hummingbird VS GmsCore

If are interested in just predictions you can try Hummingbird. It is part of the PyTorch ecosystem. We get already trained scikit-learn models and translate them into PyTorch models. From them you can run your model on any hardware support by PyTorch, export it into TVM, ONNX, etc. Performance on hardware acceleration is quite good (orders of magnitude better than scikit-learn is some cases)

I think Rapids AI's cuML tried to go into this direction (essentially scikit-learn on the GPU): https://docs.rapids.ai/api/cuml/stable/api.html#logistic-reg.... For some reason it never took really off though.

Btw., going on a tangent, you might like Hummingbird (https://github.com/microsoft/hummingbird). It allows you trained scikit-learn tree-based models to PyTorch. I watched the SciPy talk last year, and it's a super smart & elegant idea.

ONNX opens an avenue for direct inference using a number of languages and platforms. For example, a model could be run directly on Android to limit data sent to a third party service. ONNX is an exciting development with a lot of promise. Microsoft has also released Hummingbird which enables exporting traditional models (sklearn, decision trees, logistical regression..) to ONNX.

And Python.

For inference, you can convert your models to other formats that support GPU acceleration. See Hummingbird https://github.com/microsoft/hummingbird

The surprising thing is that Hummingbird can be faster than the GPU implementation of LightGBM (and XGBoost) if you use tensor compilers such as TVM. [The paper](https://www.usenix.org/conference/osdi20/presentation/nakandala) describes our findings. We have also open sourced the [benchmark code](https://github.com/microsoft/hummingbird/tree/main/benchmarks) so you try yourself!

I took their sample code from Github and tweaked it to spit out times for each model's prediction, as well as increase the number of rows to 5 million. I used Google's Colab and selected GPU for my hardware accelerator. This gives an option to run code on GPU, not that all computations will happen on the GPU.

Is anyone here daily-driving microg and can share their experiences? https://github.com/microg/GmsCore/wiki/Implementation-Status does not exactly inspire confidence.

...will need to be rewritten to avoid Google Play Services.

Not true.

All that needs to happen is for open source developers to "re-implement Google’s proprietary Android user space apps and libraries".

https://microg.org/

microG itself connects directly to Google: https://github.com/microg/GmsCore/wiki/Google-Network-Connec...

No shit, of course they do.

>In general, we obviously try to minimize the connections to Google, but some services strictly rely on them and would just not work without.

What exactly do you think they should do instead?

MicroG

In release notes for GmsCore v0.2.29.233013 (https://github.com/microg/GmsCore/releases/tag/v0.2.29.233013), I also see:

Yes, the Xposed module is one way. There are also other ways

Which one should I use? Is this MicroG's official website right? (https://microg.org/)

Have a look into https://microg.org/ . Revanced yt uses a fork of GmsCore for its non-root install, though you still have to log in with a google account.