tonic VS neuronika

Use tonic-build directly from Rust.

} // Omit the remaining code and refer to the example in Tonic: https://github.com/hyperium/tonic/blob/master/examples/src/multiplex/server.rs ```

If I’m not misunderstanding, you could both decode the gRPC protobuf AND write to delta lake in Rust. Tonic, Delta-rs.

Original post: https://github.com/hyperium/tonic/issues/1405. Cross-post here in case the problem is not specific to tonic.

Tonic

Just to double check Is this the correct repo?

Wasm isn't involved much actually. Basically, the frontend asks the backend to perform a gRPC call on its behalf using Tauri events. They are like named channels on which you can send any serde-compatible value. But the backend is a normal Rust program so there are no constraints there. I use prost-reflect to encode/decode Protobuf messages according to Protobuf descriptors loaded at runtime and make the actual requests using tonic from the tokio ecosystem. prost-reflect is necessary because, normally, tonic expects the Protobuf descriptor to be known at compile time so it can make some code generation behind the scenes.

You could have a look at gRPC i.e. https://github.com/hyperium/tonic

Looking at this example code: https://github.com/hyperium/tonic/blob/master/examples/src/tower/server.rs

The remaining gap is remote actors, since you still need some kind of serialization between them, and take your pick of standards for that one such as gRPC using Tonic.

Also perhaps comparing to Neuronika.

how does it compare with https://github.com/spearow/juice, https://github.com/neuronika/neuronika and https://github.com/spearow/juice?

Check where and how this struct is used. https://github.com/neuronika/neuronika/blob/variable-rework/neuronika-variable/src/history.rs

Also you can take a look at neuronika.

There is also https://github.com/neuronika/neuronika

I have a question: as the maintainer of [neuronika](https://github.com/neuronika/neuronika), a crate that offers dynamic neural network and auto-differentiation with dynamic graphs, I'm looking at a future possible feature for such framework consisting in the possibility of compiling models, getting thus rid of the "dynamic" part, which is not always needed. This would speed the inference and training times quite a bit.

If you like autograd you will love neuronika

After reading most of the book, I wanted to get my hands dirty. My initial idea was to build a small machine learning framework but I deemed it to be too difficult if not impossible for me at the time. (Now, neuronika would have something to say). When gathering the bibliography for my thesis, I recalled to have stumbled upon a particular algorithm, t-SNE, whom I liked very much. I found the idea behind it to be very clever and elegant (t-SNE it's still one of my favorite algorithms, together with backprop and SOM, I find manifold learning fascinating in general). "So be it", I said, and I began writing a mess of a code, that was basically a translation of the C++ implementation. Boy was it bad.