[Tune; RLlib] Missing stopping criterion should not error (just warn). #45613
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Signed-off-by: sven1977 <[email protected]>
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justinvyu,
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Hey @justinvyu , could you take a look at this PR? |
justinvyu
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Thanks! I think this generally makes sense.
Could we just add a small unit test here, asserting that the warning is logged?
https://github.com/ray-project/ray/blob/master/python/ray/tune/tests/test_trial.py#L5
python/ray/tune/experiment/trial.py
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| elif criterion not in result: | ||
| if log_once("tune_trial_stop_criterion_not_found"): | ||
| logger.warning( | ||
| f"Stopping criterion {criterion} not found in result dict. " |
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Add a quick note about the behavior that will follow:
"If this metric is never reported, the run will continue running until training is finished instead."
…tune_stop_criterium_not_found_erro
…tune_stop_criterium_not_found_erro
Signed-off-by: sven1977 <[email protected]>
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Hey @justinvyu , thanks for the review! Could you take another look? Added the test cases and enhanced the warning message. Thanks! :) |
Signed-off-by: sven1977 <[email protected]>
justinvyu
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Jun 6, 2024
python/ray/tune/tests/test_trial.py
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| result = _TrainingResult( | ||
| checkpoint=None, metrics={"a": 9.999, "b/c": 0.0, "some_other_key": True} | ||
| ) | ||
| assert not trial.should_stop(result.metrics) |
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Can we just pass in a dict without the _TrainingResult wrapper?
Signed-off-by: sven1977 <[email protected]>
…tune_stop_criterium_not_found_erro
Signed-off-by: sven1977 <[email protected]>
…tune_stop_criterium_not_found_erro
Signed-off-by: sven1977 <[email protected]>
…tune_stop_criterium_not_found_erro
Signed-off-by: sven1977 <[email protected]>
Signed-off-by: sven1977 <[email protected]>
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Jun 12, 2024
ray-project#45613) Signed-off-by: Richard Liu <[email protected]>
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Missing stopping criterion should not error (just warn).
Some RLlib algorithms take a few iterations in order to sample enough data from their EnvRunners to complete an RL episode. Only after that point in time, episode stats will be published in the result dicts returned by
Algorithm.train()(Algorithmis-atune.Trainable).The current hacky fix is to prophylactically add common episode stats (such as
episode_return_mean) to the initialized algorithm's result dict as NaNs, however, this is not a sustailable solution as users might add their own stats and will then run into theTuneError(Stopping criteria ... not provided)error without any idea how to fix this. A one-time warning here would be the better solution.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.