Configuration

defaults:
- override hydra/sweeper: orion

hydra:
    sweeper:
       params:
          a: "uniform(0, 1)"
          b: "uniform(0, 1)"

       experiment:
          name: 'experiment'
          version: '1'

       algorithm:
          type: random
          config:
             seed: 1

       worker:
          n_workers: -1
          max_broken: 3
          max_trials: 100

       storage:
          type: legacy
          database:
             type: pickleddb
             host: 'database.pkl'

# Default values
a: 0
b: 0

Code

import hydra
from omegaconf import DictConfig

@hydra.main(config_path=".", config_name="config")
def main(cfg: DictConfig) -> float:
   """Simple main function"""
   a = cfg.a
   b = cfg.b

   return float(a + b)

if __name__ == "__main__":
   main()

Running

To run the hyper parameter optimization process you need to specify the --multirun argument.

python my_app.py --multirun

The search space can also be tweaked from the command line

python my_app.py --multirun batch_size=4,8,12,16 optimizer.name=Adam,SGD 'optimizer.lr="loguniform(0.001, 1.0)"'