Simple, light-weight config handling through python data classes with to/from JSON serialization/deserialization.
Work in progress... 🌡️
What I need from a ML configuration library are
What I need from a ML configuration library are...
- Fixing a general config schema in Python to guide users about expected values. Python is good but not universal. Sometimes you train a ML model and use it on a different platform. So, you need your model configuration file importable by other programming languages.
- Simple dynamic value and type checking with default values. If you are a beginner in a ML project, it is hard to guess the right values for your ML experiment. Therefore it is important to have some default values and know what range and type of input are expected for each field.
- Ability to decompose large configs. As you define more fields for the training dataset, data preprocessing, model parameters, etc., your config file tends to get quite large but in most cases, they can be decomposed, enabling flexibility and readability. Inheritance and nested configurations. Simply helps to keep configurations consistent and easier to maintain.
- Ability to override values from the command line when necessary. For instance, you might need to define a path for your dataset, and this changes for almost every run. Then the user should be able to override this value easily over the command line arguments.
- No dependencies You don't want to install a ton of libraries for simple configuration management. If you install one, then it is better to be just pure native python and as simple as possible.
import os
from dataclasses import asdict, dataclass, field
from coqpit import Coqpit, check_argument
from typing import List, Union
@dataclass
class SimpleConfig(Coqpit):
val_a: int = 10
val_b: int = None
val_c: str = "Coqpit is great!"
def check_values(self,):
'''Check config fields'''
c = asdict(self)
check_argument('val_a', c, restricted=True, min_val=10, max_val=2056)
check_argument('val_b', c, restricted=True, min_val=128, max_val=4058, allow_none=True)
check_argument('val_c', c, restricted=True)
@dataclass
class NestedConfig(Coqpit):
val_d: int = 10
val_e: int = None
val_f: str = "Coqpit is great!"
sc_list: List[SimpleConfig] = None
sc: SimpleConfig = SimpleConfig()
union_var: Union[List[SimpleConfig], SimpleConfig] = field(default_factory=lambda: [SimpleConfig(),SimpleConfig()])
def check_values(self,):
'''Check config fields'''
c = asdict(self)
check_argument('val_d', c, restricted=True, min_val=10, max_val=2056)
check_argument('val_e', c, restricted=True, min_val=128, max_val=4058, allow_none=True)
check_argument('val_f', c, restricted=True)
check_argument('sc_list', c, restricted=True, allow_none=True)
check_argument('sc', c, restricted=True, allow_none=True)
if __name__ == '__main__':
file_path = os.path.dirname(os.path.abspath(__file__))
# init 🐸 dataclass
config = NestedConfig()
# save to a json file
config.save_json(os.path.join(file_path, 'example_config.json'))
# load a json file
config2 = NestedConfig(val_d=None, val_e=500, val_f=None, sc_list=None, sc=None, union_var=None)
# update the config with the json file.
config2.load_json(os.path.join(file_path, 'example_config.json'))
# now they should be having the same values.
assert config == config2
# pretty print the dataclass
print(config.pprint())
# export values to a dict
config_dict = config.to_dict()
# crate a new config with different values than the defaults
config2 = NestedConfig(val_d=None, val_e=500, val_f=None, sc_list=None, sc=None, union_var=None)
# update the config with the exported valuess from the previous config.
config2.from_dict(config_dict)
# now they should be having the same values.
assert config == config2import argparse
import os
from dataclasses import asdict, dataclass, field
from typing import List
from coqpit.coqpit import Coqpit, check_argument
import sys
@dataclass
class SimplerConfig(Coqpit):
val_a: int = field(default=None, metadata={'help': 'this is val_a'})
@dataclass
class SimpleConfig(Coqpit):
val_a: int = field(default=10,
metadata={'help': 'this is val_a of SimpleConfig'})
val_b: int = field(default=None, metadata={'help': 'this is val_b'})
val_c: str = "Coqpit is great!"
mylist_with_default: List[SimplerConfig] = field(
default_factory=lambda:
[SimplerConfig(val_a=100),
SimplerConfig(val_a=999)],
metadata={'help': 'list of SimplerConfig'})
# mylist_without_default: List[SimplerConfig] = field(default=None, metadata={'help': 'list of SimplerConfig'}) # NOT SUPPORTED YET!
def check_values(self, ):
'''Check config fields'''
c = asdict(self)
check_argument('val_a', c, restricted=True, min_val=10, max_val=2056)
check_argument('val_b',
c,
restricted=True,
min_val=128,
max_val=4058,
allow_none=True)
check_argument('val_c', c, restricted=True)
def main():
file_path = os.path.dirname(os.path.abspath(__file__))
# initial config
config = SimpleConfig()
print(config.pprint())
# reference config that we like to match with the config above
config_ref = SimpleConfig(val_a=222,
val_b=999,
val_c='this is different',
mylist_with_default=[
SimplerConfig(val_a=222),
SimplerConfig(val_a=111)
])
# create and init argparser with Coqpit
parser = argparse.ArgumentParser()
parser = config.init_argparse(parser)
parser.print_help()
args = parser.parse_args()
# parse the argsparser
config.from_argparse(args)
config.pprint()
# check the current config with the reference config
assert config == config_ref
if __name__ == '__main__':
sys.argv.extend(['--coqpit.val_a', '222'])
sys.argv.extend(['--coqpit.val_b', '999'])
sys.argv.extend(['--coqpit.val_c', 'this is different'])
sys.argv.extend(['--coqpit.mylist_with_default.0.val_a', '222'])
sys.argv.extend(['--coqpit.mylist_with_default.1.val_a', '111'])
main()