OneFlow is a performance-centered and open-source deep learning framework.
- Install OneFlow
- Getting Started
- Model Zoo and Benchmark
- The Team
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
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.
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 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.
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. On CentOS:
sudo yum -y install openblas-devel
sudo apt install -y libopenblas-dev
Clone Source Code
Option 1: Clone source code from github
git clone https://github.com/Oneflow-Inc/oneflow
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
Build and Install OneFlow
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=aliyunto speed up the downloading procedure for some dependency tar files.
For pure CPU build, please add this CMake flag
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
OneFlow System Design
For those who would like to understand the OneFlow internals, please read the document below:
Model Zoo and Benchmark
CNNs(ResNet-50, VGG-16, Inception-V3, AlexNet)
- Github issues : any install, bug, feature issues.
- www.oneflow.org : brand related information.