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Argument class <=> Human friendly cli

Project description

Smart Argument Suite (smart-arg)

GitHub tag PyPI version

Smart Argument Suite (smart-arg) is a slim and handy Python library that helps one work safely and conveniently with the arguments that are represented by an immutable argument container class' fields (NamedTuple or dataclass out-of-box), and passed through command-line interfaces.

smart-arg promotes arguments type-safety, enables IDEs' code autocompletion and type hints functionalities, and helps one produce correct code.

Quick start

The smart-arg package is available through pip.

pip3 install smart-arg

Users can bring or define, if not already, their argument container class -- a NamedTuple or dataclass, and then annotate it with smart-arg decorator @arg_suite in their Python scripts.

Now an argument container class instance, e.g. my_arg of MyArg class, once created, is ready to be serialized by the smart-arg API -- my_arg.__to_argv__() to a sequence of strings, passed through the command-line interface and then deserialized back to an instance again by my_arg = MyArg.__from_argv__(sys.argv[1:]).

import sys
from typing import NamedTuple, List, Tuple, Dict, Optional
from smart_arg import arg_suite

# Define the argument container class
class MyArg(NamedTuple):
    MyArg is smart! (docstring goes to description)
    nn: List[int]  # Comments go to argparse help
    a_tuple: Tuple[str, int]  # a random tuple argument
    encoder: str  # Text encoder type
    h_param: Dict[str, int]  # Hyperparameters
    batch_size: Optional[int] = None
    adp: bool = True  # bool is a bit tricky
    embedding_dim: int = 100  # Size of embedding vector
    lr: float = 1e-3  # Learning rate

def cli_interfaced_job_scheduler():
    This is to be called by the job scheduler to set up the job launching command,
    i.e., producer side of the Python job arguments
    # Create the argument container instance
    my_arg = MyArg(nn=[3], a_tuple=("str", 1), encoder='lstm', h_param={}, adp=False)  # The patched argument container class requires keyword arguments to instantiate the class

    # Serialize the argument to command-line representation
    argv = my_arg.__to_argv__()
    cli = ' ' + ' '.join(argv)
    # Schedule the job with command line `cli`
    print(f"Executing job:\n{cli}")
    # Executing job:
    # --nn 3 --a_tuple str 1 --encoder lstm --h_param --batch_size None --adp False --embedding_dim 100 --lr 0.001

def my_job(my_arg: MyArg):
    This is the actual job defined by the input argument my_arg,
    i.e., consumer side of the Python job arguments
    # MyArg(nn=[3], a_tuple=('str', 1), encoder='lstm', h_param={}, batch_size=None, adp=False, embedding_dim=100, lr=0.001)
    # `my_arg` can be used in later script with a typed manner, which help of IDEs (type hints and auto completion)
    # ...
    print(f"My network has {len(my_arg.nn)} layers with sizes of {my_arg.nn}.")
    # My network has 1 layers with sizes of [3].

if __name__ == '__main__':
    # Deserialize the command-line representation of the argument back to a container instance 
    arg_deserialized: MyArg = MyArg.__from_argv__(sys.argv[1:])  # Equivalent to `MyArg(None)`, one positional arg required to indicate the arg is a command-line representation.
> python -h
usage: [-h] --nn [int [int ...]] --a_tuple str int --encoder str
                 --h_param [str:int [str:int ...]] [--batch_size int]
                 [--adp {True,False}] [--embedding_dim int] [--lr float]

MyArg is smart! (docstring goes to description)

optional arguments:
  -h, --help            show this help message and exit
  --nn [int [int ...]]  (List[int], required) Comments go to argparse help
  --a_tuple str int     (Tuple[str, int], required) a random tuple argument
  --encoder str         (str, required) Text encoder type
  --h_param [str:int [str:int ...]]
                        (Dict[str, int], required) Hyperparameters
  --batch_size int      (Optional[int], default: None)
  --adp {True,False}    (bool, default: True) bool is a bit tricky
  --embedding_dim int   (int, default: 100) Size of embedding vector
  --lr float            (float, default: 0.001) Learning rate

Promoted practices

  • Focus on defining the arguments diligently, and let the smart-arg (backed by argparse.ArgumentParser) work its magic around command-line interface.
  • Always work directly with argument container class instances when possible, even if you only need to generate the command-line representation.
  • Stick to the default behavior and the basic features, think twice before using any of the advanced features.

More detail

For more features and implementation detail, please refer to the documentation.


Please read for details on our code of conduct, and the process for submitting pull requests to us.


This project is licensed under the BSD 2-CLAUSE LICENSE - see the file for details

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