# Resuming tune optimization from previously explored configurations

**URL:** <https://discuss.ray.io/t/resuming-tune-optimization-from-previously-explored-configurations/12294>\
**Category:** Uncategorized\
**Created:** [September 28, 2023, 9:10pm UTC](https://discuss.ray.io/t/resuming-tune-optimization-from-previously-explored-configurations/12294 "2023-09-28T21:10:10Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![Moslem\_Noori](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/moslem_noori/32/5136_2.png) [@Moslem\_Noori](https://discuss.ray.io/u/Moslem_Noori)\
**Post date:** [September 28, 2023, 9:10pm UTC](https://discuss.ray.io/t/resuming-tune-optimization-from-previously-explored-configurations/12294/1 "2023-09-28T21:10:10Z")

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Hi,  
In older versions of Ray (1.13 for example), I could set the `resume` flag for `tune.run()` to true so that the hyperparameter optimization resumes even from a previously finished experiment. For example, if in a previous experiment, the tuner already explore `{“a”:1, “b”:1.5}, it won’t do it again and only runs for unexplored configurations.  
I’v recently started migrating my code to newer versions of Ray and can’t find a similar feature in the most recent release. Has this feature been removed or it’s me who can’t find it?

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**Author:** ![justinvyu](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/justinvyu/32/4309_2.png) [@justinvyu](https://discuss.ray.io/u/justinvyu)\
**Post date:** [September 29, 2023, 12:57am UTC](https://discuss.ray.io/t/resuming-tune-optimization-from-previously-explored-configurations/12294/2 "2023-09-29T00:57:59Z")

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Experiment restoration is still possible, though it’s only for unfinished experiments that were interrupted/crashed in the middle. Ex: If I had 5 pending trials at the time of the experiment crashing, then restoring will finish up running those 5. If I had a previously finished experiment (all trials terminated), then restoring will not have anything left to run. This is in line with the previous `tune.run(resume)` flag.

Here’s how you do that: [How to Enable Fault Tolerance in Ray Tune — Ray 2.7.0](https://docs.ray.io/en/latest/tune/tutorials/tune-fault-tolerance.html#restore-a-tune-experiment)

If you’re looking to start a new run, but start from the saved searcher state from a previous experiment, this is probably what you’re looking for: [Tune Search Algorithms (tune.search) — Ray 2.7.0](https://docs.ray.io/en/latest/tune/api/suggestion.html#saving-and-restoring-tune-search-algorithms)

Let me know if this clarifies things for you!

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**Author:** ![Moslem\_Noori](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/moslem_noori/32/5136_2.png) [@Moslem\_Noori](https://discuss.ray.io/u/Moslem_Noori)\
**Post date:** [October 3, 2023, 4:19pm UTC](https://discuss.ray.io/t/resuming-tune-optimization-from-previously-explored-configurations/12294/3 "2023-10-03T16:19:32Z")

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Thanks @justinvyu for the answer.  
I was looking for something more like the Tune search algorithm restoration. I will try it out.