Gorgonia VS tfgo

For Go specifically, there are some libraries like Gorgonia (https://github.com/gorgonia/gorgonia) that can do inference.

Practically speaking though, the rate at which models change is so fast that if you opt to go this route, you'll perpetually be lagging behind the state of the art by just a bit. Either you'll be the one implementing the latest improvements or be waiting for the framework to catch up. This is the real value of the sidecar approach: when a new technique comes out (like speculative decoding, for example) you don't need to reimplement it in Go but instead can use the implementation that most other python users will use.

GitHub - gorgonia/gorgonia: Gorgonia is a library that helps facilitate machine learning in Go.

I did end up writing and using a custom library for Random Forest (it's also in AwesomGo) in one real-world project (detecting Alzheimer's and Parkinson's from speech from a mobile app) - https://github.com/malaschitz/randomForest I had better results than the team who used TensorFlow and most importantly I didn't have to use any other technology than Go. For NN's it's probably best to use https://gorgonia.org/ - but it's not exactly a user friendly library. But there is a whole book on it - Hands-On Deep Learning with Go.

Perhaps it's a product of French culture, but because Gorgonia[0] has a number of French contributors, this was actually the way we structured our documentation.

But this is the first time I've heard of the name of the framework.

[0]: https://gorgonia.org

Oh it's.Gorgonia

Website: https://gorgonia.org

I use Gorgonia.

What you think about this https://github.com/gorgonia/gorgonia ? I also recall there is something else out there but can't find it at the moment...

eh, awesome! Seems this one, right? https://github.com/galeone/tfgo. Quite many stars.

I'm excited to share a project I've been working on: [GoFaceRec](https://github.com/modanesh/GoFaceRec). This is a face recognition tool built in Go, leveraging the power of MTCNN for face detection and QMagFace for face recognition. The project was born out of a desire to bring the power of deep learning models to the Go community. After much effort, I concluded that the best approach was to convert models to TensorFlow and then work with tfgo, a Go binding to TensorFlow's C API. In GoFaceRec, the input image is first processed, and then its embeddings are compared against the ones already computed from our dataset. If the distance between embeddings falls below a specific threshold, then the face is considered as unknown. Otherwise, the proper label will be printed. The project is tested using Go 1.17 on Ubuntu 20.04. For gocv, the version of OpenCV installed is 4.7. And for tfgo, I installed [this version](https://github.com/galeone/tfgo) instead of the official one. You can install this package by running the following command in your project: > go get github.com/modanesh/[email protected] You can find more detailed instructions on how to use the tool in the [GitHub repository](https://github.com/modanesh/GoFaceRec). I welcome any feedback, suggestions, or contributions to the project. I'm looking forward to seeing how the community uses GoFaceRec and hope it can be a valuable tool for those working on face recognition tasks. Happy coding! 🚀

Paolo Galeone has improved bindings (tfgo) that can be used for training and deployment.

https://github.com/galeone/tfgo here is a very good tutorial. I would suggest starting there.

Tensorflow is actually written in C++, and the python package is just bindings to tensorflow. There are Tensorflow Go bindings: https://github.com/galeone/tfgo.

have you tried your hands at [galeone/tfgo](https://github.com/galeone/tfgo); I've just hello-world it... so can't vouch on efficiency