def init(self,
desc_name,
expr_name,
run_name,
args=None,
highlight_args=None,
configs=None):
desc, _ = Desc.get_or_create(desc_name=desc_name)
expr, _ = Experiment.get_or_create(desc=desc, expr_name=expr_name)
run = Run(expr=expr, run_name=run_name, command=' '.join(sys.argv))
run.args = ''
run.highlight_args = ''
if args is not None:
run.args = io.dumps_json(args.__dict__)
if highlight_args is not None and hasattr(highlight_args,
'highlight_args'):
run.highlight_args = io.dumps_json(
get_highlight_args(args, highlight_args))
run.configs = ''
run.highlight_configs = ''
if configs is not None:
run.configs = io.dumps_json(configs)
if getattr(args, 'configs', None) is not None:
run.highlight_configs = io.dumps_json(args.configs.kvs)
save_retry(run)
self.desc = desc
self.expr = expr
self.run = run
def dump_metainfo(metainfo=None, **kwargs):
if metainfo is None:
metainfo = {}
metainfo.update(kwargs)
metainfo.setdefault('_cmd', ' '.join(sys.argv))
metainfo.setdefault('_git', get_git_revision_hash())
return io.dumps_json(metainfo, compressed=False)
def _dump_meters(self, meters, mode):
if args.summary_file is not None:
meters_kv = meters._canonize_values('avg')
meters_kv['mode'] = mode
meters_kv['epoch'] = self.current_epoch
with open(args.summary_file, 'a') as f:
f.write(io.dumps_json(meters_kv))
f.write('\n')
def get(self):
spec = json.loads(self.get_argument('spec'))
record = tensorboard_manager.start(spec)
host = self.request.host
if ':' in host:
host = host[:host.find(':')]
if record is not None:
self.write(
io.dumps_json(
{'url': 'http://' + host + ':' + str(record['port'])}))
else:
self.write(
io.dumps_json({
'error':
'Fail to start the tensorboard visualizer: got empty selection.'
}))
def get(self):
spec = json.loads(self.get_argument('spec'))
record = tensorboard_manager.start(spec)
host = self.request.host
if ':' in host:
host = host[:host.find(':')]
self.write(
io.dumps_json(
{'url': 'http://' + host + ':' + str(record['port'])}))
def _log_metric_inner(self, key, value, target, update_func=None):
if target.metrics is None:
current = dict()
else:
current = io.loads_json(target.metrics)
if update_func is not None:
if key in current:
current[key] = update_func(current[key], value)
else:
current[key] = value
else:
current[key] = value
target.metrics = io.dumps_json(current)
save_retry(target)
def _dump_meters(self, meters, mode):
if args.summary_file is not None:
meters_kv = meters._canonize_values('avg')
meters_kv['mode'] = mode
meters_kv['time'] = time.time()
meters_kv['htime'] = str(datetime.datetime.now())
meters_kv['config'] = args.dump_dir
meters_kv['lr'] = self._optimizer.param_groups[0]['lr']
if mode == 'train':
meters_kv['epoch'] = self.current_epoch
meters_kv['data_file'] = args.train_file
else:
meters_kv['epoch'] = -1
meters_kv['data_file'] = args.test_file
meters_kv['error distribution'] = "-".join([
str(self.error_distribution[k])
for k in sorted(self.error_distribution.keys())
])
with open(args.summary_file, 'a') as f:
f.write(io.dumps_json(meters_kv))
f.write('\n')
def update_extra_info(self):
self.extra_info = io.dumps_json(self.extra_info_dict)
def dump(self, filename, values='avg'):
meters_kv = self._canonize_values(values)
with open(filename, 'a') as f:
f.write(io.dumps_json(meters_kv, compressed=False))
f.write('\n')