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# Init Blynk instance |
if blynk_enabled: |
print("Blynk upload is enabled") |
blynk = blynklib.Blynk(read_config.blynk_token, |
server=read_config.blynk_server.strip(), |
heartbeat=read_config.blynk_heartbeat) |
@blynk.handle_event("connect") |
def connect_handler(): |
global is_connected |
if not is_connected: |
is_connected = True |
print("Connected to cloud server") |
syslog.syslog(syslog.LOG_NOTICE, "Connected to cloud server") |
@blynk.handle_event("disconnect") |
def disconnect_handler(): |
global is_connected |
if is_connected: |
is_connected = False |
print("Disconnected from cloud server") |
syslog.syslog(syslog.LOG_NOTICE, "Disconnected from cloud server") |
# Init Nighscout instance (if requested) |
if nightscout_enabled: |
print("Nightscout upload is enabled") |
nightscout = nightscoutlib.nightscout_uploader(server = read_config.nightscout_server, |
secret = read_config.nightscout_api_secret) |
########################################################## |
# Initialization |
########################################################## |
syslog.syslog(syslog.LOG_NOTICE, "Starting DD-Guard daemon, version "+VERSION) |
# Init signal handler |
signal.signal(signal.SIGINT, on_sigterm) |
signal.signal(signal.SIGTERM, on_sigterm) |
upload_live_data.active = False |
# Perform first upload immediately |
# Subsequent uploads will be scheduled according to received data timestamp |
t = threading.Thread(target=upload_live_data, args=()) |
t.start() |
########################################################## |
# Main loop |
########################################################## |
while True: |
if blynk_enabled: |
blynk.run() |
else: |
time.sleep(0.1) |
# <FILESEP> |
import os |
import json |
import argparse |
import time |
import numpy as np |
import torch |
from torch.autograd import Variable |
import torch.nn.functional as F |
import torch.nn as nn |
from sklearn.utils.class_weight import compute_class_weight |
from tensorboardX import SummaryWriter |
from fastprogress import master_bar, progress_bar |
# Remove warning |
import warnings |
warnings.filterwarnings("ignore", category=UserWarning) |
from scipy.sparse import SparseEfficiencyWarning |
warnings.simplefilter('ignore', SparseEfficiencyWarning) |
from config import * |
from problems.tsp.tsp_reader import TSPReader |
from problems.tsptw.tsptw_reader import TSPTWReader |
from models.gcn_model import ResidualGatedGCNModel |
from models.sparse_wrapper import wrap_sparse |
from models.prep_wrapper import PrepWrapResidualGatedGCNModel |
parser = argparse.ArgumentParser(description='gcn_tsp_parser') |
parser.add_argument('-c','--config', type=str, default="configs/default.json") |
args = parser.parse_args() |
config_path = args.config |
config = get_config(config_path) |
print("Loaded {}:\n{}".format(config_path, config)) |
is_tsptw = config.get('problem', 'tsp') == 'tsptw' |
DataReader = TSPTWReader if is_tsptw else TSPReader |
if torch.cuda.is_available(): |
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