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
GitHub saver for stargazers, forks, repos

GitHub backup repositories Save your repos and list of stargazers & list of forks for them. Pure python3 and git with no dependencies to install. GitH

Alexander Kapitanov 23 Aug 21, 2022
A desktop app to check the unlocked courses bases on previously done courses.

Course Picker A desktop app to check the unlocked courses bases on previously done courses. Table of contents About the Project Built with What it doe

Ahmed Symum Swapno 3 Feb 07, 2022
Synchrosqueezing, wavelet transforms, and time-frequency analysis in Python

Synchrosqueezing is a powerful reassignment method that focuses time-frequency representations, and allows extraction of instantaneous amplitudes and frequencies

John Muradeli 382 Jan 06, 2023
Manjaro CN Repository

Manjaro CN Repository Automatically built packages based on archlinuxcn/repo and manjarocn/docker. Install Add manjarocn to /etc/pacman.conf: Please m

Manjaro CN 28 Jun 26, 2022
My Analysis of the VC4 Assembly Code from the RPI4

My Analysis of the VC4 Assembly Code from the RPI4

Nicholas Starke 31 Jul 13, 2022
An upgraded version of extractJS

extractJS_2.0 An enhanced version of extractJS with even more functionality Features Discover JavaScript files directly from the webpage Customizable

Ali 4 Dec 21, 2022
A tool for fixing inconsistent timestamp metadata (atime, ctime, and mtime).

Mtime Fixer Mtime Fixer is a tool for fixing inconsistent timestamp metadata (atime, ctime, and mtime). Sometimes timestamp metadata of folders are in

Halit Şimşek 2 Jan 11, 2022
Path of Exile Vendor Recipe Tracker (Chaos/Regal orb)

Path of Exile Vendor Trade Tracker Are you tired of manually keeping track of collected and missing items for farming Chaos or Regal Orbs in PoE? Me t

1 Nov 09, 2021
Домашние задания, выполненные на 3ем семестре РТУ МИРЭА, по дисциплине

ДЗ по курсу "Конфигурационное управление" в РТУ МИРЭА Описание В данном репозитории находятся домашние задания, выполненные на 3ем семестре РТУ МИРЭА,

Semyon Esaev 4 Dec 22, 2022
The program calculates the BMI of people

Programmieren Einleitung: Das Programm berechnet den BMI von Menschen. Es ist sehr einfach zu handhaben, so können alle Menschen ihren BMI berechnen.

2 Dec 16, 2021
A replacement of qsreplace, accepts URLs as standard input, replaces all query string values with user-supplied values and stdout.

Bhedak A replacement of qsreplace, accepts URLs as standard input, replaces all query string values with user-supplied values and stdout. Works on eve

Eshan Singh 84 Dec 31, 2022
京东自动入会获取京豆

京东入会领京豆 要求 有一定的电脑知识 or 有耐心爱折腾 需要Chrome(推荐)、Edge(Chromium)、Firefox 操作系统需是Mac(本人没在m1上测试)、Linux(在deepin上测试过)、Windows 安装方法 脚本采用Selenium遍历京东入会有礼界面,由于遍历了200

Vanke Anton 500 Dec 22, 2022
Extend the maya channel box with searchability and colour

channel-box-plus will add search-ability over its attributes, and it will colour user defined attributes, making them easier to distinguish.

Robert Joosten 12 Jun 08, 2022
The CS Netlogo Helper is a small python script I made, to make computer science homework easier.

The CS Netlogo Helper is a small python script I made, to make computer science homework easier. This project is really ironic now that I think about it.

1 Jan 13, 2022
The Ultimate Widevine Content Ripper (KEY Extract + Download + Decrypt) is REBORN

NARROWVINE-REBORN ** UPDATE 21.12.01 ** As expected Google patched its ChromeCDM Whitebox exploit by Satsuoni with a force-update on the ChromeCDM. Th

Vank0n 104 Dec 07, 2022
A lighweight screen color picker tool

tkpick A lighweigt screen color picker tool Availability Only GNU/Linux 🐧 Installing Install via pip (No auto-update): [sudo] pip install tkpick Usa

Adil Gürbüz 7 Aug 30, 2021
Object-oriented programming exercise session held in Petnica.

OOP vežba ⚠️ The code in this repo is used for a OOP practice session held in Petnica. All instructions in the README file are written in Serbian. Ops

Pavle Ćirić 1 Jan 30, 2022
Painel simples com consulta de cep,CNPJ,placa e ip

Painel mpm Um painel simples com consultas de IP, CNPJ, CEP e PLACA Início 🌐 apt update && apt upgrade -y pkg i python git pip install requests Insta

8 Feb 27, 2022
Consulta cpf fds

Consulta-cpf Consulta cpf fds Instalação: apt-get update -y

Moleey 1 Nov 24, 2021
This is a library which aiming to save all my code about cpp. It will help me to code conveniently.

This is a library which aiming to save all my code about cpp. It will help me to code conveniently.

Paul Leo 21 Dec 06, 2021