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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

  OneFlow	CUDA Driver Version
  oneflow_cu110	>= 450.36.06
  oneflow_cu102	>= 440.33
  oneflow_cu101	>= 418.39
  oneflow_cu100	>= 410.48
  oneflow_cu92	>= 396.26
  oneflow_cu91	>= 390.46
  oneflow_cu90	>= 384.81
  oneflow_cpu	N/A

  • 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.

  • Support for latest stable version of CUDA will be prioritized. Please upgrade your Nvidia driver to version 440.33 or above and install oneflow_cu102 if possible.

  • We are sorry that due to limits on bandwidth and other resources, we could only guarantee the efficiency and stability of oneflow_cu102. We will improve it ASAP.

• To install latest release of OneFlow with CUDA support:

  python3 -m pip install --find-links https://oneflow-inc.github.io/nightly oneflow_cu102 --user
  

• To install latest release of CPU-only OneFlow:

  python3 -m pip install --find-links https://oneflow-inc.github.io/nightly oneflow_cpu --user
  

• To install OneFlow with legacy CUDA support, run one of:

  python3 -m pip install --find-links https://oneflow-inc.github.io/nightly oneflow_cu101 --user
  python3 -m pip install --find-links https://oneflow-inc.github.io/nightly oneflow_cu100 --user
  python3 -m pip install --find-links https://oneflow-inc.github.io/nightly oneflow_cu92 --user
  python3 -m pip install --find-links https://oneflow-inc.github.io/nightly oneflow_cu91 --user
  python3 -m pip install --find-links https://oneflow-inc.github.io/nightly oneflow_cu90 --user
  

• 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. System Requirements to Build OneFlow

   • Please use a newer version of CMake to build OneFlow. You could download cmake release from here.

   • Please make sure you have G++ and GCC >= 4.8.5 installed. Clang is not supported for now.

   • To install dependencies, run:

     yum-config-manager --add-repo https://yum.repos.intel.com/setup/intelproducts.repo && \
     rpm --import https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB && \
     yum update -y && yum install -y epel-release && \
     yum install -y intel-mkl-64bit-2020.0-088 nasm swig rdma-core-devel
     

     On CentOS, if you have MKL installed, please update the environment variable:

     export LD_LIBRARY_PATH=/opt/intel/lib/intel64_lin:/opt/intel/mkl/lib/intel64:$LD_LIBRARY_PATH
     

     If you don't want to build OneFlow with MKL, you could install OpenBLAS:

     sudo yum -y install openblas-devel
     

2. Clone Source Code

   Clone source code and submodules (faster, recommended)

   git clone https://github.com/Oneflow-Inc/oneflow
   cd oneflow
   git submodule update --init --recursive
   

   Or you could also clone the repo with --recursive flag to clone third_party submodules together

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

3. Build and Install OneFlow

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

   • 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