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

• Install OneFlow
  • System Requirements
  • Install with Pip Package
  • Build from Source
  • Troubleshooting
  • Advanced features
• Getting Started
• Documentation
  • Usage & Design Docs
  • API Reference
  • OneFlow System Design
• Model Zoo and Benchmark
  • CNNs(ResNet-50, VGG-16, Inception-V3, AlexNet)
  • Wide&Deep
  • BERT
• Communication
• Contributing
• The Team
• License

• Python >= 3.5

• 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.

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

  python3 -m pip install -f https://release.oneflow.info oneflow_cu102 --user
  

• To install nightly release of OneFlow with CUDA support:

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

• To install other available builds for different variants:

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

  All available [PLATFORM]:

  Platform	CUDA Driver Version	Supported GPUs
  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

• To install latest release of CPU-only OneFlow:

  python3 -m pip install --find-links https://release.oneflow.info oneflow_cpu --user
  

• To install legacy version of OneFlow with CUDA support:

  python3 -m pip install --find-links https://release.oneflow.info oneflow_cu102==0.3.1 --user
  

  Some legacy versions available: 0.1.10, 0.2.0, 0.3.0, 0.3.1

• 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

• Releases are built with G++/GCC 4.8.5, cuDNN 7 and MKL 2020.0-088.

1. 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

2. Build and Install OneFlow

   • Option 1: Build in docker container (recommended)

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

       python3 docker/package/manylinux/build_wheel.py
       

       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 2: Build on bare metal

     • Install dependencies. For instance, on Ubuntu 20.04, run:

       sudo apt install -y libmkl-full-dev nasm libc++-11-dev libncurses5 g++ gcc cmake gdb python3-pip
       

       If there is a prompt, it is recommended to select the option to make mkl the default BLAS library.

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

       mkdir build
       cd build
       cmake ..
       make -j$(nproc)
       make pip_install
       

     • If you are in China, please add this CMake flag -DTHIRD_PARTY_MIRROR=aliyun to speed up the downloading procedure for some dependency tar files.

     • For pure CPU build, please add this CMake flag -DBUILD_CUDA=OFF.

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

• XRT

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

3 minutes to run MNIST.

1. 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/

2. Run it in Python

python mlp_mnist.py

3. 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.

• link

• link

For those who would like to understand the OneFlow internals, please read the document below:

• link

• link

• CNNs

• OneFlow-WDL

• BERT

• GitHub issues : any install, bug, feature issues.
• www.oneflow.org : brand related information.

• link

OneFlow was originally developed by OneFlow Inc and Zhejiang Lab.

Apache License 2.0