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20a155d03d
Similar to the recent change to HyperOpt (#https://github.com/ray-project/ray/pull/3944) this implements both: 1. The ability to pass in initial parameter suggestion(s) to be run through Tune first, before using the Optimiser's suggestions. This is for when you already know good parameters and want the Optimiser to be aware of these when it makes future parameter suggestions. 2. The same as 1. but if you already know the reward value for those parameters you can pass these in as well to avoid having to re-run the experiments. In the future it would be nice for Tune to potentially support this functionality directly by loading previously run Tune experiments and initialising the Optimiser with these (kind of like a top level checkpointing functionality) but this feature allows users to do this manually for now. |
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| .. | ||
| benchmarks | ||
| ray | ||
| asv.conf.json | ||
| build-wheel-macos.sh | ||
| build-wheel-manylinux1.sh | ||
| CMakeLists.txt | ||
| README-benchmarks.rst | ||
| README-building-wheels.md | ||
| setup.py | ||