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TOML yet Another Configuration System

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TACS was created as a lightweight library to define and manage system configurations, such as those commonly found in software designed for scientific experimentation. These "configurations" typically cover concepts like hyperparameters used in training a machine learning model or configurable model hyperparameters, such as the depth of a convolutional neural network. Since you're doing science, reproducibility is paramount and thus you need a reliable way to serialize experimental configurations. TACS uses TOML as a simple, human readable serialization format. The paradigm is: your code + a TACS config for experiment E (+ external dependencies + hardware + other nuisance terms ...) = reproducible experiment E. While you might not be able to control everything, at least you can control your code and your experimental configuration. YACS is here to help you with that.

TACS grew out of the experimental configuration systems used in: py-faster-rcnn and Detectron.


TACS can be used in a variety of flexible ways. There are two main paradigms:

  • Configuration as local variable
  • Configuration as a global singleton

It's up to you which you prefer to use, though the local variable route is recommended.

To use TACS in your project, you first create a project config file, typically called or 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.

# my_project/
from tacs.config import CfgNode as CN

_C = CN()

# Number of GPUS to use in the experiment
# Number of workers for doing things

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

def get_cfg_defaults():
  """Get a yacs CfgNode object with default values for my_project."""
  # Return a clone so that the defaults will not be altered
  # This is for the "local variable" use pattern
  return _C.clone()

# Alternatively, provide a way to import the defaults as
# a global singleton:
# cfg = _C  # users can `from config import cfg`

Next, you'll create TOML 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.toml
  NUM_GPUS = 2
  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/

import my_project
from config import get_cfg_defaults  # local variable usage pattern, or:
# from config import cfg  # global singleton usage pattern

if __name__ == "__main__":
  cfg = get_cfg_defaults()

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

  model = my_project.create_model(cfg)

Command line overrides

You can update a CfgNode using a list of fully-qualified key, value pairs. This makes it easy to consume override options from the command line. For example:

# Now override from a list (opts could come from the command line)
opts = ["SYSTEM.NUM_GPUS", 8, "TRAIN.SCALES", "[1, 2, 3, 4]"]

The following principle is recommended: "There is only one way to configure the same thing." This principle means that if an option is defined in a TACS config object, then your program should set that configuration option using cfg.merge_from_list(opts) and not by defining, for example, --train-scales as a command line argument that is then used to set cfg.TRAIN.SCALES.

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