[RLlib] Cleanup examples folder #14: Add example script for how to resume a tune.Tuner.fit() experiment from a checkpoint. #45681
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Signed-off-by: sven1977 <[email protected]>
…nup_examples_folder_14_continue_training_from_checkpoint
Signed-off-by: sven1977 <[email protected]>
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Signed-off-by: sven1977 <[email protected]>
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LGTM. Invaluable example for users!
| tuner = tune.Tuner( | ||
| trainable=config.algo_class, | ||
| param_space=config, | ||
| run_config=air.RunConfig( |
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In regard to the future deprecation of air: Can we use ray.train.RunConfig here instead?
| param_space=config, | ||
| run_config=air.RunConfig( | ||
| callbacks=tune_callbacks, | ||
| checkpoint_config=air.CheckpointConfig( |
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Same here: can we use ray.train.CheckpointConfig?
| results = tuner.fit() | ||
| experiment_name = Path(results.experiment_path).name | ||
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| # Extract the latest checkpoint from the results and confirm it's the right one. |
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Let's state this comment differently. The get_best_result gets us only in this specific setup and only with a checkpoint frequency of 1 the latest checkpoint, otherwise we get the one with the highest episode_return_mean from whenever this happened.
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Ah, good catch! Yes, in this example, we should probably just use the last checkpoint, not necessarily the best. ...
| # TODO (simon): Change to -800 once the metrics are fixed. Currently | ||
| # the combined return is not correctly computed. | ||
| f"{ENV_RUNNER_RESULTS}/episode_return_mean": -400, | ||
| f"{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": -800, |
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…r how to resume a tune.Tuner.fit() experiment from a checkpoint. (ray-project#45681) Signed-off-by: Richard Liu <[email protected]>
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Cleanup examples folder #14: Add example script for how to resume a tune.Tuner.fit() experiment from a checkpoint.
MetricsLogger.peek()(keyinstead of*keyto unify signature with all the other methods ofMetricsLogger).Why are these changes needed?
Related issue number
Checks
git commit -s) in this PR.scripts/format.shto lint the changes in this PR.method in Tune, I've added it in
doc/source/tune/api/under thecorresponding
.rstfile.