training_cfgs
Standalone config manager migrated from svaichu/robotdataset: a fluent,
self-learning Config object for training runs, with typed CLI overrides,
W&B sweep export, and two-way Optuna integration —
all driven by the same schema.
Install
pyyaml and optuna are core dependencies — a plain install always gets
YAML config loading and Optuna support, no extras to remember:
pip install -e . # pulls in pyyaml + optuna
pip install -e ".[dev]" # + pytest, for running the test suite
Quick start
from optuna.distributions import CategoricalDistribution, FloatDistribution
from training_cfgs import Config
cfg = Config.from_file("config.yaml")
cfg.set_distribution("training", "learning_rate", FloatDistribution(1e-5, 1e-2, log=True))
cfg.set_distribution("training", "optimizer", CategoricalDistribution(["adam", "sgd"]))
# W&B sweep export
sweep = cfg.to_sweep()
# CLI overrides: python train.py --training.learning_rate 1e-3
cfg = Config.from_cli(default_config="config.yaml")
# Optuna search, same schema as the sweep export above
study = optuna.create_study(direction="minimize")
cfg.single_objective_optimization(study, train, n_trials=50)
best_cfg = cfg.best_from_optuna(study)
See the docs (or the hosted Read the Docs
site, once connected) for the full
guide: doc/basics.md (loading, CLI overrides, exporting),
doc/wandb_sweeps.md (HPO bounds, W&B sweep export),
doc/optuna.md (Optuna integration) — and
examples/ for runnable scripts.
1 maintainer
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