oneflow VS Gorgonia

We are thrilled to announce the new release of OneFlow,, which is a deep learning framework designed to be user-friendly, scalable and efficient. OneFlow v0.9.0 contains 640 commits. For the full changelog, please check out: https://github.com/Oneflow-Inc/oneflow/releases/tag/v0.9.0.

Found relevant code at https://github.com/Oneflow-Inc/oneflow + all code implementations here

Check out OneFlow on GitHub . We'd love to hear your feedback!

OneFlow URL：https://github.com/Oneflow-Inc/oneflow/

No other options?:) We are developing a new distributed DL framework called OneFlow, which is faster than other frameworks and easier to use. Now it provides more and better PyTorch compatible APIs.

Code for https://arxiv.org/abs/2110.15032 found: https://github.com/Oneflow-Inc/oneflow

This article focuses on what is happening behind the execution of a Tensor in the deep learning framework OneFlow. It takes the operator oneflow.relu as an example to introduce the Interpreter and VM mechanisms that need to be relied on to execute this operator.

This article describes how OneFlow works with MLIR, how to add a graph-level Pass to OneFlow IR, how OneFlow Operations automatically become MLIR Operations, and why OneFlow IR can use MLIR to accelerate computations.

Backpressure mechanism, also known as credit-based flow control, is a classic scheme for network communication flow control problems. Its predecessor is the TCP sliding window. This idea is particularly simple and effective. As we will see in this article, based on the same principles, this idea is applicable to any flow control scheme and is found in the design of many hardware and software systems. In this article, the engineer of OneFlow will tell the chequered history of this simple idea.

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...

Yep, all of them: https://github.com/gorgonia/gorgonia