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oneflow

Oneflow-Inc/oneflow

OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.

GitHub Website

GraphCanon updated 2w · GitHub synced 2w

9.4k stars1.0k forksLast push 8mo C++ Apache-2.0

Decision brief

OneFlow is a deep learning framework built for user-friendly, scalable, and efficient performance in model training, with support via CUDA installations.

Good fit when

OneFlow is preferable when you need a user-friendly framework for both CPU and CUDA installations, aiming to streamline the deep learning workflow.
OneFlow should be selected when targeting applications requiring high scalability and efficiency without compromising ease of use.

Avoid when

Avoid OneFlow if your project requires extensive customization features not natively supported, as switching to another framework might offer better flexibility.
If the development environment lacks support for CUDA or Python3-based installation methods, consider an alternative framework that suits your hardware and software environment more closely.

Observed Jul 12, 2026 · Source: enrich:decision_facts

oneflow alternatives Trust report

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Maintenance and security

Full trust report

Maintenance: Slowing (242d since push); As of 2w
Provenance: Not a fork · Organization account; As of 2w
Security (OSV): No lockfile; As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

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

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

A deep learning framework focusing on user-friendliness, scalability, and efficiency, supporting both CPU and CUDA installations.

Capability facts

Languages: c++
Source: github.language · Aug 3, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Aug 3, 2026)

python3 -m pip install --upgrade pip #--user

Source link

README

Preinstall docker image

docker pull oneflowinc/oneflow:nightly-cuda11.8

Pip Install

(Highly recommended) Upgrade pip

python3 -m pip install --upgrade pip #--user

To install latest stable release of OneFlow with CUDA support:
```
python3 -m pip install oneflow
```

To install nightly release of OneFlow with CPU-only support:

python3 -m pip install --pre oneflow -f https://oneflow-staging.oss-cn-beijing.aliyuncs.com/branch/master/cpu

To install nightly release of OneFlow with CUDA support:
```
python3 -m pip install --pre oneflow -f https://oneflow-staging.oss-cn-beijing.aliyuncs.com/branch/master/cu118
```
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

Install from Source

Clone Source Code

Option 1: Clone source code from GitHub

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

Option 2: Download from Aliyun(Only available in China)

curl https://oneflow-public.oss-cn-beijing.aliyuncs.com/oneflow-src.zip -o oneflow-src.zip
unzip oneflow-src.zip

Build OneFlow

Install dependencies
```
apt install -y libopenblas-dev nasm g++ gcc python3-pip cmake autoconf libtool
```
These dependencies are preinstalled in offical conda environment and docker image, you can use the offical conda environment here or use the docker image by:
```
docker pull oneflowinc/manylinux2014_x86_64_cuda11.2
```
In the root directory of OneFlow source code, run:
```
mkdir build
cd build
```

Config the project, inside build directory:

If you are in China

config for CPU-only like this:

cmake .. -C ../cmake/caches/cn/cpu.cmake

config for CUDA like this:

cmake .. -C ../cmake/caches/cn/cuda.cmake -DCMAKE_CUDA_ARCHITECTURES=80 -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda -DCUDNN_ROOT_DIR=/usr/local/cudnn

If you are not in China

config for CPU-only like this:
```
cmake .. -C ../cmake/caches/international/cpu.cmake
```
config for CUDA like this:
```
cmake .. -C ../cmake/caches/international/cuda.cmake -DCMAKE_CUDA_ARCHITECTURES=80 -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda -DCUDNN_ROOT_DIR=/usr/local/cudnn
```
Here the DCMAKE_CUDA_ARCHITECTURES macro is used to specify the CUDA architecture, and the DCUDA_TOOLKIT_ROOT_DIR and DCUDNN_ROOT_DIR macros are used to specify the root path of the CUDA Toolkit and CUDNN.

Build the project, inside build directory, run:
```
make -j$(nproc)
```
Add oneflow to your PYTHONPATH, inside build directory, run:
```
source source.sh
```
Please note that this change is not permanent.
Simple validation
```
python3 -m oneflow --doctor
```

Getting Started

Please refer to QUICKSTART
中文版请参见快速上手

License

Apache License 2.0

For agents

This page has a .md twin and JSON over the API.

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