Package: tacs
TACSstrmethod is not supported yet.IntroductionTACS 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 paramountand 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-rcnnandDetectron.UsageTACS can be used in a variety of flexible ways. There are two main
paradigms:Configuration aslocal variableConfiguration as aglobal singletonIt'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 calledconfig.pyordefaults.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.pyfromtacs.configimportCfgNodeasCN_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)defget_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 patternreturn_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 thefreeze()method. As
illustrated below, the config options can either be used a global
set of options by importingcfgand accessing it directly, or
thecfgcan be copied and passed as an argument.# my_project/main.pyimportmy_projectfromconfigimportget_cfg_defaults# local variable usage pattern, or:# from config import cfg  # global singleton usage patternif__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 optionsifcfg.SYSTEM.NUM_GPUS>0:my_project.setup_multi_gpu_support()model=my_project.create_model(cfg)Command line overridesYou can update aCfgNodeusing 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 usingcfg.merge_from_list(opts)and
not by defining, for example,--train-scalesas a command line
argument that is then used to setcfg.TRAIN.SCALES.