econben / oneflow

OneFlow is a performance-centered and open-source platform for machine learning.

OneFlow is a performance-centered and open-source platform for machine learning.

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

Install OneFlow

System Requirements

• Python >= 3.5
• Nvidia Linux x86_64 driver version >= 440.33

Install with Pip Package

• 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 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
  

• 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. For more information, please refer to CUDA compatibility documentation.

• CPU-only OneFlow is not available for now.

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

Build from Source

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
   

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.

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.

Documentation

Usage & Design Docs

• link

API Reference

• link

Model Zoo and Benchmark

• link

CNNs(ResNet-50, VGG-16, Inception-V3, AlexNet)

• CNNs

Wide&Deep

• OneFlow-WDL

BERT

• Bert

Communication

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

Contributing

• link

The Team

OneFlow was originally developed by OneFlow Inc and Zhejiang Lab.

License

Apache License 2.0