mlos_bench.optimizers.one_shot_optimizer

No-op optimizer for mlos_bench that proposes a single configuration.

Classes

OneShotOptimizer

No-op optimizer that proposes a single configuration and returns.

Module Contents

class mlos_bench.optimizers.one_shot_optimizer.OneShotOptimizer(tunables: mlos_bench.tunables.tunable_groups.TunableGroups, config: dict, global_config: dict | None = None, service: mlos_bench.services.base_service.Service | None = None)[source]

Bases: mlos_bench.optimizers.mock_optimizer.MockOptimizer

No-op optimizer that proposes a single configuration and returns.

Explicit configs (partial or full) are possible using configuration files.

Create a new optimizer for the given configuration space defined by the tunables.

Parameters:

  • tunables (TunableGroups) – The tunables to optimize.

  • config (dict) – Free-format key/value pairs of configuration parameters to pass to the optimizer.

  • global_config (Optional[dict])

  • service (Optional[Service])

suggest() → mlos_bench.tunables.tunable_groups.TunableGroups[source]

Always produce the same (initial) suggestion.

Return type:

mlos_bench.tunables.tunable_groups.TunableGroups

property supports_preload: bool[source]

Return True if the optimizer supports pre-loading the data from previous experiments.

Return type:

bool