@mbarrett97 I see your point. If the problem is narrowed down to "skip some tasks in a model when resuming the tuning that was accidently interrupted", then your proposal is a lightweight working solution. Maybe we can file another RFC focusing on a more general history reuse support.
Then talking back to your proposal, the current solution is using `config_library` in the `log_to_file` callback so that it will store configs as well as the trial number for each layer (task). According to your reply, are you going to store the best config for each layer only? I didn't see the corresponding implementation in your PR, tho (please correct me if I missed it). If config library only stores the best one for the sake of tuning performance and disk space, how do I store all configs like now if I prefer? In addition, I don't think storing all explored configs is a problem. I stored the whole history all the time for the research purpose and didn't feel any performance problem. For the disk space, my `history.json` for 2000 records is about 1.3M. Taking mobilenet v2 for instance, it has 31 tasks in total, meaning 40.3M history file. I think this not a big problem for the modern disk. -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/dmlc/tvm/issues/4150#issuecomment-544713918
