abdur-rahmaanj / yacs Goto Github PK

To use YACS in your project, you first create a project config file, typically called config.py. This file is the one-stop reference point for all configurable options. It should be very well documented and provide sensible defaults for all options.

See example for code that uses YACS or keep reading below.

# my_project/config.py
from yacs.config import CfgNode as CN


_C = CN()

_C.SYSTEM = CN()
# Number of GPUS to use in the experiment
_C.SYSTEM.NUM_GPUS = 8
# Number of workers for doing things
_C.SYSTEM.NUM_WORKERS = 4

_C.TRAIN = CN()
# A very important hyperparameter
_C.TRAIN.HYPERPARAMETER_1 = 0.1
# The all important scales for the stuff
_C.TRAIN.SCALES = (2, 4, 8, 16)

# Exporting as cfg is a nice convention
cfg = _C

Next, you'll create YAML configuration files; typically you'll make one for each experiment. Each configuration file only overrides the options that are changing in that experiment.

# my_project/experiment.yaml
SYSTEM:
  NUM_GPUS: 2
TRAIN:
  SCALES: (1, 2)

Finally, you'll have your actual project code that uses the config system. After any initial setup it's a good idea to freeze it to prevent further modification by calling the freeze() method. As illustrated below, the config options can either be used a global set of options by importing cfg and accessing it directly, or the cfg can be copied and passed as an argument.

# my_project/main.py
import pprint

import my_project
from config import cfg


if __name__ == "__main__":
  cfg.merge_from_file("experiment.yaml")
  cfg.freeze()
  pprint.pprint(cfg)

  # Example of using the cfg as global access to options
  if cfg.SYSTEM.NUM_GPUS > 0:
    my_project.setup_multi_gpu_support()

  # Example of using a (non-global) copy of the config
  model = my_project.create_model(cfg.clone())

TODO:

• document command line overrides
• give usual tips