oneflow

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

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

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

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_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

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

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
```

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.

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
```
python mlp_mnist.py
```
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

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