def render(self):
hostname = socket.gethostname()
mem = human_size(psutil.TOTAL_PHYMEM, False)
arch = platform.machine()
disk = human_size(psutil.disk_usage("/").total)
# cpu = cmdexec("dmidecode -t4 | grep Version:")
cpu = cmdexec('cat /proc/cpuinfo | grep "model name"')
cpu = cpu[0].split("\n")
cpu_descr = "%s x %s" % (cpu[0].split(":")[1], psutil.NUM_CPUS)
boot = psutil.BOOT_TIME
boot = datetime.datetime.fromtimestamp(int(boot)).strftime("%d/%m/%Y %H:%M:%S")
try:
manufacturer = cmdexec("dmidecode -t 2 | grep Manufacturer:")
manufacturer = manufacturer[0].split("\n")
manufacturer = manufacturer[0].split(":")[1]
product_name = cmdexec('dmidecode -t 2 | grep "Product Name"')
product_name = product_name[0].split("\n")
product_name = product_name[0].split(":")[1]
baseboard = manufacturer + " " + product_name
except Exception, e:
logging.debug(e)
baseboard = "Não disponível"
def render(self):
y = yum.YumBase()
installeds = []
for pkg in y.doPackageLists('installed'):
installeds.append({"group" : pkg.group,
"name" : pkg.name,
"summary" : pkg.summary,
"size" : human_size(pkg.size),
"packager" : pkg.packager})
tmplist = sorted(installeds, key=lambda k: k['group'])
installeds = tmplist
return self.render_string("modules/packages.html", installeds=installeds)
def render(self, kills=False):
ps_list = []
for ps in psutil.process_iter():
ps_list.append(
{
"name": ps.name,
"username": ps.username,
"cpu": "%0.2f" % ps.get_cpu_percent(interval=0),
"pid": ps.pid,
"memory": human_size(ps.get_memory_info().rss, True),
}
)
ps_list.sort(reverse=True)
return self.render_string("modules/process.html", ps_list=ps_list, kills=kills)
def main():
arg_parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
arg_parser.add_argument(
"--doit",
action="store_true",
help="only with this flag, we will delete something")
args = arg_parser.parse_args()
relevant_train_setups = {}
best_recog_scores = [] # list of (score, setup_name, ep)
for setup_name, info in sorted(train_setups.items()):
if not info["existing_setup"]:
continue
# It's not finished if there are still any suggestions to do recog on.
if not train_setup_finished(info, allow_existing_recog=False):
continue
best_recog_score, best_recog_epochs = train_setup_get_best_recog(info)
relevant_train_setups[setup_name] = info
for ep in best_recog_epochs:
best_recog_scores.append((best_recog_score, setup_name, ep))
print("Out of the finished setups:")
best_recog_scores.sort(reverse=not Settings.recog_score_lower_is_better)
if best_recog_scores:
print("Number of different available scores: %i" %
len(best_recog_scores))
keep_n_best = 10
print("First %i scores:" % keep_n_best)
for score, setup_name, ep in best_recog_scores[:keep_n_best]:
score_str = "%.1f%% %s" % (score,
Settings.recog_metric_name.upper())
print(" %s, setup %s, epoch %i" % (score_str, setup_name, ep))
score_str = "%.1f%% %s" % (best_recog_scores[-1][0],
Settings.recog_metric_name.upper())
print("Worst score: %s" % score_str)
if len(best_recog_scores) < keep_n_best:
cutoff_score = best_recog_scores[-1][0]
else:
cutoff_score = best_recog_scores[keep_n_best][0]
score_str = "%.1f%% %s" % (cutoff_score,
Settings.recog_metric_name.upper())
print(
"Keep everything for setups with a score better or equal than %s."
% score_str)
else:
print("No recog scores found?")
cutoff_score = None
total_fns_to_delete = []
total_file_size = 0
for setup_name, info in sorted(relevant_train_setups.items()):
assert isinstance(setup_name, str) and isinstance(info, dict)
best_recog_score, best_recog_epochs = train_setup_get_best_recog(info)
below_cutoff = False
if best_recog_epochs:
if Settings.recog_score_lower_is_better:
if best_recog_score <= cutoff_score:
below_cutoff = True
else:
if best_recog_score >= cutoff_score:
below_cutoff = True
if below_cutoff:
# print("setup %s with score %.1f below cutoff, skip" % (setup_name, best_recog_score))
continue
if best_recog_score is None:
best_recog_score = float(
"inf") if Settings.recog_score_lower_is_better else float(
"-inf")
# TODO: by default, keep nothing.
# have clever logic when to keep. e.g. when this is used by other import (import_model_train_epoch1)...
# Keep the best epochs.
# If there are no recogs at all, it means we stopped it, usually because it was bad -> cleanup all.
keep_epochs = list(best_recog_epochs)
for epoch in ast.literal_eval(info["_multisetup_info"].get(
"keep_epochs", "[]")):
assert isinstance(epoch, int)
keep_epochs.append(epoch)
delete_epochs = find_models(info)
for epoch in keep_epochs:
if epoch in delete_epochs:
del delete_epochs[epoch]
if not delete_epochs: # already cleaned up
continue
print("finished setup:", setup_name)
if keep_epochs:
print(" best recog: %.1f%% %s, epochs to keep %r" %
(best_recog_score, Settings.recog_metric_name.upper(),
keep_epochs))
else:
print(" (no recog, model was stopped, probably bad)")
fns_to_delete = []
for _, fns in sorted(delete_epochs.items(), key=models_sort_key):
fns_to_delete.extend(fns)
file_size = 0
for fn in fns_to_delete:
file_size += os.path.getsize(fn)
print(" delete models:",
sorted(delete_epochs.keys(), key=models_sort_key),
human_size(file_size))
total_fns_to_delete.extend(fns_to_delete)
total_file_size += file_size
print("Total files to delete: %i num, %s" %
(len(total_fns_to_delete), human_size(total_file_size)))
if args.doit:
print("Deleting now!")
for fn in total_fns_to_delete:
os.remove(fn)
print("Done.")
else:
print("Not deleting now. Use --doit.")
