Hey Andrew, thanks for the write-up. I think having a Java binding will be very useful for enterprise users. Doc looks good but two things I'm curious about:
How are you planning to handle thread safe inference? It'll be great if you can hide the complexity of dealing with dispatch threading from users. The other thing I think a solid Java API could provide is a limited number of dependencies. There's some simple things we can do to make this happen (create a statically linked, portable so) but there's also some complexity around minimizing dependencies MXNet. For example we'll likely want to release MKL flavoured binaries, we should have a few versions of CUDA supported. We could try and have one version that has an absolute minimum of dependencies (maybe statically linking with openblas). It might be good to document exactly the packages you're planning to release, and give some more details about what the dependencies for the packages would be. Many thanks for looking into this, I think it'll be a big improvement for many of our users. -Kellen On Thu, May 10, 2018, 12:57 AM Andrew Ayres <andrew.f.ay...@gmail.com> wrote: > Hi all, > > There has been a lot of interest expressed in having a Java API for doing > inference. The general idea is that after training a model using python, > users would like to be able to load the model for inference inside their > existing production eco-system. > > We've begun exploring a few options for the implementation at < > https://cwiki.apache.org/confluence/display/MXNET/MXNet+Java+Inference+API > > > and would appreciate any insights/feedback. > > Thanks, > Andrew >
