Simple and efficient argument parser for every python projects
Project description
Upstride argument parser
This package provides a simple and efficient argument parser for every python projects. The idea is to define once all the arguments and then be able to parse them from the command line or configuration files
Example
Let's start with a simple example to demonstrate how it works :
let's create a file app.py
containing :
import upstride_argparse as argparse
arguments = [
[int, "batch_size", 128, 'The size of batch per gpu', lambda x: x > 0],
[bool, "cpu", False, 'run on cpu'],
[str, 'description', '', 'description of the experiment'],
[float, "lr", 0.0001, 'learning rate', lambda x: x > 0],
['list[int]', "raw_size", [256, 256, 3], 'raw shape of one image'],
['list[str]', "yaml_config", [], "config files"]
]
config = argparse.parse_cmd(arguments)
print(config)
so as you can see, the arguments are defined using a standard python list containing lists of [type, name, default, help, condition]
- type can be a python type (
int
,bool
,str
,float
) or a stringlist[python_type]
for processing lists - condition is a function called when the parameters will be parsed. if one parameter doesn't respect the condition, an exception will be raised.
Now let's try to call this program
python app.py
prints{'batch_size': 128, 'cpu': False, 'description': '', 'lr': 0.0001, 'raw_size': [256, 256, 3], 'yaml_config': []}
. This dictionary contains the default configurationpython app.py --cpu --lr 0.1 --description hello --raw_size 28 28 1
prints{'batch_size': 128, 'cpu': True, 'description': 'hello', 'lr': 0.1, 'raw_size': [28, 28, 1], 'yaml_config': []}
now lets create a yaml file config.yml
containing :
batch_size: 16
cpu: true
python app.py --yaml_config config.yml
prints{'batch_size': 16, 'cpu': True, 'description': '', 'lr': 0.0001, 'raw_size': [256, 256, 3], 'yaml_config': []}
python app.py --yaml_config config.yml --cpu false
prints{'batch_size': 16, 'cpu': False, 'description': '', 'lr': 0.0001, 'raw_size': [256, 256, 3], 'yaml_config': []}
as you can see, the command line has the priority over the configuration file
It is also possible to split the configuration between as many configuration file as you want
Namespaces
For larger project, it can become useful to define namespaces to organized the configuration. This can be done like this :
import upstride_argparse as argparse
arguments = [
[int, "batch_size", 128, 'The size of batch per gpu', lambda x: x > 0],
['list[str]', "yaml_config", [], "config files"],
['namespace', 'first_namespace', [
[str, 'arg1', 'hello', 'first argument'],
['namespace', 'second_namespace', [
[bool, "i_am_not_doing_anything", True, ''],
[bool, "nether_do_i", False, '']
]],
]],
]
config = argparse.parse_cmd(arguments)
print(config)
- calling
python app.py
will print{'batch_size': 128, 'yaml_config': [], 'first_namespace': {'arg1': 'hello', 'second_namespace': {'i_am_not_doing_anything': True, 'nether_do_i': False}}}
variable from namespace can be configure from yaml config file this way :
batch_size: 16
first_namespace:
arg1: world
second_namespace:
i_am_not_doing_anything: false
nether_do_i: true
- calling
python app.py --yaml_config config.yml
will print{'batch_size': 16, 'yaml_config': [], 'first_namespace': {'arg1': 'world', 'second_namespace': {'i_am_not_doing_anything': False, 'nether_do_i': True}}}
and these variables can be setup from the command line like this : python app.py --yaml_config config.yml --first_namespace.arg1 bob --first_namespace.second_namespace.i_am_not_doing_anything false
it will print : {'batch_size': 16, 'yaml_config': [], 'first_namespace': {'arg1': 'bob', 'second_namespace': {'i_am_not_doing_anything': False, 'nether_do_i': True}}}
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