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 de Abreu <marco.g.ab...@gmail.com> schrieb am Do., 14. März 2019,

> 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 <anirudhk...@gmail.com> 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

Reply via email to