pipcs is python configuration system
Project description
PIPCS: PIPCS is Python Configuration System
pipcs is an experimental library to create configuration files for Python.
Installation
pip install pipcs --user
Example Scenario
- In some_program.py:
from dataclasses import field
from typing import Dict, Type, Callable, Union, List, Optional
import torch
import numpy as np
import gym
from pipcs import Config, Choices, Required, required
default_config = Config()
@default_config.add('optimizer')
class OptimizerConfig():
optim_type: Choices[Type[torch.optim.Optimizer]] = Choices([torch.optim.Adam, torch.optim.SGD])
lr: float = 0.001
@default_config.add('environment')
class EnvironmentConfig():
env_id: Required[str] = required
@default_config.add('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 ReinforcementLearning():
def __init__(self, config: Optional[Config] = None):
if config is not None:
self.config = default_config.update(config)
else:
self.config = config
...
print(self.config)
- In user file:
from pipcs import Config
import gym
import torch
from dataclasses import field
from some_program import default_config, ReinforcementLearning
user_config = Config()
@user_config.inherit(default_config.optimizer)
class UserOptimizerConfig():
optim_type = torch.optim.Adam
@user_config.inherit(default_config.environment)
class UserEnvironmentConfig():
env_id = 'CartPole-v1'
@user_config.inherit(default_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 but 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 static type checking tool such asmypy
.
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
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