ivy VS tfgo

See also https://github.com/unifyai/ivy which I have not tried but seems along the lines of what you are describing, working with all the major frameworks

is this ancillary to what [these guys](https://github.com/unifyai/ivy) are trying to do?

https://unify.ai/ They are trying to do what Ivy is doing already.

Check Ivy.

ROCm's great for data centers, but good luck finding anything about desktop GPUs on their site apart from this lone blog post: https://community.amd.com/t5/instinct-accelerators/exploring...

There's a good explanation of AMD's ROCm targets here: https://news.ycombinator.com/item?id=28200477

It's currently a PITA to get common Python libs like Numba to even talk to AMD cards (admittedly Numba won't talk to older Nvidia cards either and they deprecate ruthlessly; I had to downgrade 8 versions to get it working with a 5yo mobile workstation). YC-backed Ivy claims to be working on unifying ML frameworks in a hardware-agnostic way but I don't have enough experience to assess how well they're succeeding yet: https://lets-unify.ai

I was happy to see DiffusionBee does talk the GPU in my late-model intel Mac, though for some reason it only uses 50% of its power right now. I'm sure the situation will improve as Metal 3.0 and Vulkan get more established.

Won't it all come together with https://lets-unify.ai/ ?

What do you think about unify AI https://lets-unify.ai.

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