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main_global_server_scale.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
from pymongo import MongoClient
from cryptography.fernet import Fernet
from cassandra.cluster import Cluster
from cassandra.query import named_tuple_factory, dict_factory
import asyncio
import os,paramiko,datetime
from Pyfhel import Pyfhel, PyPtxt, PyCtxt
from cryptography.fernet import Fernet
import asyncio
import os,paramiko,datetime, zlib
from declared_nodes import client_nodes_addr, mongodb_url, cassandra_addr
import psycopg2
from neo4j import GraphDatabase
import logging
from neo4j.exceptions import ServiceUnavailable
nodes =11
node_index = 1
casandra_cluster = Cluster([cassandra_addr],port=9042)
async def waiting_exception_to_interupt():
print("Waiting...")
await asyncio.sleep(5)
print('....Wait Completed..Raising Exception')
raise KeyboardInterrupt
async def raise_me():
task = asyncio.create_task(waiting_exception_to_interupt())
await task
async def waiting_exception_to_interupt():
print("Waiting...")
await asyncio.sleep(5)
print('....Wait Completed..Raising Exception')
raise KeyboardInterrupt
async def raise_me():
task = asyncio.create_task(waiting_exception_to_interupt())
await task
#nodes = 11 # 10 nodes (11 for loop)
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 send_global_round(node_addr,model_path):
localpath = model_path
remotepath = model_path
print('Connecting via ssh...')
ssh = paramiko.SSHClient()
ssh.load_host_keys(os.path.expanduser(os.path.join("~", ".ssh", "known_hosts")))
ssh.connect(node_addr, username='jahanxb')
sftp = ssh.open_sftp()
print('sftp opened...')
sftp.put(localpath, remotepath)
sftp.close()
print('sftp closed.. Model Sent!... ssh connection closing soon..')
ssh.close()
def aggregation_avg(global_model, local_updates):
'''
simple average
'''
# model_update = {k: local_updates[0][k] *0.0 for k in local_updates[0].keys()}
# for i in range(len(local_updates)):
# model_update = {k: model_update[k] + local_updates[i][k] for k in global_model.keys()}
# global_model = {k: global_model[k] + model_update[k]/ len(local_updates) for k in global_model.keys()}
# return global_model
model_update = {k: local_updates[0][k] *0.0 for k in local_updates[0]}
for i in range(len(local_updates)):
model_update = {k: model_update[k] + local_updates[i][k] for k in global_model.keys()}
global_model = {k: global_model[k] + model_update[k]/ len(local_updates) for k in global_model.keys()}
return global_model
def serve_cassandra(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))
sample_per_users = 25000
print('num. of samples per user:{}'.format(sample_per_users))
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 = []
loss_locals = []
local_updates = []
delta_norms = []
#num_selected_users = 2
####### create or drop Cassandra KEYSPACE ################
'''
create keyspace iteration_status with replication {'class':'SimpleStrategy','replication_factor':10}; # replication factor depends on num of nodes
'''
'''
USE iteration_status;
'''
table_string = '''
CREATE TABLE master_global (
task_id text,
state_ready boolean,
consumed boolean,
conv1_weight varchar,
conv1_bias varchar,
conv2_weight varchar,
conv2_bias varchar,
conv3_weight varchar,
conv3_bias varchar,
fc1_weight varchar,
fc1_bias varchar,
fc2_weight varchar,
fc2_bias varchar,
fc3_weight varchar,
fc3_bias varchar,
data varchar,
key varchar,
PRIMARY KEY (task_id)
);
'''
table_string = '''
CREATE TABLE master_global (
task_id text,
state_ready int,
consumed int,
key text,
data text,
PRIMARY KEY (task_id)
);
'''
table_string_client = '''
CREATE TABLE cass_client_cluster (
task_id text,
state_ready int,
consumed int,
key text,
data text,
PRIMARY KEY (task_id)
);
'''
try:
session = casandra_cluster.connect()
session.execute("CREATE KEYSPACE IF NOT EXISTS iteration_status with replication = {'class':'SimpleStrategy','replication_factor':10};")
except Exception as e:
print(e)
try:
session = casandra_cluster.connect('iteration_status',wait_for_all_pools=True)
try:
session.execute('DROP TABLE iteration_status.master_global')
session.execute(table_string)
except:
session.execute(table_string)
pass
except Exception as e:
print(e)
pass
try:
session = casandra_cluster.connect('iteration_status',wait_for_all_pools=True)
try:
session.execute('DROP TABLE iteration_status.cass_client_cluster')
session.execute(table_string_client)
except:
session.execute(table_string_client)
pass
except Exception as e:
print(e)
pass
#########################################################
for t in range(args.round):
seconds_to_match = 0
loss_locals = []
local_updates = []
delta_norms = []
m = max(int(args.frac * args.num_users), 1)
args.local_lr = args.local_lr * args.decay_weight
selected_idxs = list(np.random.choice(range(args.num_users), m, replace=False))
print(selected_idxs)
num_selected_users = len(selected_idxs)
print("num_selected_users: ",num_selected_users)
for nodeid in range(node_index,nodes):
if t==0:
print('Initial Global Model...')
print('Queue Preparation for Global Model')
master_global_for_round = f'master_global_for_node[{nodeid}]_round[{t}]'
msg = pickle.dumps(global_model)
####################################################################
######## Encryption Step 2 - Save encrypt data #####################
encrypt_key_path = f'/mydata/flcode/models/node_encrypted/global_models/{master_global_for_round}'
key = Fernet.generate_key()
fernet = Fernet(key=key)
encmsg = fernet.encrypt(msg)
print("key: ",key)
###################################################################
torch.save(msg,f"/mydata/flcode/models/nodes_trained_model/global_models/{master_global_for_round}.pkl")
model_path = f"/mydata/flcode/models/nodes_trained_model/global_models/{master_global_for_round}.pkl"
############### Insert data on Cassandra #######
session = casandra_cluster.connect()
print("key: ", key)
print("key: ", type(key))
keystr = str(key).replace('\'',"\"")
datastr = str(encmsg).replace('\'',"\"")
print("keystr: ",keystr)
insert_cql = f"""INSERT INTO iteration_status.master_global (task_id, state_ready, consumed , key, data)
VALUES ({"'"+master_global_for_round+"'"}, {0}, {-1} , {"'"+keystr+"'"}, {"'"+datastr+"'"} ); """
# print('insert_cql: ',insert_cql)
session.execute(insert_cql)
################################################
else:
pass
print(" [x] Sent Round=",t)
print(f'Round Process Started... Current Round on Master t={t}')
for n in range(node_index,nodes):
'''LOCAL ROUND CHECK'''
while True:
task_id = f'node[{n}]_local_round[{t}]'
try:
time.sleep(5)
seconds_to_match = seconds_to_match + 5
t1 = t1 + 5
session = casandra_cluster.connect('iteration_status')
session.row_factory = dict_factory
select_str = f"select task_id,consumed,state_ready,key,data from iteration_status.cass_client_cluster where task_id = '{task_id}'; "
print("select_Str: ",select_str)
stn = session.execute(select_str)
status = stn[0]
print("STATUS: ",type(status))
if status.get('state_ready') == 0:
print('status: ',200,' For :',status.get('task_id'))
print("key status:",status.get('key'))
print("key status type:",type(status.get('key')))
keystr = str(status.get('key')).replace('\"',"\'")
datastr = str(status.get('data')).replace('\"',"\'")
keystr = keystr.replace("b'","")
keystr = keystr.replace("'","")
datastr = datastr.replace("b'","")
datastr = datastr.replace("'","")
local_model = bytes(datastr, 'utf-8')
local_model_key = bytes(keystr, 'utf-8')
break
else:
pass
except Exception as e:
print(f'@ [{task_id}] | Cassandra Exception Thrown :',e)
'''LOCAL LOSS ROUND CHECK '''
while True:
task_id = f'node[{n}]_local_loss_round[{t}]'
try:
time.sleep(5)
seconds_to_match = seconds_to_match + 5
t1 = t1 + 5
session = casandra_cluster.connect('iteration_status')
session.row_factory = dict_factory
select_str = f"select task_id,consumed,state_ready,key,data from iteration_status.cass_client_cluster where task_id = '{task_id}'; "
print("select_Str: ",select_str)
stn = session.execute(select_str)
status = stn[0]
print("STATUS: ",type(status))
if status.get('state_ready') == 0:
print('status: ',200,' For :',status.get('task_id'))
print("key status:",status.get('key'))
print("key status type:",type(status.get('key')))
keystr = str(status.get('key')).replace('\"',"\'")
datastr = str(status.get('data')).replace('\"',"\'")
keystr = keystr.replace("b'","")
keystr = keystr.replace("'","")
datastr = datastr.replace("b'","")
datastr = datastr.replace("'","")
local_model_loss = bytes(datastr, 'utf-8')
local_model_loss_key = bytes(keystr, 'utf-8')
break
else:
pass
except Exception as e:
print(f'@ [{task_id}] | Cassandra Exception Thrown :',e)
print('################## TrainingTest onum_selected_usersn aggregated Model ######################')
fernet = Fernet(local_model_key)
lp = fernet.decrypt(local_model)
lp = list(pickle.loads(lp))
local_updates.append(lp)
fernet = Fernet(local_model_loss_key)
lp_loss = fernet.decrypt(local_model_loss)
lp_loss = list(pickle.loads(lp_loss))
loss_locals.append(lp_loss[0])
print("num_selected_users: ",num_selected_users)
for i in range(num_selected_users):
print("i=",i)
global_model = {
k: global_model[k] + local_updates[i][0][k] / num_selected_users
for k in global_model.keys()
}
print("global_model: ",global_model.keys())
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}, test_acc = {:.3f}'.
format(t, train_local_loss[-1], test_acc[-1]))
print('Submitting new global model: .....')
# send model to nodes from here
for nn in range(node_index,nodes):
master_global_for_round = f'master_global_for_node[{nn}]_round[{t+1}]'
msg = pickle.dumps(global_model)
torch.save(msg,f"/mydata/flcode/models/nodes_trained_model/global_models/{master_global_for_round}.pkl")
model_path = f"/mydata/flcode/models/nodes_trained_model/global_models/{master_global_for_round}.pkl"
key = Fernet.generate_key()
fernet = Fernet(key=key)
encmsg = fernet.encrypt(msg)
print("key: ",key)
print("key: ", key)
print("key: ", type(key))
keystr = str(key).replace('\'',"\"")
datastr = str(encmsg).replace('\'',"\"")
print("keystr: ",keystr)
insert_cql = f"""INSERT INTO iteration_status.master_global (task_id, state_ready, consumed , key, data)
VALUES ({"'"+master_global_for_round+"'"}, {0}, {-1} , {"'"+keystr+"'"}, {"'"+datastr+"'"} ); """
session.execute(insert_cql)
print(" [x] Node=", nn," Sent Round=",t+1)
t2 = time.time()
#dbs_time = datetime.timedelta(seconds=seconds_to_match)
dbs_time = t2 - t1
#dbs_time = dbs_time - seconds_to_match
hours, rem = divmod(dbs_time, 3600)
minutes, seconds = divmod(rem, 60)
print("training time: {:0>2}:{:0>2}:{:05.2f}".format(int(hours), int(minutes), seconds))
time_taken = "training time: {:0>2}:{:0>2}:{:05.2f}".format(int(hours), int(minutes), seconds)
result = '\n'+ time_taken+' \n '+'t {:3d}: train_loss = {:.3f}, test_acc = {:.3f}'.format(t, train_local_loss[-1], test_acc[-1]) + '\n'
with open('/mydata/flcode/output/cassandra-10nodes-results-log.txt', 'a') as the_file:
the_file.write(result)
the_file.close()
def serve_mongodb(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))
sample_per_users = 25000
print('num. of samples per user:{}'.format(sample_per_users))
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 = []
loss_locals = []
local_updates = []
delta_norms = []
num_selected_users = 2
#mconn = MongoClient('mongodb+srv://jahanxb:[email protected]/?retryWrites=true&w=majority')
mconn = MongoClient(mongodb_url)
mdb = mconn['iteration_status']
try:
mdb.create_collection('master_global')
except Exception as e:
print(e)
pass
try:
mdb.create_collection('master_global')
except Exception as e:
print(e)
pass
for t in range(args.round):
seconds_to_match = 0
loss_locals = []
local_updates = []
delta_norms = []
m = max(int(args.frac * args.num_users), 1)
args.local_lr = args.local_lr * args.decay_weight
selected_idxs = list(np.random.choice(range(args.num_users), m, replace=False))
print(selected_idxs)
num_selected_users = len(selected_idxs)
print("num_selected_users: ",num_selected_users)
for nodeid in range(node_index,nodes):
if t==0:
print('Initial Global Model...')
print('Queue Preparation for Global Model')
master_global_for_round = f'master_global_for_node[{nodeid}]_round[{t}]'
msg = pickle.dumps(global_model)
####################################################################
######## Encryption Step 2 - Save encrypt data #####################
encrypt_key_path = f'/mydata/flcode/models/node_encrypted/global_models/{master_global_for_round}'
key = Fernet.generate_key()
fernet = Fernet(key=key)
encmsg = fernet.encrypt(msg)
print("key: ",key)
torch.save(msg,f"/mydata/flcode/models/nodes_sftp/global_models/{master_global_for_round}.pkl")
model_path = f"/mydata/flcode/models/nodes_sftp/global_models/{master_global_for_round}.pkl"
# send model to nodes from here
print("mongodb_client_cluster.get() =",client_nodes_addr.get(nodeid))
#send_global_round(client_nodes_addr.get(nodeid),model_path)
#compressed_pickle = blosc.compress(encmsg)
compressed_encmsg = zlib.compress(encmsg)
size = sys.getsizeof(compressed_encmsg)
print(f'Size of msg: {size} bytes')
mdb_msg = {'task_id':master_global_for_round,'state-ready':True,'consumed':False,
"data":compressed_encmsg,
"key":key
}
mdb.master_global.insert_one(mdb_msg)
else:
pass
print(" [x] Sent Round=",t)
print(f'Round Process Started... Current Round on Master t={t}')
for n in range(node_index,nodes):
'''LOCAL ROUND CHECK'''
while True:
task_id = f'node[{n}]_local_round[{t}]'
try:
time.sleep(5)
seconds_to_match = seconds_to_match + 5
t1 = t1 + 5
status = mdb.mongodb_client_cluster.find_one({'task_id':task_id})
if status.get('state-ready') == True:
print('status: ',200,' For :',status.get('task_id'))
local_model_key = status.get('key')
local_model = status.get('data')
local_model = zlib.decompress(local_model)
break
else:
pass
except Exception as e:
print(f'@ [{task_id}] | MongoDB Exception Thrown :',e)
'''LOCAL LOSS ROUND CHECK '''
while True:
task_id = f'node[{n}]_local_loss_round[{t}]'
try:
time.sleep(5)
seconds_to_match = seconds_to_match + 5
t1 = t1 + 5
status = mdb.mongodb_client_cluster.find_one({'task_id':task_id})
if status.get('state-ready') == True:
print('status: ',200,' For :',status.get('task_id'))
local_model_loss_key = status.get('key')
local_model_loss = status.get('data')
local_model_loss = zlib.decompress(local_model_loss)
break
else:
pass
except Exception as e:
print(f'@ [{task_id}] | MongoDB Exception Thrown :',e)
############################################################################################
print('################## TrainingTest onum_selected_usersn aggregated Model ######################')
#lp = torch.load(f'/mydata/flcode/models/nodes_sftp/nodes_local/node[{n}]_local_round[{t}].pkl')
fernet = Fernet(local_model_key)
lp = fernet.decrypt(local_model)
lp = list(pickle.loads(lp))
local_updates.append(lp)
#lp_loss = torch.load(f'/mydata/flcode/models/nodes_sftp/nodes_local_loss/node[{n}]_local_loss_round[{t}].pkl')
fernet = Fernet(local_model_loss_key)
lp_loss = fernet.decrypt(local_model_loss)
lp_loss = list(pickle.loads(lp_loss))
loss_locals.append(lp_loss[0])
print("num_selected_users: ",num_selected_users)
for i in range(num_selected_users):
print("i=",i)
global_model = {
k: global_model[k] + local_updates[i][0][k] / num_selected_users
for k in global_model.keys()
}
print("global_model: ",global_model.keys())
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}, test_acc = {:.3f}'.
format(t, train_local_loss[-1], test_acc[-1]))
print('Submitting new global model: .....')
# send model to nodes from here
for nn in range(node_index,nodes):
master_global_for_round = f'master_global_for_node[{nn}]_round[{t+1}]'
msg = pickle.dumps(global_model)
torch.save(msg,f"/mydata/flcode/models/nodes_sftp/global_models/{master_global_for_round}.pkl")
model_path = f"/mydata/flcode/models/nodes_sftp/global_models/{master_global_for_round}.pkl"
key = Fernet.generate_key()
fernet = Fernet(key=key)
encmsg = fernet.encrypt(msg)
print("key: ",key)
compressed_encmsg = zlib.compress(encmsg)
#send_global_round(client_nodes_addr.get(nn),model_path)
mdb_msg = {'task_id':master_global_for_round,'state-ready':True,'consumed':False,
"data":compressed_encmsg,
"key":key
}
mdb.master_global.insert_one(mdb_msg)
print(" [x] Node=", nn," Sent Round=",t+1)
t2 = time.time()
#dbs_time = datetime.timedelta(seconds=seconds_to_match)
dbs_time = t2 - t1
#dbs_time = dbs_time - seconds_to_match
hours, rem = divmod(dbs_time, 3600)
minutes, seconds = divmod(rem, 60)
print("training time: {:0>2}:{:0>2}:{:05.2f}".format(int(hours), int(minutes), seconds))
time_taken = "training time: {:0>2}:{:0>2}:{:05.2f}".format(int(hours), int(minutes), seconds)
result = '\n'+ time_taken+' \n '+'t {:3d}: train_loss = {:.3f}, test_acc = {:.3f}'.format(t, train_local_loss[-1], test_acc[-1]) + '\n'
with open('/mydata/flcode/output/mongodb-10nodes-results-log.txt', 'a') as the_file:
the_file.write(result)
the_file.close()
def serve_scp(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))
sample_per_users = 25000
print('num. of samples per user:{}'.format(sample_per_users))
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 = []
loss_locals = []
local_updates = []
delta_norms = []
#num_selected_users = 2
mconn = MongoClient(mongodb_url)
mdb = mconn['iteration_status']
try:
mdb.create_collection('master_global')
except Exception as e:
print(e)
pass
try:
mdb.create_collection('master_global')
except Exception as e:
print(e)
pass
for t in range(args.round):
loss_locals = []
local_updates = []
delta_norms = []
m = max(int(args.frac * args.num_users), 1)
args.local_lr = args.local_lr * args.decay_weight
selected_idxs = list(np.random.choice(range(args.num_users), m, replace=False))
print(selected_idxs)
num_selected_users = len(selected_idxs)
for nodeid in range(node_index,nodes):
if t==0:
print('Initial Global Model...')
print('Queue Preparation for Global Model')
master_global_for_round = f'master_global_for_node[{nodeid}]_round[{t}]'
#tensor = global_model.keys()
#num_values = tensor.numel()
#num_value = global_model.numel()
#num_values = torch.numel(global_model)
#sum_of_values = sum([sum(v) for v in global_model.values()])
#print(sum_of_values)
total_values = sum([v.numel() for v in global_model.values()])
print(total_values)
print("num_value on global model before aggregation: ",total_values)
#print("num_value on global model: ",sum_of_values)
msg = pickle.dumps(global_model)
torch.save(msg,f"/mydata/flcode/models/nodes_trained_model/global_models/{master_global_for_round}.pkl")
model_path = f"/mydata/flcode/models/nodes_trained_model/global_models/{master_global_for_round}.pkl"
# send model to nodes from here
print("mongodb_client_cluster.get() =",client_nodes_addr.get(nodeid))
send_global_round(client_nodes_addr.get(nodeid),model_path)
mdb_msg = {'task_id':master_global_for_round,'state-ready':True,'consumed':False}
mdb.master_global.insert_one(mdb_msg)
else:
pass
print(" [x] Sent Round=",t)
print(f'Round Process Started... Current Round on Master t={t}')
for n in range(node_index,nodes):
######################### Check status of Queues through MongoDB ############################
'''GLOBAL ROUND CHECK'''
'''LOCAL ROUND CHECK'''
while True:
task_id = f'node[{n}]_local_round[{t}]'
try:
time.sleep(5)
status = mdb.mongodb_client_cluster.find_one({'task_id':task_id})
if status.get('state-ready') == True:
print('status: ',200,' For :',status.get('task_id'))
break
else:
pass
except Exception as e:
print(f'@ [{task_id}] | MongoDB Exception Thrown :',e)
'''LOCAL LOSS ROUND CHECK '''
while True:
task_id = f'node[{n}]_local_loss_round[{t}]'
try:
time.sleep(5)
status = mdb.mongodb_client_cluster.find_one({'task_id':task_id})
if status.get('state-ready') == True:
print('status: ',200,' For :',status.get('task_id'))
break
else:
pass
except Exception as e:
print(f'@ [{task_id}] | MongoDB Exception Thrown :',e)
############################################################################################
print('################## TrainingTest onum_selected_usersn aggregated Model ######################')
lp = torch.load(f'/mydata/flcode/models/nodes_trained_model/nodes_local/node[{n}]_local_round[{t}].pkl')
lp = list(pickle.loads(lp))
local_updates.append(lp)
lp_loss = torch.load(f'/mydata/flcode/models/nodes_trained_model/nodes_local_loss/node[{n}]_local_loss_round[{t}].pkl')
lp_loss = list(pickle.loads(lp_loss))
loss_locals.append(lp_loss[0])