refactoring: move config_pylogger out of the sample app

Soon we will be reusing this function in other sample apps, so let's
move it to app_utils.

apputils/execution_env.py

@@ -14,16 +14,18 @@
 # limitations under the License.
 #
 
-"""Log information regarfing the execution environment.
+"""Log information regarding the execution environment.
 
 This is helpful if you want to recreate an experiment at a later time, or if
 you want to understand the environment in which you execute the training.
 """
 
 import sys
 import os
+import time
 import platform
 import logging
+import logging.config
 import numpy as np
 import torch
 from git import Repo
@@ -76,3 +78,26 @@ def log_git_state():
  logger.debug("Numpy: %s", np.__version__)
  log_git_state()
  logger.debug("App args: %s", app_args)
+
+
+def config_pylogger(log_cfg_file, experiment_name):
+ """Configure the Python logger.
+
+ For each execution of the application, we'd like to create a unique log directory.
+ By default this library is named using the date and time of day, to that directories
+ can be sorted by recency. You can also name yor experiments and prefix the log
+ directory with this name. This can be useful when accessing experiment data from
+ TensorBoard, for example.
+ """
+ timestr = time.strftime("%Y.%m.%d-%H%M%S")
+ filename = timestr if experiment_name is None else experiment_name + '___' + timestr
+ logdir = './logs' + '/' + filename
+ if not os.path.exists(logdir):
+ os.makedirs(logdir)
+ log_filename = os.path.join(logdir, filename + '.log')
+ logging.config.fileConfig(os.path.join(os.getcwd(), log_cfg_file), defaults={'logfilename': log_filename})
+ msglogger = logging.getLogger()
+ msglogger.logdir = logdir
+ msglogger.log_filename = log_filename
+ msglogger.info('Log file for this run: ' + os.path.realpath(log_filename))
+ return msglogger

examples/classifier_compression/compress_classifier.py

@@ -130,21 +130,6 @@
 parser.add_argument('--name', '-n', metavar='NAME', default=None, help='Experiment name')
 
 
-def config_logger(experiment_name):
- # The Distiller library writes logs to the Python logger, so we configure it.
- timestr = time.strftime("%Y.%m.%d-%H%M%S")
- filename = timestr if experiment_name is None else experiment_name + '___' + timestr
- logdir = './logs' + '/' + filename
- if not os.path.exists(logdir):
- os.makedirs(logdir)
- log_filename = os.path.join(logdir, filename + '.log')
- logging.config.fileConfig(os.path.join(script_dir, 'logging.conf'), defaults={'logfilename': log_filename})
- msglogger = logging.getLogger()
- msglogger.logdir = logdir
- msglogger.log_filename = log_filename
- msglogger.info('Log file for this run: ' + os.path.realpath(log_filename))
- return msglogger
-
 def check_pytorch_version():
  if torch.__version__ < '0.4.0':
  print("\nNOTICE:")
@@ -161,7 +146,7 @@ def main():
  global msglogger
  check_pytorch_version()
  args = parser.parse_args()
- msglogger = config_logger(args.name)
+ msglogger = apputils.config_pylogger('logging.conf', args.name)
 
  # Log various details about the execution environment. It is sometimes useful
  # to refer to past experiment executions and this information may be useful.
@@ -514,7 +499,7 @@ def get_inference_var(tensor):
  except Exception as e:
  if msglogger is not None:
  msglogger.error(traceback.format_exc())
- exit(1)
+ raise e
  finally:
  if msglogger is not None:
  msglogger.info('')