PIPCS: PIPCS is Python Configuration System

pipcs is an experimental library to create configuration files for Python.

Installation

pip install pipcs --user

Documentation

https://pipcs.readthedocs.io/

Example

https://github.com/goktug97/nes-torch/blob/master/nes/config.py

Example Scenario

• In some_program.py:

from dataclasses import field
from typing import Dict, Type, Callable, Union, List

import torch
import numpy as np
import gym

from pipcs import Config, Choices, Condition, Required, required

default_config = Config()

@default_config('optimizer')
class OptimizerConfig():
    optim_type: Choices[Type[torch.optim.Optimizer]] = Choices([torch.optim.Adam, torch.optim.SGD], default=torch.optim.Adam)
    weight_decay: float = 0.0
    lr: float = 0.001
    betas: Condition[Tuple[float, float]] = Condition((0.9, 0.999), optim_type == torch.optim.Adam)
    eps: Condition[float] = Condition(1e-08, optim_type == torch.optim.Adam)
    momentum: Condition[float] = Condition(0.0, optim_type == torch.optim.SGD)
    dampening: Condition[float] = Condition(0.0, optim_type == torch.optim.SGD)

@default_config('environment')
class EnvironmentConfig():
    env_id: Required[str] = required

@default_config('policy')
class PolicyConfig():
    input_size: Required[int] = required
    hidden_layers: List[int] = field(default_factory=lambda: [])
    output_size: Required[int] = required
    output_func: Required[Callable[[torch.Tensor], Union[int, np.ndarray]]] = required
    activation: torch.nn.Module = torch.nn.ReLU

class Policy(torch.nn.Module):
    def __init__(self, input_size, hidden_layers, output_size, activation, output_func):
        super().__init__()
        self.seq = torch.nn.Sequential(
            torch.nn.Linear(input_size, 64),
            activation(),
            torch.nn.Linear(64, 64),
            activation(),
            torch.nn.Linear(64, output_size))

class ReinforcementLearning():
    def __init__(self, config: Config = default_config):
        self.config = config
        self.policy = Policy(**config.policy.to_dict())
        self.optim = self.make_optimizer(parameters=self.policy.parameters(), **config.optimizer.to_dict())
        self.env = gym.make(config.environment.env_id)

    def make_optimizer(self, optim_type, parameters, **kwargs):
        return optim_type(parameters, **kwargs)

• In user file:

from pipcs import Config, Condition

import gym
import torch
from dataclasses import field

from some_program import default_config, ReinforcementLearning

user_config = Config(default_config)

@user_config('optimizer')
class UserOptimizerConfig():
    optim_type = torch.optim.Adam
    # Users can add their own variables too
    amsgrad: Condition[bool] = Condition(False, default_config.optimizer.optim_type == torch.optim.Adam)
    nesterov: Condition[bool] = Condition(False, default_config.optimizer.optim_type == torch.optim.SGD)

@user_config('environment')
class UserEnvironmentConfig():
    env_id = 'CartPole-v1'

@user_config('policy')
class UserPolicyConfig():
    env = gym.make(user_config.environment.env_id)
    input_size = env.observation_space.shape[0]
    hidden_layers = field(default_factory=lambda: [64, 32])
    if isinstance(env.action_space, gym.spaces.Discrete):
        output_size = env.action_space.n
        output_func = lambda x: x.argmax().item()
    else:
        output_size = env.action_space.shape[0]
        output_func = lambda x: x.detach().numpy()

ReinforcementLearning(user_config)

• Note: If a config is not inherited, typing is necessary. Also, if you are adding your own variable to the inherited config and want it to be register, you need to specify the type. Putting the correct type is not necessary. 'typing.Any' can be used if you don't want to bother with typing but they are important if you are using a static type checking tool such as mypy.

Accessing Variables

>>> from pipcs import Config
>>> 
>>> config = Config()
>>> 
>>> @config.add('configuration')
... class Foo():
...     bar: str = 'bar'
...     baz: int = 1
... 
>>> print(config.configuration.bar)
bar
>>> print(config.configuration.baz)
1
>>> print(config['configuration']['bar'])
bar