def test_result_grid_future_checkpoint(ray_start_2_cpus, to_object): trainable_cls = get_trainable_cls("__fake") trial = Trial("__fake", stub=True) trial.config = {"some_config": 1} trial.last_result = {"some_result": 2, "config": trial.config} trainable = ray.remote(trainable_cls).remote() ray.get(trainable.set_info.remote({"info": 4})) if to_object: checkpoint_data = trainable.save_to_object.remote() else: checkpoint_data = trainable.save.remote() trial.on_checkpoint( _TrackedCheckpoint(checkpoint_data, storage_mode=CheckpointStorage.MEMORY)) trial.pickled_error_file = None trial.error_file = None result_grid = ResultGrid(None) # Internal result grid conversion result = result_grid._trial_to_result(trial) assert isinstance(result.checkpoint, Checkpoint) assert isinstance(result.metrics, dict) assert isinstance(result.config, dict) assert result.metrics_dataframe is None assert result.config == {"some_config": 1} assert result.metrics["config"] == result.config # Load checkpoint data (see ray.rllib.algorithms.mock.MockTrainer definition) with result.checkpoint.as_directory() as checkpoint_dir: with open(os.path.join(checkpoint_dir, "mock_agent.pkl"), "rb") as f: info = pickle.load(f) assert info["info"] == 4
def _exploit( self, trial_executor: "trial_runner.RayTrialExecutor", trial: Trial, trial_to_clone: Trial, ): """Transfers perturbed state from trial_to_clone -> trial. If specified, also logs the updated hyperparam state. """ trial_state = self._trial_state[trial] new_state = self._trial_state[trial_to_clone] logger.info("[exploit] transferring weights from trial " "{} (score {}) -> {} (score {})".format( trial_to_clone, new_state.last_score, trial, trial_state.last_score)) new_config = self._get_new_config(trial, trial_to_clone) # Only log mutated hyperparameters and not entire config. old_hparams = { k: v for k, v in trial_to_clone.config.items() if k in self._hyperparam_mutations } new_hparams = { k: v for k, v in new_config.items() if k in self._hyperparam_mutations } logger.info("[explore] perturbed config from {} -> {}".format( old_hparams, new_hparams)) if self._log_config: self._log_config_on_step(trial_state, new_state, trial, trial_to_clone, new_config) new_tag = _make_experiment_tag(trial_state.orig_tag, new_config, self._hyperparam_mutations) if trial.status == Trial.PAUSED: # If trial is paused we update it with a new checkpoint. # When the trial is started again, the new checkpoint is used. if not self._synch: raise TuneError("Trials should be paused here only if in " "synchronous mode. If you encounter this error" " please raise an issue on Ray Github.") else: trial_executor.stop_trial(trial) trial_executor.set_status(trial, Trial.PAUSED) trial.set_experiment_tag(new_tag) trial.set_config(new_config) trial.on_checkpoint(new_state.last_checkpoint) self._num_perturbations += 1 # Transfer over the last perturbation time as well trial_state.last_perturbation_time = new_state.last_perturbation_time trial_state.last_train_time = new_state.last_train_time
def on_trial_result(self, trial_runner: "trial_runner.TrialRunner", trial: Trial, result: Dict) -> str: if TRAINING_ITERATION not in result: # No time reported return TrialScheduler.CONTINUE if not self._next_policy: # No more changes in the config return TrialScheduler.CONTINUE step = result[TRAINING_ITERATION] self._current_step = step change_at, new_config = self._next_policy if step < change_at: # Don't change the policy just yet return TrialScheduler.CONTINUE logger.info("Population Based Training replay is now at step {}. " "Configuration will be changed to {}.".format( step, new_config)) checkpoint = trial_runner.trial_executor.save(trial, CheckpointStorage.MEMORY, result=result) new_tag = _make_experiment_tag(self.experiment_tag, new_config, new_config) trial_executor = trial_runner.trial_executor trial_executor.stop_trial(trial) trial_executor.set_status(trial, Trial.PAUSED) trial.set_experiment_tag(new_tag) trial.set_config(new_config) trial.on_checkpoint(checkpoint) self.current_config = new_config self._num_perturbations += 1 self._next_policy = next(self._policy_iter, None) return TrialScheduler.NOOP