README.md

Tree Structured Argument Parser (WIP)

Configure large and complex applications written in Rust similar to Hydra. In library modus a builder pattern and automatic hyperparameter check are added to structures. When compiled with toml flag the functionality is extended with templates and serialization support.

Compared to Hydra there are several key differences:

• use TOML instead of YAML (because it is hard to use)
• hyper-parameter check is enforced during compile time
• template engine allows different expansion, for example globbing, command line execution etc.
• sourcing from different files has no special construct and is treated as a template
• integrated into Rust serde infrastructure

Example

#[param]
#[derive(Debug)]
pub struct Main<const C: bool> {
    seed: u64,
    rev: String,
    experiment: Experiment<C>,
}

impl<const C: bool> ParamGuard for Main<C> {
    type Error = Error;

    fn check(&self) -> Result<(), Self::Error> {
    	match self.seed {
	    42 => Err(Error::InvalidArg("Seed 42 is not allowed".into())),
	    _ => Ok(())
	}
    }
}

fn main() -> Result<(), Error> {
    // populate configuration from main and amend arguments
    let main: Main<false> = Main::from_file("conf/main.toml")?
        .amend_args()?
        .try_into()?;

    // verify all hyper-parameters
    let mut main: Main<true> = main.build()?;

    // run experiment
    main.experiment.run();

    Ok(())
}

and configuration main.toml

seed = 100
rev = { cmd = "git rev-parse --short HEAD" }

[experiment]
variant = "TrainModel"
batch_size = 64
model = { from_file = { base_path = "conf/model", name = "bcresnet" }}

you can then set subtrees of your configuration (use GRU model instead of BCResnet, change seed and batch size)

cargo run --release -- seed=50 experiment.model.from_file.name=gru experiment.batch_size=32

or use a different experiment mode (set to testing mode and load stored model)

cargo run --release -- experiment.from_file.name=test_model experiment.model.from_file.name=stored experiment.model.path=pretrained.pt

you can also use inline maps in TOML style

cargo run --release -- 'dataloader.mix_snr = { Uniform = { low = 10, high = 30 }}'