I also found this thread: https://github.com/sherpa-ai/sherpa/issues/19
It seems like the authors are open to relicensing if somebody brings up good arguments. -Marco Marco de Abreu <[email protected]> schrieb am Do., 14. März 2019, 00:07: > Thanks for elaborating the details of this framework, it seems to be very > interesting! > > One concern I could see with the GPL is the usage restrictions it imposes. > As you correctly stated, it wouldn't be an issue for us as a project > because we are the upstream library, but I would see compliance issues for > our end-users. Most companies have a clear policy against the usage of GPL > licensed software. Thus, if we would make this framework one of our > preferred choices and recommendations, most users would still not be > allowed to use it. > > It's a pity, but I think we should try to stay away from category X > licensed software as much as possible to avoid bringing our end users (or > the project) into compliance issues. > > One recommendation though could be to approach the researchers and ask > them if they'd be interested to alter the license to a compatible one. > Considering we got Apache and a few other big companies behind MXNet, we > might be able to convince them if we could lay out the benefits that change > would give to both ecosystems. > > -Marco > > Anirudh Acharya <[email protected]> schrieb am Mi., 13. März 2019, > 23:43: > >> Hi All, >> >> I posted this earlier on the mxnet slack channel, based on a suggestion >> there I am reposting it here for a wider audience - >> >> I was searching for ways of performing HPO for models built with MXNet, >> and I came across Sherpa, an open source distributed HPO library presented >> in NeurIPS 2018 - https://openreview.net/pdf?id=HklSUMyJcQ. >> >> I have been trying it out and it is very easy to use and extensible. It >> already supports RandomSearch, Grid Search and BayesianOpt for performing >> the search in the hyper-parameter space. >> >> I have submitted a PR with an example gluon use-case - >> https://github.com/sherpa-ai/sherpa/pull/27 But I am yet to try it with >> large distributed training use cases. But the library does support it, we >> can run it in distributed mode for running heavy workloads. >> >> It also comes with a neat UI dashboard to monitor the jobs being run. >> >> [image: Screen Shot 2019-03-13 at 8.08.48 AM.png] >> >> I think we should explore this as an option for performing HPO with gluon. >> >> What might integration entail - >> 1. I have not fully evaluated what changes might be necessary but I think >> the integration can be fairly unobtrusive for both repositories. As >> demonstrated above we can already use sherpa for performing HPO, but the >> experience is a bit clunky. It can be made smooth by adding a few callback >> functions that will track and log the metrics of the different experiment >> runs( å la the keras callback function defined here - >> https://github.com/sherpa-ai/sherpa/blob/master/sherpa/core.py#L368 ) >> >> 2. The library is developed and maintained by folks in academia and is >> published under GPL license. I was given to understand that GPL license >> might be a problem for Apache products, but since we are not explicitly >> using it within mxnet as a sub-component, I am thinking we might have some >> wiggle room there. >> >> MXNet needs HPO functionality and instead of building something from >> scratch we could just use existing open source projects. Would like to hear >> more from the community. >> >> Thanks >> Anirudh Acharya >> >>
