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OneFlow

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

Simple CI Nightly Docker Image Nightly Release Documentation

Latest News

Publication

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

Clone Source Code

  • 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
Build OneFlow

  • 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

Troubleshooting

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

Getting Started

Documentation

Model Zoo and Benchmark

Communication

The Team

OneFlow was originally developed by OneFlow Inc and Zhejiang Lab.

License

Apache License 2.0