I guess you've used RandomTuner or GridTuner, which traverses the whole search space randomly or in sequence.
As for the ML part of the AutoTVM, it means to use the XGBTuner in the current code base. With which, AutoTVM extracts features from a given schedule and uses a XGBoost model to predict its performance, then relies on the simulated annealing to pick a better config from the search space. Check the [AutoTVM paper](http://papers.nips.cc/paper/7599-learning-to-optimize-tensor-programs) or the related code in TVM repo for more details. --- [Visit Topic](https://discuss.tvm.apache.org/t/now-that-we-have-autotvm-why-we-need-topi/7886/4) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/5750f6f3df66592c190baa3672c96a166c215fe055b3853bc811ce896a3ec520).
