happy-config 0.0.4

Managing ML experiment configuation with joy.

Happy Config

Managing ML experiment configuration with joy.

• Use dataclass to define the schema for experiment configurations.
• Load configuration from multiple sources: JSON, YAML, NNI.
• Annotate fields with search space, and generate NNI search space file using CLI tool.

Basic Usage

Define configuration schema with dataclass

from dataclasses import dataclass

@dataclass
class Bar:
    a: str
    b: str

@dataclass 
class Foo:
    x: int
    y: int
    bar: Bar

Write configuration files

{
  "x": 1,
  "y": 1,
  "bar:a": "1",
  "bar:b": "1"
}

Note the namespace syntax bar:a we use in JSON. It is also possible to write:

{
  "x": 1,
  "y": 1,
  "bar": {
    "a": "1"
  },
  "bar:b": "1"
}

Create a ConfigLoader instacen from the schema, and load the configuration

from happy_config import ConfigLoader
loader = ConfigLoader(model=Foo, config="config.json")
config: Foo = loader()  # Foo(x=1, y=1, bar=Bar(a="1", b="1"))

If we save the code in main.py, we can use command line arguments to specify the configuration file path:

python main.py --config config.json

And we can overwrite the configurations directly using command line arguments:

python main.py --x 10 --bar:b 100

We have to use the namespace syntax here.

Generating NNI Search Space File

We embed parameter search spaces in the definition of configuration schema.

from dataclasses import dataclass
from happy_config import ConfigLoader
from happy_config.typechecking import show_type
from happy_config.param_tuning import with_search_space


@dataclass
class Bar:
    s: str


@dataclass
class Foo:
    y: int
    bar: Bar
    x: float = with_search_space(1.0, space_type='uniform', space_value=[0.0, 1.0])


@dataclass
class Config:
    foo: Foo
    i: int = with_search_space(0, space_type='choice', space_value=[1, 2, 3])

Save the file in main.py. And in CLI:

python -m happy_config genspace -m main.Config -o search_space.json

The outputed search_space.json file:

{
    "foo:x": {
        "_type": "uniform",
        "_value": [
            0.0,
            1.0
        ]
    },
    "i": {
        "_type": "choice",
        "_value": [
            1,
            2,
            3
        ]
    }
}