SimpleConfig

Composes Configurations.jl with ArgParse.jl making it easier to write config files.

For users from the python world, this is similar to the functionality provided by ml_collections.

Installation

To install the module, use Julia's package manager: start pkg mode by pressing ] and then enter:

julia (v1.7) pkg> add SimpleConfig

Usage

QuickStart

This package simply extends Configurations.jl. Follow the tutorial in its documentation, to define a configuration. For example:

```julia using SimpleConfig

@option struct SchedulerConfig name::String = "cosine" minlearningrate::Float32 = 1f-4 end

@option struct OptimizerConfig name::String = "adam" learning_rate::Float32 = 3f-3 scheduler::SchedulerConfig end

@option struct ModelConfig backbone::String = "resnet18" stage_size = [2, 2, 2, 2] end

@option struct ExperimentConfig global_seed::Int64 = 0 optimizer::OptimizerConfig model::ModelConfig end ```

One way to initialize the configuration would be to use the from_dict function from Configurations.jl. For example:

```julia using Configurations

defaults = Dict("globalseed" => 0, "optimizer" => Dict("name" => "adam", "learningrate" => 3f-3, "scheduler" => Dict("name" => "cosine", "minlearningrate" => 1f-4)), "model" => Dict("backbone" => "resnet18", "stage_size" => [2, 2, 2, 2]))

cfg = from_dict(ExperimentConfig, defaults) ```

The main problem with this approach is that it is hard to use use in scripts, or when running batch experiments with different configurations. We provide a simple function define_configuration, which automatically creates a argparse settings, so that the configurations can be updated using command line arguments.

julia cfg = define_configuration(ExperimentConfig, defaults)

This simple line in your script will allow you to update the configuration via command line arguments. The structure is extremely self-explainatory, run the script using --help to see the available options.

Basic Example Script

If we run the script examples/script_dict.jl with the following command:

```julia $ julia --project=. examples/script_dict.jl --help

usage: script_dict.jl [--cfg.globalseed CFG.GLOBALSEED] [--cfg.optimizer.name CFG.OPTIMIZER.NAME] [--cfg.optimizer.learningrate CFG.OPTIMIZER.LEARNINGRATE] [--cfg.optimizer.scheduler.name CFG.OPTIMIZER.SCHEDULER.NAME] [--cfg.optimizer.scheduler.minlearningrate CFG.OPTIMIZER.SCHEDULER.MINLEARNINGRATE] [--cfg.model.backbone CFG.MODEL.BACKBONE] [--cfg.model.stagesize CFG.MODEL.STAGESIZE] [-h]

optional arguments: --cfg.globalseed CFG.GLOBALSEED (type: Int64, default: 0) --cfg.optimizer.name CFG.OPTIMIZER.NAME (default: "adam") --cfg.optimizer.learningrate CFG.OPTIMIZER.LEARNINGRATE (type: Float32, default: 0.003) --cfg.optimizer.scheduler.name CFG.OPTIMIZER.SCHEDULER.NAME (default: "cosine") --cfg.optimizer.scheduler.minlearningrate CFG.OPTIMIZER.SCHEDULER.MINLEARNINGRATE (type: Float32, default: 0.0001) --cfg.model.backbone CFG.MODEL.BACKBONE (default: "resnet18") --cfg.model.stagesize CFG.MODEL.STAGESIZE (type: Vector{Int64}, default: [2, 2, 2, 2]) -h, --help show this help message and exit ```

Specifying Defaults in Config Files

Specifying defaults in a dictionary in the script is not great for version controlling experiments. Typically we recommend storing the defaults in config files, and use command line arguments for hyper-parameters like seed. In this case, we need to make a very simple change.

julia cfg = define_configuration(ExperimentConfig, filename)

where filename can be a .toml, .yml or .json file. See examples/script_file.jl for an example script.

Logging Configuration

Logging a deeply nested struct is typically not supported OOTB by most loggers. We provide a simple function flatten_configuration which takes the configuration and returns a single level dictionary which can be easily logged.

Known Issues

1. Using field types like Symbol doesn't work with loading the defaults from a file.

2. NTuples parsing is implemented via type piracy.