OneFlow is a performance-centered and open-source deep learning framework.
- Install OneFlow
- Getting Started
- Model Zoo and Benchmark
- The Team
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
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
Support for latest stable version of CUDA will be prioritized. Please upgrade your Nvidia driver to version 440.33 or above and install
oneflow_cu102if 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
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:
If you don't want to build OneFlow with MKL, you could install OpenBLAS:
sudo yum -y install openblas-devel
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
--recursiveflag to clone third_party submodules together
git clone https://github.com/Oneflow-Inc/oneflow --recursive
Build and Install OneFlow
cd build cmake .. make -j$(nproc) make pip_install
Please refer to troubleshooting for common issues you might encounter when compiling and running OneFlow.
You can check this doc to obtain more details about how to use XLA and TensorRT with OneFlow.
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
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.
Usage & Design Docs
Model Zoo and Benchmark
- Github issues : any install, bug, feature issues.
- www.oneflow.org : brand related information.
OneFlow was originally developed by OneFlow Inc and Zhejiang Lab.
Apache License 2.0