OneFlow

OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient. With OneFlow, it is easy to:

• program a model with PyTorch-like API
• scale a model to n-dimensional-parallel execution with the Global Tensor
• accelerate/deploy a model with the Graph Compiler.

Latest News

• Version 1.0.0 is out!
  • Full changelog

Publication

• OneFlow: Redesign the Distributed Deep Learning Framework from Scratch

System Requirements

General

• Linux
• Python 3.7, 3.8, 3.9, 3.10, 3.11

CUDA

• CUDA arch 60 or above

• CUDA Toolkit version 10.0 or above

• Nvidia driver version 440.33 or above

  OneFlow will work on a minimum supported driver, and any driver beyond. For more information, please refer to CUDA compatibility documentation.

Install

Preinstall docker image

docker pull oneflowinc/oneflow:nightly-cuda11.8

Pip Install

• (Highly recommended) Upgrade pip

  python3 -m pip install --upgrade pip #--user
  

• To install latest stable release of OneFlow with CUDA support:

  python3 -m pip install oneflow
  

• To install nightly release of OneFlow with CPU-only support:

  python3 -m pip install --pre oneflow -f https://oneflow-staging.oss-cn-beijing.aliyuncs.com/branch/master/cpu
  

• To install nightly release of OneFlow with CUDA support:

  python3 -m pip install --pre oneflow -f https://oneflow-staging.oss-cn-beijing.aliyuncs.com/branch/master/cu118
  

  If you are in China, you could run this to have pip download packages from domestic mirror of pypi:

  python3 -m pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
  

  For more information on this, please refer to pypi 镜像使用帮助

Install from Source

<details> <summary>Clone Source Code</summary>

• Option 1: Clone source code from GitHub

  git clone https://github.com/Oneflow-Inc/oneflow.git
  

• Option 2: Download from Aliyun(Only available in China)

  curl https://oneflow-public.oss-cn-beijing.aliyuncs.com/oneflow-src.zip -o oneflow-src.zip
  unzip oneflow-src.zip
  

  </details>

<details> <summary>Build OneFlow</summary>

• Install dependencies

  apt install -y libopenblas-dev nasm g++ gcc python3-pip cmake autoconf libtool
  

  These dependencies are preinstalled in offical conda environment and docker image, you can use the offical conda environment here or use the docker image by:

  docker pull oneflowinc/manylinux2014_x86_64_cuda11.2
  

• In the root directory of OneFlow source code, run:

  mkdir build
  cd build
  

• Config the project, inside build directory:

  • If you are in China

    config for CPU-only like this:

    cmake .. -C ../cmake/caches/cn/cpu.cmake
    

    config for CUDA like this:

    cmake .. -C ../cmake/caches/cn/cuda.cmake -DCMAKE_CUDA_ARCHITECTURES=80 -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda -DCUDNN_ROOT_DIR=/usr/local/cudnn
    

  • If you are not in China

    config for CPU-only like this:

    cmake .. -C ../cmake/caches/international/cpu.cmake
    

    config for CUDA like this:

    cmake .. -C ../cmake/caches/international/cuda.cmake -DCMAKE_CUDA_ARCHITECTURES=80 -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda -DCUDNN_ROOT_DIR=/usr/local/cudnn
    

    Here the DCMAKE_CUDA_ARCHITECTURES macro is used to specify the CUDA architecture, and the DCUDA_TOOLKIT_ROOT_DIR and DCUDNN_ROOT_DIR macros are used to specify the root path of the CUDA Toolkit and CUDNN.

• Build the project, inside build directory, run:

  make -j$(nproc)
  

• Add oneflow to your PYTHONPATH, inside build directory, run:

  source source.sh
  

  Please note that this change is not permanent.

• Simple validation

  python3 -m oneflow --doctor
  

  </details>

Troubleshooting

Please refer to troubleshooting for common issues you might encounter when compiling and running OneFlow.

Getting Started

• Please refer to QUICKSTART
• 中文版请参见 快速上手

Documentation

• API Reference
• Usage & Design Docs
• System Design

Model Zoo and Benchmark

• Libai(Toolbox for Parallel Training Large-Scale Transformer Models)
  • BERT-large
  • GPT
  • T5
  • VisionTransformer
  • SwinTransformer
• FlowVision(Toolbox for Computer Vision Datasets, SOTA Models and Utils)
• OneFlow-Models(Outdated)
  • ResNet-50
  • Wide&Deep
• OneFlow-Benchmark(Outdated)

Communication

• GitHub issues: any install, bug, feature issues.

• www.oneflow.org: brand related information.

• 中文

  • QQ 群: 331883
  • 微信号（加好友入交流群）: OneFlowXZS
  • 知乎

• International

  • Discord
  • Twitter
  • LinkedIn
  • Medium

The Team

OneFlow was originally developed by OneFlow Inc and Zhejiang Lab.

License

Apache License 2.0