👩‍✈️ Coqpit

Simple, light-weight and no dependency config handling through python data classes with to/from JSON serialization/deserialization.

Currently it is being used by 🐸TTS.

❔ Why I need this

What I need from a ML configuration library...

1. 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.

2. 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.

3. 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.

4. Inheritance and nested configurations.

   Simply helps to keep configurations consistent and easier to maintain.

5. 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.

   It also allows easy hyper-parameter search without changing your original code. Basically, you can run different models with different parameters just using command line arguments.

6. Defining dynamic or conditional config values.

   Sometimes you need to define certain values depending on the other values. Using python helps to define the underlying logic for such config values.

7. No dependencies

   You don't want to install a ton of libraries for just configuration management. If you install one, then it is better to be just native python.

🚫 Limitations

• Union type dataclass fields cannot be parsed from console arguments due to the type ambiguity.
• JSON is the only supported serialization format, although the others can be easily integrated.
• Listtype with multiple item type annotations are not supported. (e.g. List[int, str]).
• dict fields are parsed from console arguments as JSON str without type checking. (e.g --val_dict '{"a":10, "b":100}').
• MISSING fields cannot be avoided when parsing console arguments.

🔍 Examples

👉 Simple Coqpit

```python import os from dataclasses import asdict, dataclass, field from typing import List, Union from coqpit import MISSING, Coqpit, check_argument

@dataclass class SimpleConfig(Coqpit): vala: int = 10 valb: int = None vald: float = 10.21 valc: str = "Coqpit is great!" # mandatory field # raise an error when accessing the value if it is not changed. It is a way to define valk: int = MISSING # optional field valdict: dict = field(defaultfactory=lambda: {"valaa": 10, "valss": "This is in a dict."}) # list of list vallistoflist: List[List] = field(defaultfactory=lambda: [[1, 2], [3, 4]]) vallistofunion: List[List[Union[str,int]]] = field(default_factory=lambda: [[1, 3], [1, "Hi!"]])

def check_values(
    self,
):  # you can define explicit constraints on the fields using `check_argument()`
    """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)

if name == "main": filepath = os.path.dirname(os.path.abspath(file_)) config = SimpleConfig()

# try MISSING class argument
try:
    k = config.val_k
except AttributeError:
    print(" val_k needs a different value before accessing it.")
config.val_k = 1000

# try serialization and deserialization
print(config.serialize())
print(config.to_json())
config.save_json(os.path.join(file_path, "example_config.json"))
config.load_json(os.path.join(file_path, "example_config.json"))
print(config.pprint())

# try `dict` interface
print(*config)
print(dict(**config))

# value assignment by mapping
config["val_a"] = -999
print(config["val_a"])
assert config.val_a == -999

```

👉 Serialization

```python import os from dataclasses import asdict, dataclass, field from coqpit import Coqpit, check_argument from typing import List, Union

@dataclass class SimpleConfig(Coqpit): vala: int = 10 valb: 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): vald: int = 10 vale: int = None valf: str = "Coqpit is great!" sclist: List[SimpleConfig] = None sc: SimpleConfig = SimpleConfig() unionvar: Union[List[SimpleConfig], SimpleConfig] = field(defaultfactory=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': filepath = 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 == config2

```

👉 argparse handling and parsing.

```python import argparse import os from dataclasses import asdict, dataclass, field from typing import List

from coqpit import Coqpit, check_argument import sys

@dataclass class SimplerConfig(Coqpit): vala: int = field(default=None, metadata={'help': 'this is vala'})

@dataclass class SimpleConfig(Coqpit): valreq: str # required field vala: int = field(default=10, metadata={'help': 'this is vala of SimpleConfig'}) valb: int = field(default=None, metadata={'help': 'this is valb'}) nestedconfig: SimplerConfig = SimplerConfig() mylistwithdefault: List[SimplerConfig] = field( defaultfactory=lambda: [SimplerConfig(vala=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_req', c, restricted=True)

def main(): # reference config that we like to match with the one parsed from argparse configref = SimpleConfig(valreq='this is different', vala=222, valb=999, nestedconfig=SimplerConfig(vala=333), mylistwithdefault=[ SimplerConfig(vala=222), SimplerConfig(vala=111) ])

# create new config object from CLI inputs
parsed = SimpleConfig.init_from_argparse()
parsed.pprint()

# check the parsed config with the reference config
assert parsed == config_ref

if name == 'main': sys.argv.extend(['--coqpit.valreq', 'this is different']) sys.argv.extend(['--coqpit.vala', '222']) sys.argv.extend(['--coqpit.valb', '999']) sys.argv.extend(['--coqpit.nestedconfig.vala', '333']) sys.argv.extend(['--coqpit.mylistwithdefault.0.vala', '222']) sys.argv.extend(['--coqpit.mylistwithdefault.1.val_a', '111']) main() ```

🤸‍♀️ Merging coqpits

```python import os from dataclasses import dataclass from coqpit import Coqpit, check_argument

@dataclass class CoqpitA(Coqpit): vala: int = 10 valb: int = None vald: float = 10.21 valc: str = "Coqpit is great!"

@dataclass class CoqpitB(Coqpit): vald: int = 25 vale: int = 257 valf: float = -10.21 valg: str = "Coqpit is really great!"

if name == 'main': filepath = os.path.dirname(os.path.abspath(file)) coqpita = CoqpitA() coqpitb = CoqpitB() coqpitb.merge(coqpita) print(coqpitb.vala) print(coqpitb.pprint()) ```

Development

Install the pre-commit hook to automatically check your commits for style and hinting issues:

bash $ python .pre-commit-2.12.1.pyz install