While I am testing if tuning more trials could make the result more intuitive,
I would like to first ask for the feedbacks about the naming. Here are my
thoughts based on Tianqi's comments.
- select.py -> pass.py
As suggested, this module is more like a pass over a set of tasks, so we can
treat it as a pass. I can implement it as a pass table so that we can add other
passes in the future.
- Select representative tasks. Origianl: `autotvm.task.mark_depend(tasks)`
With the pass implemented above, this API becomes
`autotvm.task.pass.FindReference(tasks)`, meaning that this pass is going to
find the reference task for each task.
- task.depend -> task.cfg_ref_task
`cfg_ref_task` points to the task that we can refer its tuned configs when
tuning.
- tuner.depend_mode -> tuner.cfg_ref_mode
`cfg_ref_mode` is a string in the `<mode>-<cfg>` format. `<mode>` can be either
"only" or "start"; while `<cfg>` can be either "topN" or "N%". Here are some
examples:
- "only-top10": Stop tuning after trying the top 10 configs in the
`cfg_ref_task`.
- "start-5%": First try the top 5% configs in the `cfg_ref_task` and back
to the normal tuning.
- The default is set to "only-top10", meaning that we will stop tuning
after 10 trials if the task we are tuning has a tuned reference task.
@kevinthesun @eqy @icemelon9 please let me know what you guys think about these
names, and you're welcome to propose better ones. Thanks.
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