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effortless-config-0.7.0
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Globally scoped configuration with argparse integration
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | effortless-config-0.7.0 |
| نام | effortless-config |
| نسخه کتابخانه | 0.7.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/andreasjansson/effortless-config |
| آدرس اینترنتی | https://pypi.org/project/effortless-config/ |
| مجوز | - |
# Effortless Config
[](https://badge.fury.io/py/effortless-config) [](https://circleci.com/gh/andreasjansson/effortless-config/tree/master)
_Globally scoped configuration with argparse integration._
## Installation
```
pip install effortless-config
```
## Rationale
1. When building machine learning models, I often find myself with a file named `config.py` that has a bunch of global variables that I reference throughout the codebase.
2. As the work progresses, I end up with _groups_ of specific configuration settings that correspond to specific experiments.
3. I want to be able to select configuration group on the command line when I start an experiment.
4. And I want to be able to override certain settings within that experiment, from the command line.
## Basic usage
First, define a class that extends `effortless_config.Config` and specify your default configuration values inside it:
```python
from effortless_config import Config
class config(Config): # lowercase "c" optional
SOME_INTEGER_SETTING = 10
FLOAT_SETTING = 0.5
A_BOOLEAN = False
MY_STRING_SETTING = 'foo'
```
Then you can refer to these settings elsewhere in your code base:
```python
def main():
print(f'SOME_INTEGER_SETTING is {config.SOME_INTEGER_SETTING}')
print(f'FLOAT_SETTING is {config.FLOAT_SETTING}')
print(f'A_BOOLEAN is {config.A_BOOLEAN}')
print(f'MY_STRING_SETTING is {config.MY_STRING_SETTING}')
```
The function `config.parse_args()` creates an [argparse](https://docs.python.org/3/library/argparse.html) parser from your configuration. If we put the two previous code snippets in a file called `basic_example.py` and finish it off with
```python
if __name__ == '__main__':
config.parse_args()
main()
```
...we can see the available settings with `-h/--help`:
```console
$ python basic_example.py --help
usage: basic_example.py [-h] [--some-integer-setting SOME_INTEGER_SETTING]
[--float-setting FLOAT_SETTING] [--a-boolean {true,false}]
[--my-string-setting MY_STRING_SETTING]
optional arguments:
-h, --help show this help message and exit
--some-integer-setting SOME_INTEGER_SETTING
--float-setting FLOAT_SETTING
--a-boolean {true,false}
--my-string-setting MY_STRING_SETTING
```
We can then override configuration settings from the command line:
```console
$ python basic_example.py
SOME_INTEGER_SETTING is 10
FLOAT_SETTING is 0.5
A_BOOLEAN is False
MY_STRING_SETTING is foo
$ python basic_example.py --some-integer-setting 1000
SOME_INTEGER_SETTING is 1000
FLOAT_SETTING is 0.5
A_BOOLEAN is False
MY_STRING_SETTING is foo
```
List and dict type settings can be overridden by passing json strings from the command line, e.g.
```console
$ python lists_and_dicts.py --my-list='[1, 2, 3]' --my-dict='{"foo": 10, "bar": 20}'
```
## Using _groups_
Configuration _groups_ are families of settings that you want to tie together. For example, you might have some machine learning problem for which you have a large model and a small model, and you want to easily switch between the two models without having to specify all parameters on the command line every time.
To specify groups, use the `settings(...)` function:
```python
from effortless_config import Config, setting
class config(Config):
groups = ['large', 'small']
NUM_LAYERS = setting(default=5, large=10, small=3)
NUM_UNITS = setting(default=128, large=512, small=32)
USE_SKIP_CONNECTIONS = setting(default=True, small=False)
LEARNING_RATE = 0.1
OPTIMIZER = 'adam'
```
`setting(...)` has the signature:
```python
def setting(default: T, **kwargs: T) -> T
```
...where `T` is `Union[int, float, str, bool, list, dict, NoneType]` and `kwargs` is a map from group names to values. Specifying parameters by value is shorthand for a `setting` with no groups, i.e. `SOME_KEY = 'value'` is equivalent to `SOME_KEY = setting(default='value')`.
When using groups you must first define the group names using the `config.groups` list.
Then in your code you can use these settings like in the basic example
```python
def main():
print(f'NUM_LAYERS is {config.NUM_LAYERS}')
print(f'NUM_UNITS is {config.NUM_UNITS}')
print(f'USE_SKIP_CONNECTIONS is {config.USE_SKIP_CONNECTIONS}')
print(f'LEARNING_RATE is {config.LEARNING_RATE}')
print(f'OPTIMIZER is {config.OPTIMIZER}')
if __name__ == '__main__':
config.parse_args()
main()
```
Now we see an additional `--configuration` option when we ask for `--help`:
```console
$ python group_example.py --help
usage: group_example.py [-h] [--configuration {default,large,small}]
[--num-layers NUM_LAYERS] [--num-units NUM_UNITS]
[--use-skip-connections {true,false}]
[--learning-rate LEARNING_RATE] [--optimizer OPTIMIZER]
optional arguments:
-h, --help show this help message and exit
--configuration {default,large,small}, -c {default,large,small}
--num-layers NUM_LAYERS
--num-units NUM_UNITS
--use-skip-connections {true,false}
--learning-rate LEARNING_RATE
--optimizer OPTIMIZER
```
The `--configuration` option specifies the configuration group, and defaults to `default` if omitted.
For example:
```console
$ python group_example.py
NUM_LAYERS is 5
NUM_UNITS is 128
USE_SKIP_CONNECTIONS is True
LEARNING_RATE is 0.1
OPTIMIZER is adam
$ python group_example.py --configuration large
NUM_LAYERS is 10
NUM_UNITS is 512
USE_SKIP_CONNECTIONS is True
LEARNING_RATE is 0.1
OPTIMIZER is adam
$ python group_example.py --configuration small
NUM_LAYERS is 3
NUM_UNITS is 32
USE_SKIP_CONNECTIONS is False
LEARNING_RATE is 0.1
OPTIMIZER is adam
```
We can also override individual settings in conjunction with groups. Individual settings take precedence over the group setting:
```console
$ python group_example.py --configuration large --num-units 768
NUM_LAYERS is 10
NUM_UNITS is 768
USE_SKIP_CONNECTIONS is True
LEARNING_RATE is 0.1
OPTIMIZER is adam
```
## Testing
When writing tests, you can use the `config.override` context manager to override individual settings:
```python
import pytest
from .config import config
def test_with_context_manager():
with config.override(FLOAT_SETTING=0.8, A_BOOLEAN=True):
assert config.FLOAT_SETTING * config.SOME_INTEGER_SETTING == 8
assert config.A_BOOLEAN is True
```
The `config.override` method can also be used without context management in conjunction with `config.reset_to_defaults`:
```python
def test_with_manual_reset():
config.override(FLOAT_SETTING=0.8, A_BOOLEAN=True)
assert config.FLOAT_SETTING * config.SOME_INTEGER_SETTING == 8
assert config.A_BOOLEAN is True
config.reset_to_defaults()
```
---
Btw, this has nothing to do with Chef's Effortless Config project, I forgot to google the name before I put it on pypi and now I guess I'm stuck with it.
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl effortless-config-0.7.0:
pip install effortless-config-0.7.0.whl
نصب پکیج tar.gz effortless-config-0.7.0:
pip install effortless-config-0.7.0.tar.gz