hyperparameter 0.5.6

A hyper-parameter library for researchers, data scientists and machine learning engineers.

Hyperparameter

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Hyperparameter, Make configurable AI applications.Build for Python hackers.

Quick Start

Hyperparameter uses auto _ param decorator to convert keywords arguments into configurable parameters:

from hyperparameter import auto_param

@auto_param("foo")
def foo(x, y=1, z="a"):
    return f"x={x}, y={y}, z={z}"

The parameters can be controlled with param_scope

from hyperparameter import param_scope

foo(1) # x=1, y=1, z='a'
with param_scope(**{"foo.y":2}):
    foo(1) # x=1, y=2, z='a'

Advanced Usage

Read/Write Parameters

from hyperparameter import param_scope

# create param_scope
with param_scope():
    pass

with param_scope("foo.y=1", "foo.z=b"):
    pass

with param_scope(**{"foo.y":1, "foo.z":2}):
    pass

# read param with default value
with param_scope(**{"foo.y":2}) as ps:
    y = ps.foo.y(1)  
    y = ps.foo.y | 1
    y = param_scope.foo.y(1)
    y = param_scope.foo.y | 1
    foo(1) # x=1, y=2, z='a'

# wite values to param_scope
with param_scope(**{"foo.y":2}) as ps:
    ps.foo.y = 2
    param_scope.foo.y = 2

Nested Scope

Hyperparameter support nested param_scope:

from hyperparameter import param_scope

# no param_scope, use the default value defined in foo
foo(1) # x=1, y=1, z='a'

# start a new param_scope
# and set the default value of `foo.y` to `2`
with param_scope(**{"foo.y":2}) as ps:
    # found one param_scope `ps`, 
    # and receive default value of `foo.y` from `ps`
    foo(1) # x=1, y=2, z='a'

    # start another param_scope
    # and set the default value of `foo.y` to `3`
    with param_scope(**{"foo.z": "b"}) as ps2:
        # found nested param_scope `ps2`, 
        # and receive default values of `foo.z` from `ps2`
        foo(1) # x=1, y=2, z='b'
    # `ps2` ends here, and `foo.y` is restored to `2`
    foo(1) # x=1, y=2, z='a'
# `ps` ends here, and `foo.y` is restored to `1`
foo(1) # x=1, y=1, z='a'

CMD Line Arguments

An example CLI app:

from hyperparameter import param_scope, auto_param

@auto_param
def main(a=0, b=1): # `inline default values`
    print(a, b)

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()

    parser.add_argument("-D", "--define", nargs="*", default=[], action="extend")
    args = parser.parse_args()

    with param_scope(*args.define):
        main()

Examples

parameter tunning for researchers

This example shows how to use hyperparameter in your research projects, and make your experiments reproducible.

experiment tracing for data scientists

This example shows experiment management with hyperparameter, and tracing the results with mlflow.tracing.