Turns Python functions into CLI via Python annotations

pip install gearup==0.4.1



Have you ever had a moment, when the code is ready, you are eager to launch it, you want to know if your new and shiny method works or not, just to realize you need to write 100+ lines of argparse or click?

Gear up and get ready to go!

Quick (and only) intro

Assume your project contains script with the following functions:

def train(method : str, dataset : str, alpha : float):
  <do stuff>

def test(method : str, dataset : str):
  <do testing>

Just add:

from gearup import gearup

if __name__ == '__main__':
  gearup(train, test)()

and you are ready to go!

> python train method=resnet dataset=mnist alpha=0.01
> python test method=resnet dataset=mnist


As usual:

pip install gearup


pip install git+

How it works

gearup, applied to a function, reads signature of the function and infers types of its arguments from the annotations:

def f(x: int, y: int):
  return x + y

Annotations here can be any callable of type str -> A, that raises either ValueError or TypeError when its argument is not a proper representation of any instance of type A.

When gear-uped function is called without arguments it reads sys.argv, alternatively, it can be called with a list of strings:

gearup(f)(['1', '2']) ### result = 3
gearup(f)() ### read from console arguments

Then, gear-uped function parses arguments using the following rules:

  • if = symbol is present in the argument: k=v, value v is assigned to the argument k and added to kwargs;
  • otherwise, the argument is treated as a positional one and appended to args.

After that the underlying function is called: f(*args, **kwargs), converting arguments in their respective types beforehand...

Yes, no flags, no aliases, just launch script like a python function (Haskell style)...

> python 1 y=2


  • spaces should not appear between argument name, = and argument value:
    • a=x sets value of argument a to x;
    • a = x is interpreted as three separate arguments: two positional: a and x, and a keyword one (with empty name and value);
  • if you need to supply a value with a space character in it, use quotes: python x='a b c';
  • if you need to supply a value with = character in it, just specify argument name: python x=a=b or, better, python x='a=b';
  • it is impossible to set one of variational positional arguments (*args) to a value, that contains = character;
  • if annotation is absent, type of the argument is inferred from its default value;
    • the only exception from this rule is None, in such case, type of the argument is still considered to be absent;
  • default value can be of different type than annotation:
    • this can be used to detect if value was specified or not, e.g. def f(flag: bool = None);
  • bool is automatically wrapped into gearup.common.boolean (see below).

As a bonus, gearup.apply(f, *args, **kwargs) provides a Python-friendly way to do the same thing, which is useful when your script contains multiple methods with non-identical sets of parameters.

import gearup

def method1(x: int, y: int): return x + y
def method2(x: int, z: float): return x / z

def main(method: gearup.choice(method1, method2), x: int, **kwargs):
  gearup.apply(method, x, **kwargs)

if __name__ == '__main__':


Sometimes you need to pack several functions into one script:

gearup(train, test)()
### or
gearup(train=train, test=test)()
### or
gearup(train, test=test)()
> python train <arguments for train>
> python test <arguments for test>

More precisely, if supplied with more than one argument or at least one keyword argument, gearup consumes the first CLI argument and switches between provided functions.

Bonus: it is recursive!

def train(...): pass
def test_fast(...): pass
def test_slow(...): pass

> python train method=resnet alpha=0.1
> python test slow method=resnet

Note: when a non-keyword argument is passed to gearup, it reads __name__ attribute of this argument. For example, gearup(f1, f2) is equivalent to gearup(f1=f1, f2=f2).



As bool type behaves strangely in Python (e.g., bool('False') == True), annotation bool is automatically replaced by gearup.common.boolean, that parses strings that represent boolean values properly.

Variable keyword arguments

Variable keyword arguments (**kwargs) are automatically processes by gearup.special.kwargs.

gearup.special.kwargs supports complex arguments like classifier.alpha=1.0, in which case, it expands variables into nested dictionaries, for example:

from gearup import gearup

def f(**kwargs):

gearup(f)(['clf.alpha=1', 'clf.beta=2', 'method.beta=3'])

prints {'clf': {'alpha': '1', 'beta': '2'}, 'method': {'beta': '3'}}.

This might be useful for handling configuration of methods with non-identical sets of parameters:

from gearup import gearup, apply, choice

def f1(alpha: float): return alpha
def f2(beta: float, gamma: float): return beta + gamma

def main(f: choice(f1, f2), **kwargs):
  return apply(f, **kwargs.get('func', dict()))

gearup(main)(['f=f1', 'func.alpha=3']) ### returns 6.0
gearup(main)(['f=f2', 'func.beta=5', 'func.gamma=6']) ### returns 11.0


gearup.config offers a more strict version of such behavior. gearup.config(arg_name_1, arg_name_2, ..., arg_name_n, typed_arg_1=type_1, ..., typed_arg_m=type_m):

  • checks that all arguments are from the defined set of arguments (arg_name_1, ..., typed_arg_m);
  • checks that all arguments are provided;
  • if supplied with a type, automatically converts values into the corresponding type;
  • type_i can also be a dictionary, which will be converted into a nested config;
  • typed_arg = None as well as untyped configuration option arg_name indicate unchecked values, which might be either a string value (e.g., argument=1) or a dictionary (possibly with nested dictionaries), e.g., argument.x=1 or argument.coefs.alpha=1e-3.

config might be useful if you want to separate arguments into several sets, for example:

from gearup import gearup, apply, choice, config

def f1(alpha: float): return 2 * alpha
def f2(beta: float, gamma: float): return beta + gamma

def g1(x: float): return x + 1
def g2(x: float, y: float): return x + y

def main(f: choice(f1, f2), g: choice(g1, g2), **kwargs: config(fargs=None, gargs=None)):
  return apply(f, **kwargs['fargs']) * apply(g, **kwargs['gargs'])

assert gearup(main)(['f=f1', 'g=g2', 'fargs.alpha=2', 'gargs.x=2.0', 'gargs.y=1.5']) == 14.0
assert gearup(main)(['f=f2', 'g=g1', 'fargs.beta=2', 'fargs.gamma=1e-1', 'gargs.x=9.0']) == 21.0


Just add --help:

> python examples/ --help
Available commands:
train -> (method: {nonlogreg, logreg}, power: [-2, 5), alpha: float)   Trains method with alpha.
test -> slow -> (method: {nonlogreg, logreg})   Tests method...
        fast -> (method: {nonlogreg, logreg, inception})   Undocumented test function.

--help also works with commands:

> python examples/ test --help
Available commands:
slow -> (method: {logreg, nonlogreg})   Tests method...
fast -> (method: {logreg, inception, nonlogreg})   Undocumented test function.
> python examples/ test slow --help

  Tests method...

  A long
  several lines
(method: {nonlogreg, logreg})

Non-standard types

gearup also defines several non-standard types:

  • choice(x_1, x_2, ..., x_n, k_1=v_1, k_2=v_2, ..., k_m=v_m) --- only accepts arguments from the provided set; for a keyword argument k=v, k is used to retrieve the value v, for a positional argument x x.__name__ is used as the key, or str(x) if __name__ attribute is absent; works nicely with functions, e.g. choice(function1, function2). Don't use with numbers as a single number has multiple string representations, e.g., choice(1, 2, 3) does not accept string '01', use interval instead.
  • member[module] --- similar to choice, but retrieves elements from module.__all__ or dir(object) if __all__ is not defined. For example, given a module utils, member[utils] allows to switch between functions defined in the module. Also can retrieve values from submodules, e.g., member[utils]('data.functions.mean') returns
  • either[type_1, type_2, ..., type_n] --- tries to convert supplied value to one of the provided types; note, that type_i has priority over type_j if i < j, thus, e.g., either[float, int] is equivalent to float as any string representing int is also a valid float.
  • interval[a:b] --- half-open interval a <= x < b, type (int or float) is inferred from types of a and b; also a more complete constructor exists: interval(start, stop, left=True, right=False, cast=None).
  • a < number, a <= number, number < b, number <= b - an alternative syntax for constructing intervals, intervals can also be combined via &, e.g., (a < number) & (number < b) (note, that parenthesis are required as almost every operator has higher priority than comparison operators). Unfortunately, Python does not support overloading chained comparisons, thus, a nice a < number < b syntax is not available, however, (a < number) < b works fine.