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.
To install the module, use Julia's package manager: start pkg mode by pressing ] and then enter:
(v1.7) pkg> add SimpleConfigThis package simply extends Configurations.jl. Follow the tutorial in its
documentation, to define a
configuration. For example:
using SimpleConfig
@option struct SchedulerConfig
name::String = "cosine"
min_learning_rate::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
endOne way to initialize the configuration would be to use the from_dict function from
Configurations.jl. For example:
using Configurations
defaults = Dict("global_seed" => 0,
"optimizer" => Dict("name" => "adam", "learning_rate" => 3f-3,
"scheduler" => Dict("name" => "cosine",
"min_learning_rate" => 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.
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.
If we run the script examples/script_dict.jl with the following command:
$ julia --project=. examples/script_dict.jl --help
usage: script_dict.jl [--cfg.global_seed CFG.GLOBAL_SEED]
[--cfg.optimizer.name CFG.OPTIMIZER.NAME]
[--cfg.optimizer.learning_rate CFG.OPTIMIZER.LEARNING_RATE]
[--cfg.optimizer.scheduler.name CFG.OPTIMIZER.SCHEDULER.NAME]
[--cfg.optimizer.scheduler.min_learning_rate CFG.OPTIMIZER.SCHEDULER.MIN_LEARNING_RATE]
[--cfg.model.backbone CFG.MODEL.BACKBONE]
[--cfg.model.stage_size CFG.MODEL.STAGE_SIZE]
[-h]
optional arguments:
--cfg.global_seed CFG.GLOBAL_SEED
(type: Int64, default: 0)
--cfg.optimizer.name CFG.OPTIMIZER.NAME
(default: "adam")
--cfg.optimizer.learning_rate CFG.OPTIMIZER.LEARNING_RATE
(type: Float32, default: 0.003)
--cfg.optimizer.scheduler.name CFG.OPTIMIZER.SCHEDULER.NAME
(default: "cosine")
--cfg.optimizer.scheduler.min_learning_rate CFG.OPTIMIZER.SCHEDULER.MIN_LEARNING_RATE
(type: Float32, default: 0.0001)
--cfg.model.backbone CFG.MODEL.BACKBONE
(default: "resnet18")
--cfg.model.stage_size CFG.MODEL.STAGE_SIZE
(type: Vector{Int64}, default: [2, 2, 2, 2])
-h, --help show this help message and exitSpecifying 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.
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 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.
-
Using field types like
Symboldoesn't work with loading the defaults from a file. -
NTuples parsing is implemented via type piracy.