(job) server._current_ep = os.environ['FL_SERVER_SERVICE_HOST'] + ":" + os.environ[ 'FL_SERVER_SERVICE_PORT_FL_SERVER'] # IP address for server print(job._scheduler_ep, server._current_ep) server.start() print("connect")
from paddle_fl.paddle_fl.core.server.fl_server import FLServer from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob import json import argparse parser = argparse.ArgumentParser() parser.add_argument('--config_path', help="path to the config file") args = parser.parse_args() with open(args.config_path, 'r') as fp: params = json.load(fp) server = FLServer() server_id = 0 job_path = params["federated"]["job_path"] print("job_path: ", job_path) job = FLRunTimeJob() job.load_server_job(job_path, server_id) job._scheduler_ep = "127.0.0.1:" + str(params["federated"]["scheduler_port"]) print("IP address for scheduler: ", job._scheduler_ep) server.set_server_job(job) server._current_ep = "127.0.0.1:" + str(params["federated"]["server_port"]) print("IP address for server: ", server._current_ep) server.start()
class ServerControlFrame(QWidget): def __init__(self, config, id, lang='cn'): super(ServerControlFrame, self).__init__() self.lang = lang self.config = config self.id = id self.connected_agent = 0 self.loadQSS() self.initGUI() self.translateAll() def clickedChinese(self): self.lang = "cn" self.translateAll() def clickedEnglish(self): self.lang = "en" self.translateAll() def translateAll(self): self.clientGroup.setTitle(language[self.lang]['Client ProgressBar']) if self.processLabel.text() == language['cn']['noconnect']: self.processLabel.setText(language[self.lang]['noconnect']) if self.processLabel.text() == language['en']['noconnect']: self.processLabel.setText(language[self.lang]['noconnect']) if self.processLabel.text() == language['cn']['training']: self.processLabel.setText(language[self.lang]['training']) if self.processLabel.text() == language['en']['training']: self.processLabel.setText(language[self.lang]['training']) if self.processLabel.text() == language['cn']['finished']: self.processLabel.setText(language[self.lang]['finished']) if self.processLabel.text() == language['en']['finished']: self.processLabel.setText(language[self.lang]['finished']) self.initBtn.setToolTip(language[self.lang]['init env']) self.startBtn.setToolTip(language[self.lang]['start']) self.stopBtn.setToolTip(language[self.lang]['stop']) self.testBtn.setToolTip(language[self.lang]['test']) for grouplist in self.clientGroupList: if grouplist[3].text() == language['cn']['disconnected']: grouplist[3].setText(language[self.lang]['disconnected']) if grouplist[3].text() == language['en']['disconnected']: grouplist[3].setText(language[self.lang]['disconnected']) if grouplist[3].text() == language['cn']['connected']: grouplist[3].setText(language[self.lang]['connected']) if grouplist[3].text() == language['en']['connected']: grouplist[3].setText(language[self.lang]['connected']) def loadQSS(self): """ 加载QSS """ file = 'qss/style/main.qss' with open(file, 'rt', encoding='utf8') as f: styleSheet = f.read() self.setStyleSheet(styleSheet) f.close() def initGUI(self): self.resize(800, 500) self.clientGroup = QGroupBox('Client ProgressBar', self) self.clientGroup.resize(350, 400) self.clientGroup.move(25, 25) self.clientGroupList = [] self.cgvBox = QVBoxLayout() self.cgvBox.setSpacing(20) # print(self.config['parameter']['num_users']) for i in range(int(self.config['parameter']['num_users'])): cliSubgroup = QWidget(objectName='subgroup') cliLabel = QLabel('Client {}'.format(i)) clivBox = QVBoxLayout() clihBox = QHBoxLayout() """ layout.addWidget(CircleProgressBar(self)) layout.addWidget(CircleProgressBar( self, color=QColor(255, 0, 0), clockwise=False)) layout.addWidget(CircleProgressBar(self, styleSheet="" " qproperty-color: rgb(0, 255, 0); "" ")) """ cliProBar = QProgressBar() cliProBar.setTextVisible(False) cliProBar.setMaximum(100) cliProBar.setValue(40) cliProLabel = QLabel('{} %'.format(0)) cliStateLabel = QLabel('Disconnected') clihBox.addWidget(cliLabel) clihBox.addWidget(cliStateLabel) clivBox.addLayout(clihBox) clivBox.addWidget(cliProBar) clivBox.addWidget(cliProLabel) cliSubgroup.setLayout(clivBox) effect_shadow = QGraphicsDropShadowEffect(self) effect_shadow.setOffset(3,3) # 偏移 effect_shadow.setBlurRadius(10) # 阴影半径 effect_shadow.setColor(QColor(38, 78, 200, 127)) # 阴影颜色 cliSubgroup.setGraphicsEffect(effect_shadow) self.cgvBox.addWidget(cliSubgroup) self.clientGroupList.append([cliLabel, cliProBar, cliProLabel, cliStateLabel]) # self.serGroupList = [] # serLabel = QLabel('server') # serProBar = QProgressBar() # serProLabel = QLabel('{} %'.format(0)) # self.cgvBox.addWidget(serLabel) # self.cgvBox.addWidget(serProBar) # self.cgvBox.addWidget(serProLabel) # self.clientGroupList.append() self.clientGroup.setLayout(self.cgvBox) self.processLabel0 = QLabel(chr(0xf00d), self) self.processLabel0.resize(20, 20) self.processLabel0.move(590, 480) self.processLabel0.setStyleSheet("color:red") self.processLabel0.setFont(qta.font('fa', 20)) self.processLabel = QLabel(language[self.lang]['noconnect'], self) self.processLabel.resize(175, 20) self.processLabel.move(610, 480) self.initBtn = QPushButton(chr(0xf112), self, objectName='btnSuccess') effect_shadow = QGraphicsDropShadowEffect(self) effect_shadow.setOffset(3,3) # 偏移 effect_shadow.setBlurRadius(10) # 阴影半径 effect_shadow.setColor(QColor(38, 200, 78, 127)) # 阴影颜色 self.initBtn.setGraphicsEffect(effect_shadow) self.initBtn.setToolTip(language[self.lang]['init env']) self.initBtn.setFont(qta.font('fa', 30)) #self.initBtn = QPushButton('init env', self, objectName='btnSuccess') self.initBtn.resize(70, 70) self.initBtn.move(35, 450) self.initBtn.clicked.connect(self.init_env) self.startBtn = QPushButton(chr(0xf04b), self, objectName='btnSuccess') effect_shadow = QGraphicsDropShadowEffect(self) effect_shadow.setOffset(3,3) # 偏移 effect_shadow.setBlurRadius(10) # 阴影半径 effect_shadow.setColor(QColor(38, 200, 78, 127)) # 阴影颜色 self.startBtn.setGraphicsEffect(effect_shadow) self.startBtn.setFont(qta.font('fa', 30)) self.startBtn.setToolTip(language[self.lang]['start']) #self.startBtn = QPushButton('start', self, objectName='btnSuccess') self.startBtn.resize(70, 70) self.startBtn.move(160, 450) self.startBtn.setDisabled(True) # train thread self.trainThread = threading.Thread(target=self.start_train) self.startBtn.clicked.connect(self.trainThread.start) self.stopBtn = QPushButton(chr(0xf04d), self, objectName='btnSuccess') effect_shadow = QGraphicsDropShadowEffect(self) effect_shadow.setOffset(3,3) # 偏移 effect_shadow.setBlurRadius(10) # 阴影半径 effect_shadow.setColor(QColor(38, 200, 78, 127)) # 阴影颜色 self.stopBtn.setGraphicsEffect(effect_shadow) self.stopBtn.setFont(qta.font('fa', 30)) self.stopBtn.setToolTip(language[self.lang]['stop']) #self.stopBtn = QPushButton('stop', self, objectName='btnSuccess') self.stopBtn.resize(70, 70) self.stopBtn.move(285, 450) self.stopBtn.setDisabled(True) self.stopBtn.clicked.connect(self.stop_train) self.testBtn = QPushButton(chr(0xf040), self, objectName='btnPrimary') effect_shadow = QGraphicsDropShadowEffect(self) effect_shadow.setOffset(3,3) # 偏移 effect_shadow.setBlurRadius(10) # 阴影半径 effect_shadow.setColor(QColor(38, 78, 200, 127)) # 阴影颜色 self.testBtn.setGraphicsEffect(effect_shadow) self.testBtn.setToolTip(language[self.lang]['test']) self.testBtn.setFont(qta.font('fa', 30)) #self.testBtn = QPushButton('test', self, objectName='btnInfo') self.testBtn.resize(70, 70) self.testBtn.move(600, 400) self.testBtn.clicked.connect(self.open_test_frame) self.lossLabel = QLabel(self) self.lossLabel.resize(300, 300) self.lossLabel.move(425, 25) self.contrLabel = QLabel(self) self.contrLabel.resize(300, 80) self.contrLabel.move(425, 345) self.setWindowTitle('ServerControlFrame {}'.format(self.id)) #self.show() def open_test_frame(self): self.testframe = FramelessWindow() frame = TestFrame(self.config, lang=self.lang) self.testframe.setWindowTitle('TestFrame') self.testframe.setWindowIcon(QIcon('icon.png')) self.testframe.setFixedSize(QSize(600,480)) #因为这里固定了大小,所以窗口的大小没有办法任意调整,想要使resizeWidget函数生效的话要把这里去掉,自己调节布局和窗口大小 self.testframe.setWidget(frame) # 把自己的窗口添加进来 self.testframe.titleBar.clickedChinese.connect(frame.clickedChinese) self.testframe.titleBar.clickedEnglish.connect(frame.clickedEnglish) self.testframe.show() def init_env(self): from paddle_fl.paddle_fl.core.scheduler.agent_master import FLScheduler import paddle.fluid as fluid # import paddle_fl as fl from paddle_fl.paddle_fl.core.master.job_generator import JobGenerator from paddle_fl.paddle_fl.core.strategy.fl_distribute_transpiler import FLDistributeTranspiler from paddle_fl.paddle_fl.core.strategy.fl_strategy_base import FLStrategyFactory, FedAvgStrategy if self.config['parameter']['model'] == 'resnet': model = ResNet18() # inputs = np.array([np.zeros((3, 224, 224)).astype('float32')]).astype('float32') inputs = fluid.layers.data(name='x', shape=[1, 3, 224, 224], dtype='float32') labels = np.array([0]).astype('float32').reshape(-1, 1) labels = fluid.layers.data(name='label', shape=[1, 1], dtype='float32') model.resnet(inputs, labels) else: inputs = [fluid.layers.data( name=str(slot_id), shape=[5], dtype="float32") for slot_id in range(3)] label = fluid.layers.data( name="label", shape=[1], dtype='int64') model = Model() model.mlp(inputs, label) job_generator = JobGenerator() optimizer = fluid.optimizer.SGD(learning_rate=0.1) job_generator.set_optimizer(optimizer) job_generator.set_losses([model.loss]) job_generator.set_startup_program(model.startup_program) job_generator.set_infer_feed_and_target_names( [x.name for x in inputs], [model.predict.name]) build_strategy = FLStrategyFactory() build_strategy.fed_avg = True build_strategy.inner_step = 1 strategy = build_strategy.create_fl_strategy() endpoints = ['{}:{}'.format(self.config['server']['ip'], self.config['server']['port'])] output = self.config['path']['job_path'] job_generator.generate_fl_job( strategy, server_endpoints=endpoints, worker_num=int(self.config['parameter']['num_users']), output=output) QMessageBox.information(self, self, language[self.lang]['compile ok'], self, language[self.lang]['compile env done'], QMessageBox.Ok) print('finish!') self.worker_num = int(self.config['parameter']['num_users']) self.server_num = 1 # Define the number of worker/server and the port for scheduler self.scheduler = MFLScheduler(self.worker_num, self.server_num, port=int(self.config['scheduler']['port'])) self.scheduler.set_sample_worker_num(self.worker_num) # self.scheduler.set_sample_worker_num(max(1, int(float(self.config['parameter']['frac']) * self.worker_num))) import paddle_fl as fl import paddle.fluid as fluid from paddle_fl.paddle_fl.core.server.fl_server import FLServer from paddle_fl.paddle_fl.core.master.fl_job import FLRunTimeJob self.server = FLServer() server_id = self.id job_path = self.config['path']['job_path'] print(job_path) self.job = FLRunTimeJob() self.job.load_server_job(job_path, server_id) self.job._scheduler_ep = '{}:{}'.format(self.config['scheduler']['ip'], self.config['scheduler']['port']) print(self.job._scheduler_ep) self.server.set_server_job(self.job) self.server._current_ep = '{}:{}'.format(self.config['server']['ip'], self.config['server']['port']) print(self.server._current_ep) self.processLabel.setText(language[self.lang]['waiting for agents']) # self.server.start() self.initThread = threading.Thread(target=self.scheduler.init_env) self.waitThread = threading.Thread(target=self.wait_agent) self.servThread = threading.Thread(target=self.server.start) self.initThread.start() self.servThread.start() self.waitThread.start() # self.servThread.join() # self.waitThread.join() print("init env done.") # scheduler.start_fl_training() def wait_agent(self): # print('?????????') cli_set = set([]) # print(threading.activeCount()) while self.connected_agent < self.worker_num: # print('{} ? {}'.format(self.connected_agent, self.worker_num)) # print(self.scheduler.fl_workers) # print(self.scheduler.fl_servers) if self.connected_agent != len(self.scheduler.fl_workers): # print(self.scheduler.fl_workers, cli_set) new_cli = set(self.scheduler.fl_workers) - cli_set self.clientGroupList[self.connected_agent][3].setText(language[self.lang]['connected']) # print(new_cli) new_cli = list(new_cli) print('get client {} connection'.format(new_cli)) cli_set = set(self.scheduler.fl_workers) self.connected_agent = len(self.scheduler.fl_workers) QMessageBox.information(self, language[self.lang]['init env'], language[self.lang]['init env done'], QMessageBox.Ok) self.startBtn.setDisabled(False) def start_train(self): self.stopBtn.setDisabled(False) self.startBtn.setDisabled(True) self.processLabel.setText(language[self.lang]['training']) self.scheduler.start_fl_training_with_round(int(self.config['parameter']['round']), label=self.clientGroupList) print('train ok!') self.stopBtn.setDisabled(True) self.startBtn.setDisabled(False) self.processLabel.setText(language[self.lang]['finished']) QMessageBox.information(self, language[self.lang]['training progress'], language[self.lang]['The model has been trained!!'], QMessageBox.Ok) def stop_train(self): print(self.trainThread.is_alive())