Comments (2)
Neeratyoy
commented on June 21, 2024
Hi!
Thanks for your interest in DEHB. The original design of DEHB was designed to interface with black-box functions that come with a specific fidelity value. For a NN it would be the maximum epochs. One way to approach it would be to implement model checkpointing from within the objective_function definition such that a model when queried for a higher fidelity resumes training based on some file saved to disk. However, DEHB currently doesn't support such a feature implicitly.
However, there is currently an unmerged PR #6 that might be of interest to you!
(Unfortunately, I cannot commit to a time when this PR will be merged but user feedback on the PR might expedite the merge ;) )
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pavelbatyr
commented on June 21, 2024
Thanks a lot for the detailed answer!
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Related Issues (12)
- Improve readability of DEHB examples and docstrings HOT 1
- Support for python 3.7 HOT 7
- Upgrade ConfigSpace HOT 1
- DEHB and non-async DE optimiser update design HOT 5
- Example on how to train an RF leaks data HOT 1
- DE Optimiser return syntax is deprecated HOT 5
- [Question] Understanding how the parent pool is generated HOT 2
- DEHB hangs when there are pending tasks HOT 1
- Hyperband bracket generation is inconsistent with the original Hyperband paper HOT 1
- Question about Torch version HOT 1
- Pip-installable Issue HOT 6
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