Hello Anton, Is this for TensorFlow only or for ML algorithms Ignite supplies out of the box? Also, do you need C++ for the training phase?
-- Denis On Mon, Dec 17, 2018 at 2:02 AM dmitrievanth...@gmail.com < dmitrievanth...@gmail.com> wrote: > Currently ML/TensorFlow module requires an ability to expose some > functionality to be used in C++ code. > > As far as I understand, currently Ignite provides an ability to work with > it from C++ only through the Thin Client. The list of operations supported > by it is very limited. What is the best approach to work with additional > Ignite functionality (like ML/TensorFlow) from C++ code? > > I see several ways we can do it: > 1. Extend list of Thin Client operations. Unfortunately, it will lead to > overgrowth of API. As result of that it will be harder to implement and > maintain Thin Clients for different languages. > 2. Use Thin Client as a "transport layer" and make Ignite functionality > calls via puts/gets commands/responses into/from cache (like command > pattern). It's looks a bit confusing to use cache with put/get operations > as a transport. > 3. Add custom endpoint that will listen specific port and process custom > commands. It will introduce a new endpoint and a new protocol. > > What do you think about these approaches? Could you suggest any other ways? > > To have more concrete discussion lets say we need to functions available > from C++: "saveModel(name, model)", "getModel(name)" already implemented in > Ignite ML and available via Java API. >
