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From Igor Sapego <isap...@apache.org>
Subject Re: What is the best approach to extend Thin Client functionality?
Date Mon, 17 Dec 2018 10:43:55 GMT

First of all, it would help to know what is the functionality you need,
to give you an answer. Can you scribed required API?

Best Regards,

On Mon, Dec 17, 2018 at 1:02 PM dmitrievanthony@gmail.com <
dmitrievanthony@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.

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