easy-configer 2.3.2

An easy way for configurating python program by the given config file or config str

Project description

easy_configer version : 2.3.2

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Configeruating the program in an easy-way

I'm willing to provide a light-weight solution for configurating your python program. Hope this repository make every user control their large project more easier ~ ~

Introduction 📝

With the python project go into large-scale, a lot of argument will be required to control the complex business logic, user may need a simple way to load configurations through a file eventually. Their exists various package cover part of function and offer some solution to tackle the mentioned problem.

Unfortunately, I can not find a solution for load & use the argument in simple manner at least. Instead, most of the config-tools seems only works for the specific goal, then cause the code more longer and hard to read.

For example :

## ConfigParser
import ConfigParser 
Config = ConfigParser.ConfigParser()
Config.read("c:\\tomorrow.ini")
# get arg via method
Config.get(section, option)
# or get arg with converter
int(Config['lucky_num'])

## Argparse
import argparse
parse = argparse.ArgumentParser("description string")
parse.add_argument("--lucky_num", type=int)
...
args = parser.parse_args()
args.lucky_num

That leverage me to package my solution for solving this issue. The easy_config will cover the following attributes :

1. Hierachical section config (nested dictionary)

2. Accept multiple config file in dynamic loading manner

3. Support customized class (initialized by keyword arguments)

4. Commend-line update all declared-value wherever it belong, even in hierachical section

5. Support the absl style FLAGS functionality (declare once, use anywhere)

And, of course the following attribute will also be supported :

• dot-access of any arguments (even in nested dictionary)

• inline comment '#', now you can write comment in everyline ~

• support arguments interpolation!!

• support config conversion, which turn easy_config into the other kind of config package (omegaconf, argparse, ..., etc.)

• support hierachical configurating system with dynamic override ~

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Newly update features 🚀

1. Enable all test case (automatically ci by git-action)
2. Support dot-access of any arguments
3. Consistent import syntax..
4. New document is released ~

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Bug Fixed 🐛

Fix-up import syntax.. plz open an issue if you find a bug ~

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Dependencies 🏗️

This package is written for Python 3.8. After refactor in this version, this package also support down to python 3.6!! Of course, light-weight solution do not contain any 3-rd package complex dependencies. The python standard package (such as pathlib, sys, .., etc) is the only source of dependencies, so you don't need to worry about that ~ ~

However, if you want to use the IO_Converter for converting config into omegaconf, you still need to install omegaconf for this functionality ~

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Installation ⚙️

1. pypi install
   simply type the pip install easy_configer (due to name conflict of pypi pkg, we use different pkg name)

2. install from source code
   clone the project from github : git clone repo-link Chage to the root directory of the cloned project, and type pip install -e .

3. import syntax
   Wherever you install, pypi or source. Now, you just need a simple import : from easy_configer.Configer import Configer

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Quick start 🥂

1. Handy example of config file

Let's say we have an easy-config for development enviroment on jupyter notebook. we want to define several variable for configurating a simple math calculation.

# config string
cfg_str = '''
title = 'math calculation'@str
coef = 1e-3@float
with_intercept = True@bool
intercept = 3@int
'''

# look's good, let's get the config!
from easy_configer.Config import Config
cfg = Config(description="math calculation config!", cmd_args=False)
cfg.cfg_from_str(cfg_str)

# oh.. wait, could we do it more easier ?
ez_cfg_str = '''
# opps.. let's change some value
title = 'linear equation'
coef = 15        
'''
# Note : every time you load the config, if you have the same variable name, 
#        it will override the value of the variable!
cfg.cfg_from_str(ez_cfg_str)

lin_equ = lambda x : cfg.coef * x + cfg.intercept if cfg.with_intercept else (cfg.coef * x)
x = 15
print( f"Linear equation with x={x} : { lin_equ(x) }" )

In larger project, we may write a config file to control the program, so that the config will become easy to trace, check and debug. In here, we first prepare an config called test_cfg.ini in the working directory.

For easy-config file, there're two type of argument : flatten argument, hierachical argument. You can see that flatten argument is placed in first shallow level, and the argument could be easily accessed by dot operator. Besides flatten argument, all of hierachical argument will be placed in python dict object, thus accessing each argument by key string!

# ./test_cfg.ini
# '#' denote comment line, the inline comment is also supported!

# define 'flatten' arguments :
serv_host = '127.0.0.1'  
serv_port = 9478@int    # specific type is also allowed!!
api_keys = {'TW_REGION':'SV92VS92N20', 'US_REGION':'W92N8WN029N2'}

# define 'hierachical' arguments :
# the 'section' is the key of accessing dict value and could be defined as follows :
[db_setup]
    db_host = $serv_host
    # first `export mongo_port=5566` in your bash, then support os.env interpolation!
    db_port = $Env.mongo_port  
    snap_shot = True

# and then define second section for backend server..
[bknd_srv]
    load_8bit = True
    async_req = True
    chat_mode = 'inference'
    model_type = 'LlaMa2.0'
    [bknd_srv.mod_params]
        log_hist = False
        tempeture = 1e-4
        model_mode = $bknd_srv.chat_mode  # hierachical args interpolation

Now, we're free to launch the chatbot via python quick_start.py (quick_start.py in work directory)! However, you can also override the arguemnts via commendline python quick_start.py serv_port=7894

import sys

# main_block 
if __name__ == "__main__":
    from easy_configer.Configer import Configer

    cfger = Configer(description="chat-bot configuration", cmd_args=True)
    # we have defined a config file, let's try to load it!
    cfger.cfg_from_ini("./test_cfg.ini")
    
    # Display the Namespace, it will display all flatten arguemnts and first-level sections
    print(cfger)
    
    ... # for building chat-bot instance `Chat_server`
    chat_serv = Chat_server(host=cfger.serv_host, port=cfger.serv_port, api_keys=cfger.api_keys)

    ... # build mongo-db instance `mongo_serv` for logging chat history..
    mongo_serv.init_setup( **cfger.db_setup )

    ... # loading llm model instance `Llama` ~
    llm_mod = Llama(
        ld_8bit=cfger.bknd_srv.load_8bit, 
        chat_mode=cfger.chat_mode, 
        model_type=cfger.model_type
    )

    # you can access nested-dict by dot access ~
    llm_mod.init_mod_param( **cfger.bknd_srv.mod_params )

    # or you can keep the dict fashion ~
    if cfger.bknd_srv['async_req']:
        chat_serv.chat_mod = llm_mod
        chat_serv.hist_db = mongo_serv
    else:
        ... # write sync conversation by yourself..

    sys.exit( chat_serv.server_forever() )

Note that the recommended way to access the argument is still key-string access cfger.args['#4$%-var'], as you may notice, dot-access doesn't support ugly variable name.

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More detail tutorial about each topic is as follows :

2. How to declare hierachical config

There have two kind of way to prepare the arguments in easy-config : we can either define flatten argument or groupping the multiple arguments in an hierachical manner (begin from second level). In most of time, we define the flatten argument as global setup, and arrange the rest of arguments into the corresponding dictionary for easy to assign it to the subroutine.

Let's give a deep-learning example ~

hier_cfg.ini in work directory

glb_seed = 42
exp_id = '0001'

# we call '...' in [...] as section name,
# i.e. we can assign dict dataset to subroutine by `build_dataset(**cfg.dataset)`, just such easy!!
[dataset]   
    service_port = 65536
    path = '/data/kitti'
    # of course, nested dict is also supported! it just the native python dictionary in dictionary!
    [dataset.loader]
        batch_size = 32

[model]
    [model.backbone]
        mod_typ = 'resnet'
        [model.backbone.optimizer]
            lay_seed = 42  

[train_cfg]
    batch_size = 32
    [train_cfg.opt]
        opt_typ = 'Adam'
        lr = 1e-4
        sched = 'cos_anneal'

We have defined the config file, now let's see how to access any agruments! Execute python quick_hier.py in work directory*.

from easy_configer.Configer import Configer

if __name__ == "__main__":
    cfger = Configer(cmd_args=True)
    
    # omit cfg_from_str, hier-config also could be declared in str though ~
    cfger.cfg_from_ini("./hier_cfg.ini")
    
    print(cfger.dataset)  
    # output nested dict : { 'service_port':65536, 'path':'/data/kitti', 'loader':{'batch_size':32} }
    
    print(f"key-string access bz : {cfger.dataset['loader']['batch_size']}")
    # output - "key-string access bz : 32"

    print(f"bz : {cfger.dataset.loader.batch_size}")
    # output - "dot-access bz : 32"

    # we usually conduct initialization such simple & elegant!
    ds = build_dataset(**cfger.dataset)
    mod = build_model(**cfger.model)
    ... # get torch Trainer
    Trainer(mod).fit(ds)

However, the syntax of above config file could be improved, isn't it !? For example, the batch_size is defined twice under dataset.loader and train_cfg, so as layer seed. Moreover, path is defined as python string, it need to be further converted by Path object in python standard package. Could we regist our customized data type for easy-config ?

Glade to say : Yes! it's possible to elegantly deal with above mentioned issue. We can solve the first issue by using argument interpolation, and solve the second issue by using the customized register!!

config interpolation with $ symbol and customized register method regist_cnvtor

Currently we support interpolation mechnaism to interpolate ANY arguemnts belong the different level of nested dictionary. Moreover, we also support $Env for accessing enviroment variables exported in bash!!

# For convience, we define string-config!
def get_str_cfg():
    ''' # `export glb_seed=42` in bash!!
        glb_seed = $Env.glb_seed
        exp_id = '0001'

        [dataset]   
            service_port = 65536

            # Don't forgot to regist Path object first and the typename will be the given name!!
            path = {'path':'/data/kitti'}@pyPath
            
            [dataset.loader]
                batch_size = 32

        [model]
            [model.backbone]
                mod_typ = 'resnet'
                [model.backbone.optimizer]
                    lay_seed = $glb_seed
        
        [train_cfg]
            batch_size = $dataset.loader.batch_size
            [train_cfg.opt]
                opt_typ = 'Adam'
                lr = 1e-4
                sched = 'cos_anneal'
    '''

# main_block 
if __name__ == "__main__":
    from pathlib import Path

    cfger = Configer(description="sample for arguments interpolation")
    cfger.regist_cnvtor("pyPath", Path)  # regist customer class 'Path'

    cfg_str = get_str_cfg()
    cfger.cfg_from_str(cfg_str)
    # do whatever you want to do!

3. Access all arguments flexibly

We simple set a breakpoint to feel how flexible does easy_configer.utils.Container.AttributeDict support.

from easy_configer.Configer import Configer

if __name__ == "__main__":
    cfger = Configer()
    cfger.cfg_from_ini("./hier_cfg.ini")
    breakpoint()

We write a special example hier_cfg.ini!! # nested-dict [secA] # test depth ((sub^4)-section under secA) lev = 1 [secA.secB] lev = 2 [secA.secB.secC] lev = 3 [secA.secB.secC.secD] lev = 4

Now you can access each lev :

1. (pdb) cfger.secA.lev , output lev : 1
2. (pdb) cfger['secA'].secB['lev'] , output lev : 2, and so on..
3. Most crazy one ~ (pdb) cfger.secA.['secB'].secC['secD'].lev , output lev : 4

4. Commmend-line Support

We also take hier_cfg.ini as example!

# hier_cfg.ini
glb_var = 42@int
[dataset]         
    ds_type = None
    path = {'root':'/data/kitti'}@Path
    [dataset.loader]
        batch_size = 32@int

# Hier-Cell cfg written by Josef-Huang..

Execute python program and print out the helper information
python quick_hier.py -h

Update flatten argument and print out the helper information
python quick_hier.py glb_var=404 -h

Especially update non-flatten argument, you can access any argument at any level by dot-access in commend-line!! (with combining any argument update). Now, try to change any nested argument
python quick_hier.py dataset.ds_type="'kitti'" dataset.path="{'path':'/root/ds'}" dataset.loader.batch_size=48

( Note that the commendline declaration for string is tricky, but currently we only support two way for that : dataset.ds_type="'kitti'" or dataset.ds_type=kitti@str, pick up one of you like ~ )

5. Import Sub-Config

Like omegaconf, most of user expect to seperate the config based on their type and dynamically merge it in runtime. It's a rational requirement and the previous version of easy-config provide two way to conduct it, but both have it's limit :

1. you can call the cfg_from_ini twice, for example, cfg.cfg_from_ini('./base_cfg') ; cfg.cfg_from_ini('./override_cfg'). But it's not explicitly load the config thus reducing readability.
2. you can use the config merging, for example, new_cfg = base_cfg | override_cfg. But it's not elegant solution while you have to merge several config..

Now, we provide the thrid way : sub-config. you can import the sub-config in any depth of hierachical config by simply placing the > symbol at the beginning of line.

# ./base_cfg.ini
glb_seed = 42@int
[dataset]         
    > ./config/ds_config.ini

[model]
    > ./root/config/model_config.ini

# ./config/ds_config.ini
ds_type = None
path = {'root':'/data/kitti'}@Path
[dataset.loader]
    batch_size = 32@int

# ./root/config/model_config.ini
[model.backbone]
    mod_typ = 'resnet'
    [model.backbone.optimizer]
    # and yes, interpolation is still valid "after" the reference argument is declared!
        lay_seed = $glb_seed  

6. Config Operation

Config operation is one of the core technique for dynamic configuration system!! In the following example, you can see that the merging config system already provided a impressive hierachical merging funtionality!

For example, ghyu.opop.add in cfg_a can be replaced by the cfg_b in same section with the same variable name, while the different namespace keep their variable safely ~ so the value of ghyu.opop.add will be 67 and ghyu.opop.tueo.inpo refer the flatten variable inpo and the value will be 46.

from easy_configer.Configer import Configer

def build_cfg_text_a():
    return '''
    # Initial config file :
    inpo = 46@int
    [test]         
        mrg_var_tst = [1, 3, 5]@list
        [test.ggap]
            gtgt = haha@str

    [ghyu]
        [ghyu.opop]
            add = 32@int
            [ghyu.opop.tueo]
                salt = $inpo

    # Cell cfg written by Josef-Huang..
    '''

def build_cfg_text_b():
    return '''
    # Initial config file :
    inop = 32@int
    [test]         
        mrg_var_tst = [1, 3, 5]@list
        [test.ggap]
            gtgt = overrides@str
            [test.ggap.conf]
                secert = 42@int

    [ghyu]
        [ghyu.opop]
            add = 67@int
            div = 1e-4@float

    [new]
        [new.new]
            newsec = wpeo@str
    # Cell cfg written by Josef-Huang..
    '''

if __name__ == "__main__":
    cfg_a = Configer(cmd_args=True)
    cfg_a.cfg_from_str(build_cfg_text_a())  
    

    cfg_b = Configer()
    cfg_b.cfg_from_str(build_cfg_text_b())
    
    # default, override falg is turn off ~
    cfg_a.merge_conf(cfg_b, override=True)

    # `cfg_b = cfg_b | cfg_a`, operator support, warn to decrease the read-ability...
    # cfg_a will override the argument of cfg_b which share the identitical variable name in cfg_b!
    # operator support : `cfg_b |= cfg_a` == `cfg_b = cfg_b | cfg_a`

---

Miscellnous features

7. IO Converter

from dataclasses import dataclass
from typing import Optional

@dataclass
class TableConfig:
    rows: int = 1

@dataclass
class DatabaseConfig:
    table_cfg: TableConfig = TableConfig()

@dataclass
class ModelConfig:
    data_source: Optional[TableConfig] = None

@dataclass
class ServerConfig:
    db: DatabaseConfig = DatabaseConfig()
    model: ModelConfig = ModelConfig()

if __name__ == '__main__':
    from easy_configer.IO_Converter import IO_Converter

    # first import the IO_converter
    from easy_config.IO_Converter import IO_Converter
    cnvt = IO_Converter()

    # convert easy_config instance into the argparse instance
    argp_cfg = cnvt.cnvt_cfg_to(cfger, 'argparse')

    uargp_cfg = cnvt.cnvt_cfg_to(cfger, 'argparse', parse_arg=False)
    argp_cfg = uargp_cfg.parse_args()

    ## convert config INTO..
    # convert easy_config instance into the omegaconf instance
    ome_cfg = cnvt.cnvt_cfg_to(cfger, 'omegaconf')

    # convert easy_config instance into the "yaml string"
    yaml_cfg = cnvt.cnvt_cfg_to(cfger, 'yaml')

    # convert easy_config instance into the "dict"
    yaml_cfg = cnvt.cnvt_cfg_to(cfger, 'dict')

    ## convert into easy-config FROM..
    # argparse, omegaconf, yaml, dict ... is supported
    ez_cfg = cnvt.cnvt_cfg_from(argp_cfg, 'omegaconf')

    # Especially, it support "dataclass"!
    ds_cfg = ServerConfig()
    ez_cfg = cnvt.cnvt_cfg_from(ds_cfg, 'dataclass')

8. Absl style flag

easy_config also support that you can access the 'same' config file in different python file without re-declare the config. test_flag.py under the same work directory

Suppose you have executed main.py: from easy_configer.Configer import Configer from utils import get_var_from_flag

if __name__ == "__main__":
    cfg = Configer()
    cfg.cfg_from_str("var = 32")

    # both should output 32 ~
    print(f"var from main : {cfg.var}")
    print(f"var from flag : { get_var_from_flag() }")

Now, when you step in get_var_from_flag function in different file.. from easy_configer.Configer import Configer

def get_var_from_flag():
    new_cfger = Configer()
    flag = new_cfger.get_cfg_flag()
    # test to get the pre-defined 'var'
    return flag.var

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The documentation of easy_configer is also released in read doc 🔗

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Simple Unittest 🧪

If you clone this repo and built from source, you can try to run the unittest. python -m unittest discover

I have placed all test file under test folder.

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License

MIT License. More information of each term, please see LICENSE.md

Author

Josef-Huang, [e-mail]

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