TOML yet Another Configuration System
str method is not supported yet.
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 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
defaults.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.
# my_project/config.py from tacs.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) 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 [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
cfg can be copied and passed as an argument.
# my_project/main.py 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() cfg.merge_from_file("experiment.toml") cfg.freeze() print(cfg) # Example of using the cfg as global access to options if cfg.SYSTEM.NUM_GPUS > 0: my_project.setup_multi_gpu_support() 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:
cfg.merge_from_file("experiment.toml") # Now override from a list (opts could come from the command line) opts = ["SYSTEM.NUM_GPUS", 8, "TRAIN.SCALES", "[1, 2, 3, 4]"] cfg.merge_from_list(opts)
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
not by defining, for example,
--train-scales as a command line
argument that is then used to set
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