spaGO VS data-components-contrib

Take a look into https://github.com/nlpodyssey/spago. If you don't need GPU processing it could fit your needs

CGO? Too much overhead in calling C functions (in which you can wrap libtorch or TF C++ code). And too much struggling woth CUDA (actually all GPU stuff). But, there are interesting attempts: https://github.com/gorgonia/gorgonia (I love it most), https://github.com/sugarme/gotch (bindings to libtorch), https://github.com/nlpodyssey/spago.

From data connectors, to REST API, to AI engine, we've now rebuilt Spice.ai's data processing and transport on the Apache Arrow project. Specifically, using the Apache Arrow for Go implementation. Many thanks to Matt Topol for his contributions to the project and guidance on using it.

Before v0.3.1-alpha, data was required to include a specific time field. In v0.3.1-alpha, the JSON and CSV data processors now support the ability to select a specific field to populate the time field. An example selector to use the created_at column for time is:

Highlights include initial support for interpretations and the addition of a new Json Data Processor which enables observations to be posted in JSON to a new Dataspaces API. The ability to post observations directly to the Dataspace also now makes Data Connectors optional.

We chose them because they were fairly straightforward to implement and different enough from one another that we could ensure our interface generalized well.

Re benchmarking - at this point we're looking to show directionality, not necessarily blinding speed. We intend to get the tooling feeling right, then work to optimize perf.

Right now, training data comes from the local disk, InfluxDB, or can be piped in from your application via our API. We're looking to build out a set of community-driven components for streaming and processing data. You can learn more about that here - https://github.com/spiceai/data-components-contrib

We'd love for you to contribute!