OneFlow

OneFlow is a performance-centered and open-source deep learning framework.

Latest News

• Version 0.4.0 is out!
  • New Pytorch flavored APIs (import oneflow.experimental as flow)
  • New models and examples
  • Full changelog

Install OneFlow

System Requirements

• Python 3.6, 3.7, 3.8 (3.9 is available in nightly version)

• (Highly recommended) Upgrade pip

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

• CUDA Toolkit Linux x86_64 Driver

  • CUDA runtime is statically linked into OneFlow. OneFlow will work on a minimum supported driver, and any driver beyond. For more information, please refer to CUDA compatibility documentation.

  • Please upgrade your Nvidia driver to version 440.33 or above and install OneFlow for CUDA 10.2 if possible.

Install with Pip Package

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

  python3 -m pip install -f https://release.oneflow.info oneflow==0.4.0+cu102

• To install nightly release of OneFlow with CUDA support:

  python3 -m pip install oneflow -f https://staging.oneflow.info/branch/master/cu102

• To install other available builds for different variants:

  • Stable

    python3 -m pip install --find-links https://release.oneflow.info oneflow==0.4.0+[PLATFORM]

  • Nightly

    python3 -m pip install oneflow -f https://staging.oneflow.info/branch/master/[PLATFORM]
    

  • All available [PLATFORM]:

    Platform	CUDA Driver Version	Supported GPUs
    cu112	>= 450.80.02	GTX 10xx, RTX 20xx, A100, RTX 30xx
    cu111	>= 450.80.02	GTX 10xx, RTX 20xx, A100, RTX 30xx
    cu110, cu110_xla	>= 450.36.06	GTX 10xx, RTX 20xx, A100
    cu102, cu102_xla	>= 440.33	GTX 10xx, RTX 20xx
    cu101, cu101_xla	>= 418.39	GTX 10xx, RTX 20xx
    cu100, cu100_xla	>= 410.48	GTX 10xx, RTX 20xx
    cpu	N/A	N/A

• 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

Build from Source

Clone Source Code

• Option 1: Clone source code from GitHub

  git clone https://github.com/Oneflow-Inc/oneflow --depth=1

• Option 2: Download from Aliyun

  If you are in China, please download OneFlow source code from: https://oneflow-public.oss-cn-beijing.aliyuncs.com/oneflow-src.zip

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

Build OneFlow

• Option 1: Build with Conda (recommended)

  Please refer to this repo

• Option 2: Build in docker container (recommended)

  • Pull a docker image:

    docker pull oneflowinc/oneflow-manylinux2014-cuda10.2:0.1
    

    All images available : https://hub.docker.com/u/oneflowinc

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

    python3 docker/package/manylinux/build_wheel.py --python_version=3.6
    

    This should produce .whl files in the directory wheelhouse

  • If you are in China, you might need to add these flags:

    --use_tuna --use_system_proxy --use_aliyun_mirror
    

  • You can choose CUDA/Python versions of wheel by adding:

    --cuda_version=10.1 --python_version=3.6,3.7
    

  • For more useful flags, plese run the script with flag --help or refer to the source code of the script.

• Option 3: Build on bare metal

  • Install dependencies

    • on Ubuntu 20.04, run:

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

    • on macOS, run:

      brew install nasm
      

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

    mkdir build
    cd build
    

  • Config the project, inside build directory:

    • If you are in China

      run this to config for CUDA:

      cmake .. -C ../cmake/caches/cn/cuda.cmake
      

      run this to config for CPU-only:

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

    • If you are not in China

      run this to config for CUDA:

      cmake .. -C ../cmake/caches/international/cuda.cmake
      

      run this to config for CPU-only:

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

  • 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
    

Troubleshooting

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

Advanced features

XRT

• You can check this doc to obtain more details about how to use XLA and TensorRT with OneFlow.

Getting Started

3 minutes to run MNIST.

• Clone the demo code from OneFlow documentation

  git clone https://github.com/Oneflow-Inc/oneflow-documentation.git
  cd oneflow-documentation/cn/docs/code/quick_start/
  

• Run it in Python

  python mlp_mnist.py
  

• Oneflow is running and you got the training loss

  2.7290366
  0.81281316
  0.50629824
  0.35949975
  0.35245502
  ...
  

• More info on this demo, please refer to doc on quick start.

Documentation

• API Reference
• Usage & Design Docs
• System Design

Model Zoo and Benchmark

• OneFlow-Benchmark
• GPT
• CNNs(ResNet-50, VGG-16, Inception-V3, AlexNet)
• Wide&Deep
• BERT

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