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WIP: Introduce TrialRunner Abstraction #720

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@bpkroth bpkroth commented Mar 19, 2024

This is another step in adding support for parallel trial execution #380.

Here we separate out the running of an individual trial to a single class - TrialRunner.

Multiple TrialRunners are instantiated at CLI invocation with the --num-trial-runners argument. 
Each TrialRunner associated with a single copy of the root Environment, and made unique by means of a unique trial_runner_id value that's included in that Environment's global_config.

TODO:

  • tests

In future PRs we will add:

  • New Scheduler implementations to run TrialRunners in parallel.
  • Async polling of status results in each TrialRunner independently.

@@ -46,6 +46,16 @@ def __init__( # pylint: disable=too-many-arguments
self._engine = engine
self._schema = schema

def _save_new_config_data(self, new_config_data: Dict[str, Union[int, float, str]]) -> None:
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Needs tests

@@ -145,6 +161,7 @@ def get_results_df(
"tunable_config_id",
"tunable_config_trial_group_id",
"status",
"trial_runner_id",
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Needs tests

"""ID of the TrialRunner."""
if not self._trial_runner_id:
self._trial_runner_id = self.metadata_dict.get("trial_runner_id")
return self._trial_runner_id
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Needs tests

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2 participants