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main_fed_streams.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6
from asyncore import read
import copy
from fileinput import filename
import sys
import threading
from collections import OrderedDict
import grpc
import numpy as np
import time, math
import torch
from utils.data_utils import data_setup, DatasetSplit
from utils.model_utils import *
from utils.aggregation import *
from options import call_parser
from models.Update import LocalUpdate
from models.test import test_img
from torch.utils.data import DataLoader
from concurrent import futures
# from utils.rdp_accountant import compute_rdp, get_privacy_spent
import warnings
import glob
import statistics
warnings.filterwarnings("ignore")
torch.cuda.is_available()
from kafka import KafkaProducer, KafkaConsumer
from multiprocessing import Pool, Process, ProcessError, Queue
import pika
from celery import Celery
import pickle,json
from queues_func_list import Node0RabbitQueues as rq0
from queues_func_list import Node1RabbitQueues as rq1
def arrange_round_train(args):
pass
def global_model_aggregate():
pass
def ack_agent():
pass
def something_something():
pass
nodes = 2
# global local_updates
# global loss_locals
local_updates = []
loss_locals = []
def callback(ch, method, properties, body):
print(" [x] Received %r" % body)
time.sleep(body.count(b'.'))
print(" [x] Done")
ch.basic_ack(delivery_tag=method.delivery_tag)
def serve(args):
torch.manual_seed(args.seed+args.repeat)
torch.cuda.manual_seed(args.seed+args.repeat)
np.random.seed(args.seed+args.repeat)
args, dataset_train, dataset_test, dict_users = data_setup(args)
print("{:<50}".format("=" * 15 + " data setup " + "=" * 50)[0:60])
print('length of dataset:{}'.format(len(dataset_train) + len(dataset_test)))
print('num. of training data:{}'.format(len(dataset_train)))
print('num. of testing data:{}'.format(len(dataset_test)))
print('num. of classes:{}'.format(args.num_classes))
print('num. of users:{}'.format(len(dict_users)))
sample_per_users = int(sum([ len(dict_users[i]) for i in range(len(dict_users))])/len(dict_users))
print('num. of samples per user:{}'.format(sample_per_users))
# credentials = pika.PlainCredentials('jahanxb', 'phdunr')
# parameters = pika.ConnectionParameters('130.127.134.6',
# 5672,
# '/',
# credentials)
# connection = pika.BlockingConnection(parameters)
# #connection = pika.BlockingConnection(pika.ConnectionParameters(host='amqp://jahanxb:[email protected]:15672'))
# channel = connection.channel()
# channel.queue_declare(queue='task_queue', durable=True)
# message = "Hello World! This might help me"
# channel.basic_publish(
# exchange='',
# routing_key='task_queue',
# body=message,
# properties=pika.BasicProperties(delivery_mode=2)
# )
# print(" [x] Sent %r" % message)
# connection.close()
'''
yaha se connection pakrna ha
'''
# credentials = pika.PlainCredentials('jahanxb', 'phdunr')
# parameters = pika.ConnectionParameters('130.127.134.6',
# 5672,
# '/',
# credentials)
# # ye wala code waise master node p hone chaiyee ab , q k ye ab receive kare ga round
# connection = pika.BlockingConnection(parameters)
# channel = connection.channel()
# channel.queue_declare(queue='task_queue',durable=True)
# print(' [*] Waiting for messages. To exit press')
# channel.basic_qos(prefetch_count=1)
# channel.basic_consume(queue='task_queue', on_message_callback=callback)
# channel.start_consuming()
'''
chalo idr end kar do icay
'''
if args.dataset == 'fmnist' or args.dataset == 'cifar':
dataset_test, val_set = torch.utils.data.random_split(
dataset_test, [9000, 1000])
print(len(dataset_test), len(val_set))
elif args.dataset == 'svhn':
dataset_test, val_set = torch.utils.data.random_split(
dataset_test, [len(dataset_test)-2000, 2000])
print(len(dataset_test), len(val_set))
print("{:<50}".format("=" * 15 + " log path " + "=" * 50)[0:60])
log_path = set_log_path(args)
print(log_path)
args, net_glob = model_setup(args)
print("{:<50}".format("=" * 15 + " model setup " + "=" * 50)[0:60])
# ###################################### model initialization ###########################
print("{:<50}".format("=" * 15 + " training... " + "=" * 50)[0:60])
t1 = time.time()
net_glob.train()
# copy weights
global_model = copy.deepcopy(net_glob.state_dict())
local_m = []
train_local_loss = []
test_acc = []
norm_med = []
credentials = pika.PlainCredentials('jahanxb', 'phdunr')
parameters = pika.ConnectionParameters('130.127.134.6',
5672,
'/',
credentials)
connection = pika.BlockingConnection(parameters)
#connection = pika.BlockingConnection(pika.ConnectionParameters(host='amqp://jahanxb:[email protected]:15672'))
channel = connection.channel()
nodes = 2
num_selected_users = 2
for t in range(args.round):
if t==0:
print('Initial Global Model...')
print('Queue Preparation for Global Model')
master_global_for_node0_round = f'master_global_for_node[{0}]_round[{t}]'
task_queue = master_global_for_node0_round
channel.queue_declare(queue=task_queue, durable=True)
msg = pickle.dumps(global_model)
channel.basic_publish(
exchange='',
routing_key=task_queue,
body=msg,
properties=pika.BasicProperties(delivery_mode=2)
)
master_global_for_node1_round = f'master_global_for_node[{1}]_round[{t}]'
task_queue = master_global_for_node1_round
channel.queue_declare(queue=task_queue, durable=True)
msg = pickle.dumps(global_model)
channel.basic_publish(
exchange='',
routing_key=task_queue,
body=msg,
properties=pika.BasicProperties(delivery_mode=2)
)
print(" [x] Sent Round=",t)
print(f'Round Process Started... Current Round on Master t={t}')
input('Insert Any Key.. If global Model is delivered...: ')
print('################## TrainingTest onum_selected_usersn aggregated Model ######################')
##################### testing on global model #######################
# Access the CLODUAMQP_URL environment variable and parse it (fallback to localhost)
url = 'amqp://jahanxb:[email protected]:5672/'
params = pika.URLParameters(url)
connection = pika.BlockingConnection(params)
channel = connection.channel() # start a channel
# ################### callback global #################
# channel.basic_consume(f'node_global_round_queue_[{glc}][{0}]',
# rq0.callback_global_1,
# auto_ack=True)
# ##################### callback local #################
# channel.basic_consume(f'node_local_round_queue_[{glc}][{0}]',
# rq0.callback_local_1,
# auto_ack=True)
# ################### callback Local Loss #################
# channel.basic_consume(f'node_local_loss_queue_[{glc}][{0}]',
# rq0.callback_local_loss_1,
# auto_ack=True)
# #######################################################
# channel.basic_consume(f'node_global_round_queue_[{glc}][{0}]',
# rq0.callback_local_loss,
# auto_ack=True)
################### callback global #################
channel.basic_consume(f'node[{0}]_global_round[{t}]',
rq0.callback_global,
auto_ack=True)
channel.basic_consume(f'node[{1}]_global_round[{t}]',
rq1.callback_global,
auto_ack=True)
##################### callback local #################
channel.basic_consume(f'node[{0}]_local_round[{t}]',
rq0.callback_local,
auto_ack=True)
channel.basic_consume(f'node[{1}]_local_round[{t}]',
rq1.callback_local,
auto_ack=True)
################### callback Local Loss #################
channel.basic_consume(f'node[{0}]_local_loss_round[{t}]',
rq0.callback_local_loss,
auto_ack=True)
channel.basic_consume(f'node[{1}]_local_loss_round[{t}]',
rq1.callback_local_loss,
auto_ack=True)
#######################################################
#print("t=",t ," | global_model: ",global_model.get('fc3.bias'))
try:
channel.start_consuming()
except KeyboardInterrupt:
channel.stop_consuming()
connection.close()
lp0 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[0]_local_round[{t}].pkl')
lp1 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[1]_local_round[{t}].pkl')
local_updates.append(lp0)
local_updates.append(lp1)
lp0_loss = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[0]_local_loss_round[{t}].pkl')
lp1_loss = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[1]_local_loss_round[{t}].pkl')
gm = []
gm0 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[0]_global_round[{t}].pkl')
gm1 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[1]_global_round[{t}].pkl')
gm.append(gm0)
gm.append(gm1)
print("gm: ",gm[0].get('fc3.bias'))
print("gm: ",gm[1].get('fc3.bias'))
for i in range(num_selected_users):
global_model = {
k: global_model[k] + gm[i][k]
for k in global_model.keys()
}
loss_locals.append(lp0_loss[0])
loss_locals.append(lp1_loss[0])
print("global_model: ",global_model.get('fc3.bias'))
for i in range(num_selected_users):
global_model = {
k: global_model[k] + local_updates[i][0][k] / num_selected_users
for k in global_model.keys()
}
global_counter = 1
net_glob.load_state_dict(global_model)
net_glob.eval()
test_acc_, _ = test_img(net_glob, dataset_test, args)
test_acc.append(test_acc_)
train_local_loss.append(sum(loss_locals) / len(loss_locals))
print('t {:3d}: '.format(t, ))
print('t {:3d}: train_loss = {:.3f}, norm = Not Recording, test_acc = {:.3f}'.
format(t, train_local_loss[0], test_acc[0]))
print('Submitting new global model: .....')
credentials = pika.PlainCredentials('jahanxb', 'phdunr')
parameters = pika.ConnectionParameters('130.127.134.6',
5672,
'/',
credentials)
connection = pika.BlockingConnection(parameters)
#connection = pika.BlockingConnection(pika.ConnectionParameters(host='amqp://jahanxb:[email protected]:15672'))
channel = connection.channel()
nodes = [0,1]
for n in nodes:
print('Initial Global Model...')
print('Queue Preparation for Global Model')
task_queue = f'master_global_for_node[{n}]_round[{t+1}]'
channel.queue_declare(queue=task_queue, durable=True)
msg = pickle.dumps(global_model)
channel.basic_publish(
exchange='',
routing_key=task_queue,
body=msg,
properties=pika.BasicProperties(delivery_mode=2)
)
print(" [x] Node=", n," Sent Round=",t+1)
connection.close()
elif t==1:
print(f'Next Iteration round: {t}')
print('Waiting for the Client/Slave Node to complete the Process...')
input('enter something to exit:')
print('Initial Global Model...')
print('Queue Preparation for Global Model')
credentials = pika.PlainCredentials('jahanxb', 'phdunr')
parameters = pika.ConnectionParameters('130.127.134.6',
5672,
'/',
credentials)
connection = pika.BlockingConnection(parameters)
#connection = pika.BlockingConnection(pika.ConnectionParameters(host='amqp://jahanxb:[email protected]:15672'))
channel = connection.channel()
master_global_for_node0_round = f'master_global_for_node[{0}]_round[{t}]'
task_queue = master_global_for_node0_round
channel.queue_declare(queue=task_queue, durable=True)
msg = pickle.dumps(global_model)
channel.basic_publish(
exchange='',
routing_key=task_queue,
body=msg,
properties=pika.BasicProperties(delivery_mode=2)
)
master_global_for_node1_round = f'master_global_for_node[{1}]_round[{t}]'
task_queue = master_global_for_node1_round
channel.queue_declare(queue=task_queue, durable=True)
msg = pickle.dumps(global_model)
channel.basic_publish(
exchange='',
routing_key=task_queue,
body=msg,
properties=pika.BasicProperties(delivery_mode=2)
)
print(" [x] Sent Round=",t)
print(f'Round Process Started... Current Round on Master t={t}')
input('Insert Any Key.. If global Model is delivered...: ')
print('################## TrainingTest onum_selected_usersn aggregated Model ######################')
##################### testing on global model #######################
# Access the CLODUAMQP_URL environment variable and parse it (fallback to localhost)
url = 'amqp://jahanxb:[email protected]:5672/'
params = pika.URLParameters(url)
connection = pika.BlockingConnection(params)
channel = connection.channel() # start a channel
# ################### callback global #################
# channel.basic_consume(f'node_global_round_queue_[{glc}][{0}]',
# rq0.callback_global_1,
# auto_ack=True)
# ##################### callback local #################
# channel.basic_consume(f'node_local_round_queue_[{glc}][{0}]',
# rq0.callback_local_1,
# auto_ack=True)
# ################### callback Local Loss #################
# channel.basic_consume(f'node_local_loss_queue_[{glc}][{0}]',
# rq0.callback_local_loss_1,
# auto_ack=True)
# #######################################################
# channel.basic_consume(f'node_global_round_queue_[{glc}][{0}]',
# rq0.callback_local_loss,
# auto_ack=True)
################### callback global #################
channel.basic_consume(f'node[{0}]_global_round[{t}]',
rq0.callback_global_1,
auto_ack=True)
channel.basic_consume(f'node[{1}]_global_round[{t}]',
rq1.callback_global_1,
auto_ack=True)
##################### callback local #################
channel.basic_consume(f'node[{0}]_local_round[{t}]',
rq0.callback_local_1,
auto_ack=True)
channel.basic_consume(f'node[{1}]_local_round[{t}]',
rq1.callback_local_1,
auto_ack=True)
################### callback Local Loss #################
channel.basic_consume(f'node[{0}]_local_loss_round[{t}]',
rq0.callback_local_loss_1,
auto_ack=True)
channel.basic_consume(f'node[{1}]_local_loss_round[{t}]',
rq1.callback_local_loss_1,
auto_ack=True)
#######################################################
#print("t=",t ," | global_model: ",global_model.get('fc3.bias'))
try:
channel.start_consuming()
except KeyboardInterrupt:
channel.stop_consuming()
connection.close()
lp0 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[0]_local_round[{t}].pkl')
lp1 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[1]_local_round[{t}].pkl')
local_updates.append(lp0)
local_updates.append(lp1)
lp0_loss = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[0]_local_loss_round[{t}].pkl')
lp1_loss = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[1]_local_loss_round[{t}].pkl')
gm = []
gm0 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[0]_global_round[{t}].pkl')
gm1 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[1]_global_round[{t}].pkl')
gm.append(gm0)
gm.append(gm1)
print("gm: ",gm[0].get('fc3.bias'))
print("gm: ",gm[1].get('fc3.bias'))
for i in range(num_selected_users):
global_model = {
k: global_model[k] + gm[i][k]
for k in global_model.keys()
}
loss_locals.append(lp0_loss[0])
loss_locals.append(lp1_loss[0])
print("global_model: ",global_model.get('fc3.bias'))
for i in range(num_selected_users):
global_model = {
k: global_model[k] + local_updates[i][0][k] / num_selected_users
for k in global_model.keys()
}
global_counter = 1
net_glob.load_state_dict(global_model)
net_glob.eval()
test_acc_, _ = test_img(net_glob, dataset_test, args)
test_acc.append(test_acc_)
train_local_loss.append(sum(loss_locals) / len(loss_locals))
print('t {:3d}: '.format(t, ))
print('t {:3d}: train_loss = {:.3f}, norm = Not Recording, test_acc = {:.3f}'.
format(t, train_local_loss[0], test_acc[0]))
print('Submitting new global model: .....')
credentials = pika.PlainCredentials('jahanxb', 'phdunr')
parameters = pika.ConnectionParameters('130.127.134.6',
5672,
'/',
credentials)
connection = pika.BlockingConnection(parameters)
#connection = pika.BlockingConnection(pika.ConnectionParameters(host='amqp://jahanxb:[email protected]:15672'))
channel = connection.channel()
nodes = [0,1]
for n in nodes:
print('Initial Global Model...')
print('Queue Preparation for Global Model')
task_queue = f'master_global_for_node[{n}]_round[{t+1}]'
channel.queue_declare(queue=task_queue, durable=True)
msg = pickle.dumps(global_model)
channel.basic_publish(
exchange='',
routing_key=task_queue,
body=msg,
properties=pika.BasicProperties(delivery_mode=2)
)
print(" [x] Node=", n," Sent Round=",t+1)
connection.close()
elif t==2:
print(f'Next Iteration round: {t}')
print('Waiting for the Client/Slave Node to complete the Process...')
input('enter something to exit:')
print('Initial Global Model...')
print('Queue Preparation for Global Model')
credentials = pika.PlainCredentials('jahanxb', 'phdunr')
parameters = pika.ConnectionParameters('130.127.134.6',
5672,
'/',
credentials)
connection = pika.BlockingConnection(parameters)
#connection = pika.BlockingConnection(pika.ConnectionParameters(host='amqp://jahanxb:[email protected]:15672'))
channel = connection.channel()
master_global_for_node0_round = f'master_global_for_node[{0}]_round[{t}]'
task_queue = master_global_for_node0_round
channel.queue_declare(queue=task_queue, durable=True)
msg = pickle.dumps(global_model)
channel.basic_publish(
exchange='',
routing_key=task_queue,
body=msg,
properties=pika.BasicProperties(delivery_mode=2)
)
master_global_for_node1_round = f'master_global_for_node[{1}]_round[{t}]'
task_queue = master_global_for_node1_round
channel.queue_declare(queue=task_queue, durable=True)
msg = pickle.dumps(global_model)
channel.basic_publish(
exchange='',
routing_key=task_queue,
body=msg,
properties=pika.BasicProperties(delivery_mode=2)
)
print(" [x] Sent Round=",t)
print(f'Round Process Started... Current Round on Master t={t}')
input('Insert Any Key.. If global Model is delivered...: ')
print('################## TrainingTest onum_selected_usersn aggregated Model ######################')
##################### testing on global model #######################
# Access the CLODUAMQP_URL environment variable and parse it (fallback to localhost)
url = 'amqp://jahanxb:[email protected]:5672/'
params = pika.URLParameters(url)
connection = pika.BlockingConnection(params)
channel = connection.channel() # start a channel
# ################### callback global #################
# channel.basic_consume(f'node_global_round_queue_[{glc}][{0}]',
# rq0.callback_global_1,
# auto_ack=True)
# ##################### callback local #################
# channel.basic_consume(f'node_local_round_queue_[{glc}][{0}]',
# rq0.callback_local_1,
# auto_ack=True)
# ################### callback Local Loss #################
# channel.basic_consume(f'node_local_loss_queue_[{glc}][{0}]',
# rq0.callback_local_loss_1,
# auto_ack=True)
# #######################################################
# channel.basic_consume(f'node_global_round_queue_[{glc}][{0}]',
# rq0.callback_local_loss,
# auto_ack=True)
################### callback global #################
channel.basic_consume(f'node[{0}]_global_round[{t}]',
rq0.callback_global_2,
auto_ack=True)
channel.basic_consume(f'node[{1}]_global_round[{t}]',
rq1.callback_global_2,
auto_ack=True)
##################### callback local #################
channel.basic_consume(f'node[{0}]_local_round[{t}]',
rq0.callback_local_2,
auto_ack=True)
channel.basic_consume(f'node[{1}]_local_round[{t}]',
rq1.callback_local_2,
auto_ack=True)
################### callback Local Loss #################
channel.basic_consume(f'node[{0}]_local_loss_round[{t}]',
rq0.callback_local_loss_2,
auto_ack=True)
channel.basic_consume(f'node[{1}]_local_loss_round[{t}]',
rq1.callback_local_loss_2,
auto_ack=True)
#######################################################
#print("t=",t ," | global_model: ",global_model.get('fc3.bias'))
try:
channel.start_consuming()
except KeyboardInterrupt:
channel.stop_consuming()
connection.close()
lp0 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[0]_local_round[{t}].pkl')
lp1 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[1]_local_round[{t}].pkl')
local_updates.append(lp0)
local_updates.append(lp1)
lp0_loss = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[0]_local_loss_round[{t}].pkl')
lp1_loss = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[1]_local_loss_round[{t}].pkl')
gm = []
gm0 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[0]_global_round[{t}].pkl')
gm1 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[1]_global_round[{t}].pkl')
gm.append(gm0)
gm.append(gm1)
print("gm: ",gm[0].get('fc3.bias'))
print("gm: ",gm[1].get('fc3.bias'))
for i in range(num_selected_users):
global_model = {
k: global_model[k] + gm[i][k]
for k in global_model.keys()
}
loss_locals.append(lp0_loss[0])
loss_locals.append(lp1_loss[0])
print("global_model: ",global_model.get('fc3.bias'))
for i in range(num_selected_users):
global_model = {
k: global_model[k] + local_updates[i][0][k] / num_selected_users
for k in global_model.keys()
}
global_counter = 1
net_glob.load_state_dict(global_model)
net_glob.eval()
test_acc_, _ = test_img(net_glob, dataset_test, args)
test_acc.append(test_acc_)
train_local_loss.append(sum(loss_locals) / len(loss_locals))
print('t {:3d}: '.format(t, ))
print('t {:3d}: train_loss = {:.3f}, norm = Not Recording, test_acc = {:.3f}'.
format(t, train_local_loss[0], test_acc[0]))
print('Submitting new global model: .....')
credentials = pika.PlainCredentials('jahanxb', 'phdunr')
parameters = pika.ConnectionParameters('130.127.134.6',
5672,
'/',
credentials)
connection = pika.BlockingConnection(parameters)
#connection = pika.BlockingConnection(pika.ConnectionParameters(host='amqp://jahanxb:[email protected]:15672'))
channel = connection.channel()
nodes = [0,1]
for n in nodes:
print('Initial Global Model...')
print('Queue Preparation for Global Model')
task_queue = f'master_global_for_node[{n}]_round[{t+1}]'
channel.queue_declare(queue=task_queue, durable=True)
msg = pickle.dumps(global_model)
channel.basic_publish(
exchange='',
routing_key=task_queue,
body=msg,
properties=pika.BasicProperties(delivery_mode=2)
)
print(" [x] Node=", n," Sent Round=",t+1)
connection.close()
elif t==3:
print(f'Next Iteration round: {t}')
print('Waiting for the Client/Slave Node to complete the Process...')
input('enter something to exit:')
print('Initial Global Model...')
print('Queue Preparation for Global Model')
credentials = pika.PlainCredentials('jahanxb', 'phdunr')
parameters = pika.ConnectionParameters('130.127.134.6',
5672,
'/',
credentials)
connection = pika.BlockingConnection(parameters)
#connection = pika.BlockingConnection(pika.ConnectionParameters(host='amqp://jahanxb:[email protected]:15672'))
channel = connection.channel()
master_global_for_node0_round = f'master_global_for_node[{0}]_round[{t}]'
task_queue = master_global_for_node0_round
channel.queue_declare(queue=task_queue, durable=True)
msg = pickle.dumps(global_model)
channel.basic_publish(
exchange='',
routing_key=task_queue,
body=msg,
properties=pika.BasicProperties(delivery_mode=2)
)
master_global_for_node1_round = f'master_global_for_node[{1}]_round[{t}]'
task_queue = master_global_for_node1_round
channel.queue_declare(queue=task_queue, durable=True)
msg = pickle.dumps(global_model)
channel.basic_publish(
exchange='',
routing_key=task_queue,
body=msg,
properties=pika.BasicProperties(delivery_mode=2)
)
print(" [x] Sent Round=",t)
print(f'Round Process Started... Current Round on Master t={t}')
input('Insert Any Key.. If global Model is delivered...: ')
print('################## TrainingTest onum_selected_usersn aggregated Model ######################')
##################### testing on global model #######################
# Access the CLODUAMQP_URL environment variable and parse it (fallback to localhost)
url = 'amqp://jahanxb:[email protected]:5672/'
params = pika.URLParameters(url)
connection = pika.BlockingConnection(params)
channel = connection.channel() # start a channel
# ################### callback global #################
# channel.basic_consume(f'node_global_round_queue_[{glc}][{0}]',
# rq0.callback_global_1,
# auto_ack=True)
# ##################### callback local #################
# channel.basic_consume(f'node_local_round_queue_[{glc}][{0}]',
# rq0.callback_local_1,
# auto_ack=True)
# ################### callback Local Loss #################
# channel.basic_consume(f'node_local_loss_queue_[{glc}][{0}]',
# rq0.callback_local_loss_1,
# auto_ack=True)
# #######################################################
# channel.basic_consume(f'node_global_round_queue_[{glc}][{0}]',
# rq0.callback_local_loss,
# auto_ack=True)
################### callback global #################
channel.basic_consume(f'node[{0}]_global_round[{t}]',
rq0.callback_global_3,
auto_ack=True)
channel.basic_consume(f'node[{1}]_global_round[{t}]',
rq1.callback_global_3,
auto_ack=True)
##################### callback local #################
channel.basic_consume(f'node[{0}]_local_round[{t}]',
rq0.callback_local_3,
auto_ack=True)
channel.basic_consume(f'node[{1}]_local_round[{t}]',
rq1.callback_local_3,
auto_ack=True)
################### callback Local Loss #################
channel.basic_consume(f'node[{0}]_local_loss_round[{t}]',
rq0.callback_local_loss_3,
auto_ack=True)
channel.basic_consume(f'node[{1}]_local_loss_round[{t}]',
rq1.callback_local_loss_3,
auto_ack=True)
#######################################################
#print("t=",t ," | global_model: ",global_model.get('fc3.bias'))
try:
channel.start_consuming()
except KeyboardInterrupt:
channel.stop_consuming()
connection.close()
lp0 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[0]_local_round[{t}].pkl')
lp1 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[1]_local_round[{t}].pkl')
local_updates.append(lp0)
local_updates.append(lp1)
lp0_loss = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[0]_local_loss_round[{t}].pkl')
lp1_loss = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[1]_local_loss_round[{t}].pkl')
gm = []
gm0 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[0]_global_round[{t}].pkl')
gm1 = torch.load(f'/mydata/flcode/models/rabbitmq-queues/pickles/node[1]_global_round[{t}].pkl')
gm.append(gm0)
gm.append(gm1)
print("gm: ",gm[0].get('fc3.bias'))
print("gm: ",gm[1].get('fc3.bias'))
for i in range(num_selected_users):
global_model = {
k: global_model[k] + gm[i][k]
for k in global_model.keys()
}
loss_locals.append(lp0_loss[0])
loss_locals.append(lp1_loss[0])
print("global_model: ",global_model.get('fc3.bias'))
for i in range(num_selected_users):
global_model = {
k: global_model[k] + local_updates[i][0][k] / num_selected_users
for k in global_model.keys()
}
global_counter = 1
net_glob.load_state_dict(global_model)
net_glob.eval()
test_acc_, _ = test_img(net_glob, dataset_test, args)
test_acc.append(test_acc_)
train_local_loss.append(sum(loss_locals) / len(loss_locals))
print('t {:3d}: '.format(t, ))
print('t {:3d}: train_loss = {:.3f}, norm = Not Recording, test_acc = {:.3f}'.
format(t, train_local_loss[0], test_acc[0]))
print('Submitting new global model: .....')
credentials = pika.PlainCredentials('jahanxb', 'phdunr')
parameters = pika.ConnectionParameters('130.127.134.6',
5672,
'/',
credentials)
connection = pika.BlockingConnection(parameters)
#connection = pika.BlockingConnection(pika.ConnectionParameters(host='amqp://jahanxb:[email protected]:15672'))
channel = connection.channel()
nodes = [0,1]
for n in nodes:
print('Initial Global Model...')
print('Queue Preparation for Global Model')
task_queue = f'master_global_for_node[{n}]_round[{t+1}]'
channel.queue_declare(queue=task_queue, durable=True)