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- concept for multi-fidelity trainer - concept for successive halving optimizer Co-authored-by: Lars Gerne <lage2104@gmail.com>
Co-authored-by: Lars Gerne <lage2104@gmail.com>
Co-authored-by: Lars Gerne <lage2104@gmail.com>
- sh optimizer: add support for budget type "epoch" - train_statistics: add support to be able to track fidelities Co-authored-by: Lars Gerne <lage2104@gmail.com>
+added hash to dict +try different checkpointer
for now, just prototyping
Co-authored-by: Lars Gerne <lage2104@gmail.com>
but one thing make no sense
Co-authored-by: Lars Gerne <lage2104@gmail.com>
lage2104
requested review from
arberzela,
crwhite14 and
yashsmehta
as code owners
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@TillFetzer and myself (@lage2104 ) implemented 4 multi-fidelity optimizers to NASLib.
These are: Successive Halving, Hyperband, Bayesian Optimization Hyperband and Differential Evolution Hyperband.
The implementation is mainly based on https://github.com/automl/nas-bench-x11.
Their implementation has been improved to run stable in NASLib.