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        "description": "# Effortless Config\n\n[![PyPI version](https://badge.fury.io/py/effortless-config.svg)](https://badge.fury.io/py/effortless-config) [![CircleCI](https://circleci.com/gh/andreasjansson/effortless-config/tree/master.svg?style=svg)](https://circleci.com/gh/andreasjansson/effortless-config/tree/master)\n\n_Globally scoped configuration with argparse integration._\n\n## Installation\n\n```\npip install effortless-config\n```\n\n## Rationale\n\n1. 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.\n2. As the work progresses, I end up with _groups_ of specific configuration settings that correspond to specific experiments.\n3. I want to be able to select configuration group on the command line when I start an experiment.\n4. And I want to be able to override certain settings within that experiment, from the command line.\n\n## Basic usage\n\nFirst, define a class that extends `effortless_config.Config` and specify your default configuration values inside it:\n\n```python\nfrom effortless_config import Config\n\nclass config(Config):  # lowercase \"c\" optional\n\n    SOME_INTEGER_SETTING = 10\n    FLOAT_SETTING = 0.5\n    A_BOOLEAN = False\n    MY_STRING_SETTING = 'foo'\n```\n\nThen you can refer to these settings elsewhere in your code base:\n\n```python\ndef main():\n    print(f'SOME_INTEGER_SETTING is {config.SOME_INTEGER_SETTING}')\n    print(f'FLOAT_SETTING is {config.FLOAT_SETTING}')\n    print(f'A_BOOLEAN is {config.A_BOOLEAN}')\n    print(f'MY_STRING_SETTING is {config.MY_STRING_SETTING}')\n```\n\nThe 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\n\n```python\nif __name__ == '__main__':\n    config.parse_args()\n    main()\n```\n\n...we can see the available settings with `-h/--help`:\n\n```console\n$ python basic_example.py --help\nusage: basic_example.py [-h] [--some-integer-setting SOME_INTEGER_SETTING]\n                        [--float-setting FLOAT_SETTING] [--a-boolean {true,false}]\n                        [--my-string-setting MY_STRING_SETTING]\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --some-integer-setting SOME_INTEGER_SETTING\n  --float-setting FLOAT_SETTING\n  --a-boolean {true,false}\n  --my-string-setting MY_STRING_SETTING\n```\n\nWe can then override configuration settings from the command line:\n\n```console\n$ python basic_example.py\nSOME_INTEGER_SETTING is 10\nFLOAT_SETTING is 0.5\nA_BOOLEAN is False\nMY_STRING_SETTING is foo\n\n$ python basic_example.py --some-integer-setting 1000\nSOME_INTEGER_SETTING is 1000\nFLOAT_SETTING is 0.5\nA_BOOLEAN is False\nMY_STRING_SETTING is foo\n```\n\nList and dict type settings can be overridden by passing json strings from the command line, e.g.\n\n```console\n$ python lists_and_dicts.py --my-list='[1, 2, 3]' --my-dict='{\"foo\": 10, \"bar\": 20}'\n```\n\n## Using _groups_\n\nConfiguration _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.\n\nTo specify groups, use the `settings(...)` function:\n\n```python\nfrom effortless_config import Config, setting\n\nclass config(Config):\n    groups = ['large', 'small']\n\n    NUM_LAYERS = setting(default=5, large=10, small=3)\n    NUM_UNITS = setting(default=128, large=512, small=32)\n    USE_SKIP_CONNECTIONS = setting(default=True, small=False)\n    LEARNING_RATE = 0.1\n    OPTIMIZER = 'adam'\n```\n\n`setting(...)` has the signature:\n\n```python\ndef setting(default: T, **kwargs: T) -> T\n```\n\n...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')`.\n\nWhen using groups you must first define the group names using the `config.groups` list.\n\nThen in your code you can use these settings like in the basic example\n\n```python\ndef main():\n    print(f'NUM_LAYERS is {config.NUM_LAYERS}')\n    print(f'NUM_UNITS is {config.NUM_UNITS}')\n    print(f'USE_SKIP_CONNECTIONS is {config.USE_SKIP_CONNECTIONS}')\n    print(f'LEARNING_RATE is {config.LEARNING_RATE}')\n    print(f'OPTIMIZER is {config.OPTIMIZER}')\n\n\nif __name__ == '__main__':\n    config.parse_args()\n    main()\n```\n\nNow we see an additional `--configuration` option when we ask for `--help`:\n\n```console\n$ python group_example.py --help\nusage: group_example.py [-h] [--configuration {default,large,small}]\n                        [--num-layers NUM_LAYERS] [--num-units NUM_UNITS]\n                        [--use-skip-connections {true,false}]\n                        [--learning-rate LEARNING_RATE] [--optimizer OPTIMIZER]\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --configuration {default,large,small}, -c {default,large,small}\n  --num-layers NUM_LAYERS\n  --num-units NUM_UNITS\n  --use-skip-connections {true,false}\n  --learning-rate LEARNING_RATE\n  --optimizer OPTIMIZER\n```\n\nThe `--configuration` option specifies the configuration group, and defaults to `default` if omitted.\n\nFor example:\n\n```console\n$ python group_example.py\nNUM_LAYERS is 5\nNUM_UNITS is 128\nUSE_SKIP_CONNECTIONS is True\nLEARNING_RATE is 0.1\nOPTIMIZER is adam\n\n$ python group_example.py --configuration large\nNUM_LAYERS is 10\nNUM_UNITS is 512\nUSE_SKIP_CONNECTIONS is True\nLEARNING_RATE is 0.1\nOPTIMIZER is adam\n\n$ python group_example.py --configuration small\nNUM_LAYERS is 3\nNUM_UNITS is 32\nUSE_SKIP_CONNECTIONS is False\nLEARNING_RATE is 0.1\nOPTIMIZER is adam\n```\n\nWe can also override individual settings in conjunction with groups. Individual settings take precedence over the group setting:\n\n```console\n$ python group_example.py --configuration large --num-units 768\nNUM_LAYERS is 10\nNUM_UNITS is 768\nUSE_SKIP_CONNECTIONS is True\nLEARNING_RATE is 0.1\nOPTIMIZER is adam\n```\n\n## Testing\n\nWhen writing tests, you can use the `config.override` context manager to override individual settings:\n\n```python\nimport pytest\nfrom .config import config\n\ndef test_with_context_manager():\n    with config.override(FLOAT_SETTING=0.8, A_BOOLEAN=True):\n        assert config.FLOAT_SETTING * config.SOME_INTEGER_SETTING == 8\n        assert config.A_BOOLEAN is True\n```\n\nThe `config.override` method can also be used without context management in conjunction with `config.reset_to_defaults`:\n\n```python\ndef test_with_manual_reset():\n    config.override(FLOAT_SETTING=0.8, A_BOOLEAN=True)\n    assert config.FLOAT_SETTING * config.SOME_INTEGER_SETTING == 8\n    assert config.A_BOOLEAN is True\n    config.reset_to_defaults()\n```\n\n---\n\nBtw, 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.\n\n\n",
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