Simple but maybe too simple config management through python data classes. We use it for machine learning.

Overview

👩‍✈️ Coqpit

CI

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.

🔍 Examples

👉 Simple Coqpit

import os
from dataclasses import asdict, dataclass, field

from coqpit.coqpit import MISSING, Coqpit, check_argument


@dataclass
class SimpleConfig(Coqpit):
    val_a: int = 10
    val_b: int = None
    val_d: float = 10.21
    val_c: str = "Coqpit is great!"
    # mandatory field
    # raise an error when accessing the value if it is not changed. It is a way to define
    val_k: int = MISSING
    # optional field
    val_dict: dict = field(default_factory=lambda: {"val_aa": 10, "val_ss": "This is in a dict."})
    # list of list
    val_listoflist: List[List] = field(default_factory=lambda: [[1, 2], [3, 4]])
    val_listofunion: List[List[Union[str]]] = 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__":
    file_path = 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

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 == config2

👉 argparse handling and parsing.

import 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():
    # 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.parse_args(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()

🤸‍♀️ Merging coqpits

import os
from dataclasses import dataclass
from coqpit.coqpit import Coqpit, check_argument


@dataclass
class CoqpitA(Coqpit):
    val_a: int = 10
    val_b: int = None
    val_d: float = 10.21
    val_c: str = "Coqpit is great!"


@dataclass
class CoqpitB(Coqpit):
    val_d: int = 25
    val_e: int = 257
    val_f: float = -10.21
    val_g: str = "Coqpit is really great!"


if __name__ == '__main__':
    file_path = os.path.dirname(os.path.abspath(__file__))
    coqpita = CoqpitA()
    coqpitb = CoqpitB()
    coqpitb.merge(coqpita)
    print(coqpitb.val_a)
    print(coqpitb.pprint())
Comments
  • Allow file-like objects when saving and loading

    Allow file-like objects when saving and loading

    Allow users to save the configs to arbitrary locations through file-like objects. Would e.g. simplify coqui-ai/TTS#683 without adding an fsspec dependency to this library.

    opened by agrinh 6
  • Latest PR causes an issue when a `Serializable` has default None

    Latest PR causes an issue when a `Serializable` has default None

    https://github.com/coqui-ai/coqpit/blob/5379c810900d61ae19d79b73b03890fa103487dd/coqpit/coqpit.py#L539

    @reuben I am on it but if you have an easy fix go for it. Right now it breaks all the TTS trainings.

    opened by erogol 2
  • [feature request] change the `arg_perfix` of coqpit

    [feature request] change the `arg_perfix` of coqpit

    Is it possible to change the arg_perfix when using Coqpit object to another value / empty string? I see the option is supported in the code by changing arg_perfix, but not sure how to access it using the proposed API.

    Thanks for the package, looks very useful!

    opened by mosheman5 1
  • Setup CI to push new tags to PyPI automatically

    Setup CI to push new tags to PyPI automatically

    I'm gonna add a workflow to automatically upload new tags to PyPI. @erogol when you have a chance could you transfer the coqpit project on PyPI to the coqui user?[0] Then you can add your personal account as a maintainer also, so you don't have to change your local setup.

    In the mean time I'll iterate on testpypi.

    [0] https://pypi.org/user/coqui/

    opened by reuben 1
  • Fix rsetattr

    Fix rsetattr

    rsetattr() is updated to pass the new test cases below.

    I don't know if it is the right solution. It might be that rsetattr confuses when coqpit is used as a prefix.

    opened by erogol 0
  • [feature request] Warning when unexpected key is loaded but not present in class

    [feature request] Warning when unexpected key is loaded but not present in class

    Here is an toy scenario where it would be nice to have a warning

    from dataclasses import dataclass
    from coqpit import Coqpit
    
    @dataclass
    class SimpleConfig(Coqpit):
        val_a: int = 10
        val_b: int = None
    
    if __name__ == "__main__":
        config = SimpleConfig()
    
        tmp_config = config.to_dict()
        tmp_config["unknown_key"] = "Ignored value"
        config.from_dict(tmp_config)
        print(config.to_json())
    

    There the value of config.to_json() is

    {
        "val_a": 10,
        "val_b": null
    }
    

    Which is expected behaviour, but we should get a warning that some keys were ignored (IMO)

    feature request 
    opened by WeberJulian 6
  • [feature request] Add `is_defined`

    [feature request] Add `is_defined`

    Use coqpit.is_defined('field') to check if "field" in coqpit and coqpit.field is not None:

    It is a common condition when you parse out a coqpit object.

    feature request 
    opened by erogol 0
  • Allow grouping of argparse fields according to subclassing

    Allow grouping of argparse fields according to subclassing

    When using inheritance to extend config definitions the resulting ArgumentParser has all fields flattened out. It would be nice to group fields by class and allow some control over ordering.

    opened by reuben 2
Releases(v0.0.17)
Owner
coqui
Coqui, a startup providing open speech tech for everyone 🐸
coqui
Automated moth pictures for biodiversity research

Automated moth pictures for biodiversity research

Ludwig Kürzinger 1 Dec 16, 2021
Zotero references script (and app)

A little script (and PyInstaller build) for a very specific, somewhat hack-ish purpose: managing and exporting project references with Zotero and its API.

Marius Rödder 0 Dec 05, 2021
A lightweight Python module to interact with the Mitre Att&ck Enterprise dataset.

enterpriseattack - Mitre's Enterprise Att&ck A lightweight Python module to interact with the Mitre Att&ck Enterprise dataset. Built to be used in pro

xakepnz 7 Jan 01, 2023
YourX: URL Clusterer With Python

YourX | URL Clusterer Screenshots Instructions for running Install requirements

ARPSyndicate 1 Mar 11, 2022
This is a method to build your own qgis configuration packages using osgeo4W.

This is a method to build your own qgis configuration packages using osgeo4W. Then you can automate deployment in your organization with a controled and trusted environnement.

Régis Haubourg 26 Dec 05, 2022
CupScript is a simple programing language made with python

CupScript CupScript is a simple programming language made with python It includes some basic functions, variables, loops, and some other built in func

FUSEN 23 Dec 29, 2022
Incident Response Process and Playbooks | Goal: Playbooks to be Mapped to MITRE Attack Techniques

PURPOSE OF PROJECT That this project will be created by the SOC/Incident Response Community Develop a Catalog of Incident Response Playbook for every

Austin Songer 987 Jan 02, 2023
一个可以自动生成PTGen,MediaInfo,截图,并且生成发布所需内容的脚本

Differential 差速器 一个可以自动生成PTGen,MediaInfo,截图,并且生成发种所需内容的脚本 为什么叫差速器 差速器是汽车上的一种能使左、右轮胎以不同转速转动的结构。使用同样的动力输入,差速器能够输出不同的转速。就如同这个工具之于PT资源,差速器帮你使用同一份资源,输出不同PT

Lei Shi 96 Dec 15, 2022
A collection of design patterns and idioms in Python (With tests!).

Python Patterns Help the project financially: Donate: https://smartlegion.github.io/donate/ Yandex Money: https://yoomoney.ru/to/4100115206129186 PayP

5 Sep 12, 2021
Run CodeServer on Google Colab using Inlets in less than 60 secs using your own domain.

Inlets Colab Run CodeServer on Colab using Inlets in less than 60 secs using your own domain. Features Optimized for Inlets/InletsPro Use your own Cus

2 Dec 30, 2021
A dot matrix rendered using braille characters.

⣿ dotmatrix A dot matrix rendered using braille characters. Description This library provides class called Matrix which represents a dot matrix that c

Tim Fischer 25 Dec 12, 2022
EDF R&D implementation of ISO 15118-20 FDIS.

EDF R&D implementation of ISO 15118-20 FDIS ============ This project implements the ISO 15118-20 using Python. Supported features: DC Bidirectional P

30 Dec 29, 2022
Learn Python tips, tools, and techniques in around 5 minutes each.

Python shorts Learn Python tips, tools, and techniques in around 5 minutes each. Watch on YouTube Subscribe on YouTube to keep up with all the videos.

Michael Kennedy 28 Jan 01, 2023
Unofficial package for fetching users information based on National ID Number (Tanzania)

Nida Unofficial package for fetching users information based on National ID Number made by kalebu Installation You can install it directly or using pi

Jordan Kalebu 57 Dec 28, 2022
urlwatch is intended to help you watch changes in webpages and get notified of any changes.

urlwatch is intended to help you watch changes in webpages and get notified (via e-mail, in your terminal or through various third party services) of any changes.

Thomas Perl 2.5k Jan 08, 2023
Choice Coin 633 Dec 23, 2022
An open-source Python project series where beginners can contribute and practice coding.

Python Mini Projects A collection of easy Python small projects to help you improve your programming skills. Table Of Contents Aim Of The Project Cont

Leah Nguyen 491 Jan 04, 2023
A python script that fetches the grades of a student from a WAEC result in pdf format.

About waec-result-analyzer A python script that fetches the grades of a student from a WAEC result in pdf format. Built for federal government college

Oshodi Kolapo 2 Dec 04, 2021
CoreSE - basic of social Engineering tool

Core Social Engineering basic of social Engineering tool. just for fun :) About First of all, I must say that I wrote such a project because of my int

Hamed Mohammadvand 7 Jun 10, 2022
This is a working model for which I have used python.

Jarvis_voiceAssistance This is a working model for which I have used python. This model can: 1)Play a video or song on youtube. 2)Tell us time. 3)Tell

Hardik Jain 1 Jan 30, 2022