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import os
import sys
import ujson
# import mlflow
import traceback
# from torch.utils.tensorboard import SummaryWriter
from colbert.utils.utils import print_message, create_directory
class Logger():
def __init__(self, rank, run):
self.rank = rank
self.is_main = self.rank in [-1, 0]
self.run = run
self.logs_path = os.path.join(self.run.path, "logs/")
if self.is_main:
# self._init_mlflow()
# self.initialized_tensorboard = False
create_directory(self.logs_path)
# def _init_mlflow(self):
# mlflow.set_tracking_uri('file://' + os.path.join(self.run.experiments_root, "logs/mlruns/"))
# mlflow.set_experiment('/'.join([self.run.experiment, self.run.script]))
# mlflow.set_tag('experiment', self.run.experiment)
# mlflow.set_tag('name', self.run.name)
# mlflow.set_tag('path', self.run.path)
# def _init_tensorboard(self):
# root = os.path.join(self.run.experiments_root, "logs/tensorboard/")
# logdir = '__'.join([self.run.experiment, self.run.script, self.run.name])
# logdir = os.path.join(root, logdir)
# self.writer = SummaryWriter(log_dir=logdir)
# self.initialized_tensorboard = True
def _log_exception(self, etype, value, tb):
if not self.is_main:
return
output_path = os.path.join(self.logs_path, 'exception.txt')
trace = ''.join(traceback.format_exception(etype, value, tb)) + '\n'
print_message(trace, '\n\n')
self.log_new_artifact(output_path, trace)
def _log_all_artifacts(self):
if not self.is_main:
return
# mlflow.log_artifacts(self.logs_path)
def _log_args(self, args):
if not self.is_main:
return
# for key in vars(args):
# value = getattr(args, key)
# if type(value) in [int, float, str, bool]:
# mlflow.log_param(key, value)
# with open(os.path.join(self.logs_path, 'args.json'), 'w') as output_metadata:
# # TODO: Call provenance() on the values that support it
# ujson.dump(args.input_arguments.__dict__, output_metadata, indent=4)
# output_metadata.write('\n')
with open(os.path.join(self.logs_path, 'args.txt'), 'w') as output_metadata:
output_metadata.write(' '.join(sys.argv) + '\n')
def log_metric(self, name, value, step, log_to_mlflow=True):
if not self.is_main:
return
# if not self.initialized_tensorboard:
# self._init_tensorboard()
# if log_to_mlflow:
# mlflow.log_metric(name, value, step=step)
# self.writer.add_scalar(name, value, step)
def log_new_artifact(self, path, content):
with open(path, 'w') as f:
f.write(content)
# mlflow.log_artifact(path)
def warn(self, *args):
msg = print_message('[WARNING]', '\t', *args)
with open(os.path.join(self.logs_path, 'warnings.txt'), 'a') as output_metadata:
output_metadata.write(msg + '\n\n\n')
def info_all(self, *args):
print_message('[' + str(self.rank) + ']', '\t', *args)
def info(self, *args):
if self.is_main:
print_message(*args)
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