def main():
sys.stdout = lib.Logger(logging.info)
sys.stderr = lib.Logger(logging.warning)
run_fim_scan = FIM_scan()
run_fim_scan.run()
import jira
import lib
import sys
import logging
sys.stdout = lib.Logger(logging.info)
sys.stderr = lib.Logger(logging.warning)
# Build config
config = lib.ConfigHelper()
# Halo events
events = lib.HaloEvents(config)
# Matcher object
matcher = lib.Matcher(config.match_list)
jira = jira.JiraController(config.jira)
# Iterate over events, quarantine targeted workloads
while True:
for event in events:
if matcher.is_a_match(event["type"]):
exist, summary = jira.check_ticket_existence(event)
if event["type"] == "issue_resolved" and exist:
jira.transition_ticket(summary)
elif event["type"] != "issue_resolved" and not exist:
jira.create_ticket(event, summary)
def main():
sys.stdout = lib.Logger(logging.info)
sys.stderr = lib.Logger(logging.warning)
halo_arf = AutoRestoreFw()
halo_arf.run()
help="Maximum length of predictions.")
# Others
parser.add_argument("-eps",
type=float,
default=0.75,
help="tolerance in entropy constraint")
parser.add_argument("-mu",
type=float,
default=0.8,
help="threshold for a phrase to be stored in Buffer")
opt = parser.parse_args()
# Setup a logger here for logging the training process
trpro_logger = lib.Logger()
if opt.eval:
save_file = 'evalInfo.txt'
stat_logger = None
samples_logger = None
else:
# training mode
save_file = 'trainInfo.txt'
if opt.use_bipnmt:
stat_logger = lib.Logger()
stat_logger.set_log_file(os.path.join(opt.save_dir, "stat.txt"))
samples_logger = lib.Logger()
samples_logger.set_log_file(os.path.join(opt.save_dir, "samples.txt"))
else:
stat_logger = None
samples_logger = None