flashlight VS oneflow

I think a comparison to PyTorch, TensorFlow and/or JAX is more relevant than a comparison to CuPy/NumPy.

And then maybe also a comparison to Flashlight (https://github.com/flashlight/flashlight) or other C/C++ based ML/computing libraries?

Also, there is no mention of it, so I suppose this does not support automatic differentiation?

This Facebook ai project seems reasonably structured after looking at its CMakeLists.txt. CMake is a build generator for c++, it's how you make binaries to run your project: https://github.com/flashlight/flashlight

Continue reading | Check out the paper and github link

I built and maintain Flashlight, a C++-first library for ML/DL. We built Flashlight to be:

I’ve never used it, but Facebook’s flashlight looks interesting

Flashlight bro, not flash. Read again

I've had good results with https://github.com/flashlight/flashlight/blob/master/flashli.... Seems to work well with spoken english in a variety of accents. Biggest limitation is that the architecture they have pretrained models for doesn't really work well with clips longer than ~15 seconds, so you have to segment your input files.

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.