# Deep Learning Framework

* Pathways: Asynchronous Distributed Dataflow for ML (MLSys 2022) \[[Paper](https://mlsys.org/virtual/2022/oral/2146)]
  * Google
  * **Outstanding Paper Award**
* OneFlow: Redesign the Distributed Deep Learning Framework from Scratch (arXiv 2110.15032) \[[Paper](https://arxiv.org/abs/2110.15032)] \[[Code](https://github.com/Oneflow-Inc/oneflow)] \[[中文官网](https://www.oneflow.org/index.html)]
  * OneFlow
* Jittor: a novel deep learning framework with meta-operators and unified graph execution (Science China Information Sciences 2020) \[[Paper](http://scis.scichina.com/en/2020/222103.pdf)] \[[Code](https://github.com/Jittor/Jittor)] \[[中文主页](https://cg.cs.tsinghua.edu.cn/jittor/)]
  * THU
* PyTorch: An Imperative Style, High-Performance Deep Learning Library (NeurIPS 2019) \[[Paper](https://papers.nips.cc/paper_files/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html)] \[[Code](https://github.com/pytorch/pytorch)] \[[Homepage](https://pytorch.org/)]
  * FAIR
* XDL: An Industrial Deep Learning Framework for High-dimensional Sparse Data (DLP-KDD 2019) \[[Paper](https://dl.acm.org/doi/10.1145/3326937.3341255)] \[[Code](https://github.com/alibaba/x-deeplearning)]
  * Alibaba
  * High-dimensional sparse data.
* TensorFlow: A System for Large-Scale Machine Learning (OSDI 2016) \[[Paper](https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi)] \[[Code](https://github.com/tensorflow/tensorflow)] \[[Homepage](https://www.tensorflow.org/)]
  * Google Brain
* MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems (NIPS 2016 Workshop on MLSys) \[[Paper](https://arxiv.org/abs/1512.01274)] \[[Homepage](https://mxnet.apache.org/)] \[[Code](https://github.com/apache/mxnet)]
* Caffe: Convolutional Architecture for Fast Feature Embedding (arXiv 1408.5093) \[[Paper](https://arxiv.org/abs/1408.5093)] \[[Homepage](http://caffe.berkeleyvision.org/)] \[[Code](https://github.com/BVLC/Caffe/)]
  * UC Berkeley
* An Introduction to Computational Networks and the Computational Network Toolkit (MSR-TR-2014-112) \[[Paper](https://www.microsoft.com/en-us/research/publication/an-introduction-to-computational-networks-and-the-computational-network-toolkit/)] \[[Code](https://github.com/microsoft/CNTK)] \[[Homepage](https://learn.microsoft.com/en-us/cognitive-toolkit/)]
  * Microsoft
  * CNTK
  * No longer developed.