OneFlow is a performance-centered and open-source deep learning framework.
- Version 0.4.0 is out!
- New Pytorch flavored APIs (
import oneflow.experimental as flow) - New models and examples
- Full changelog
- New Pytorch flavored APIs (
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Python 3.6, 3.7, 3.8 (3.9 is available in nightly version)
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(Highly recommended) Upgrade pip
python3 -m pip install --upgrade pip #--user -
CUDA Toolkit Linux x86_64 Driver
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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.
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Please upgrade your Nvidia driver to version 440.33 or above and install OneFlow for CUDA 10.2 if possible.
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To install latest stable release of OneFlow with CUDA support:
python3 -m pip install -f https://release.oneflow.info oneflow==0.4.0+cu102
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To install nightly release of OneFlow with CUDA support:
python3 -m pip install oneflow -f https://staging.oneflow.info/branch/master/cu102
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To install other available builds for different variants:
- Stable
python3 -m pip install --find-links https://release.oneflow.info oneflow==0.4.0+[PLATFORM]
- Nightly
python3 -m pip install oneflow -f https://staging.oneflow.info/branch/master/[PLATFORM] - All available
[PLATFORM]:Platform CUDA Driver Version Supported GPUs cu112 >= 450.80.02 GTX 10xx, RTX 20xx, A100, RTX 30xx cu111 >= 450.80.02 GTX 10xx, RTX 20xx, A100, RTX 30xx cu110, cu110_xla >= 450.36.06 GTX 10xx, RTX 20xx, A100 cu102, cu102_xla >= 440.33 GTX 10xx, RTX 20xx cu101, cu101_xla >= 418.39 GTX 10xx, RTX 20xx cu100, cu100_xla >= 410.48 GTX 10xx, RTX 20xx cpu N/A N/A
- Stable
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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/simpleFor more information on this, please refer to pypi 镜像使用帮助
Clone Source Code
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git clone https://github.com/Oneflow-Inc/oneflow --depth=1
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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 OneFlow
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Please refer to this repo
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Pull a docker image:
docker pull oneflowinc/oneflow-manylinux2014-cuda10.2:0.1All images available : https://hub.docker.com/u/oneflowinc
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In the root directory of OneFlow source code, run:
python3 docker/package/manylinux/build_wheel.py --python_version=3.6This should produce
.whlfiles in the directorywheelhouse -
If you are in China, you might need to add these flags:
--use_tuna --use_system_proxy --use_aliyun_mirror -
You can choose CUDA/Python versions of wheel by adding:
--cuda_version=10.1 --python_version=3.6,3.7 -
For more useful flags, plese run the script with flag
--helpor refer to the source code of the script.
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Install dependencies
- on Ubuntu 20.04, run:
sudo apt install -y libopenblas-dev nasm g++ gcc python3-pip cmake autoconf libtool - on macOS, run:
brew install nasm
- on Ubuntu 20.04, run:
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In the root directory of OneFlow source code, run:
mkdir build cd build -
Config the project, inside
builddirectory:-
If you are in China
run this to config for CUDA:
cmake .. -C ../cmake/caches/cn/cuda.cmakerun this to config for CPU-only:
cmake .. -C ../cmake/caches/cn/cpu.cmake -
If you are not in China
run this to config for CUDA:
cmake .. -C ../cmake/caches/international/cuda.cmakerun this to config for CPU-only:
cmake .. -C ../cmake/caches/international/cpu.cmake
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Build the project, inside
builddirectory, run:make -j$(nproc) -
Add oneflow to your PYTHONPATH, inside
builddirectory, run:source source.shPlease note that this change is not permanent.
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Simple validation
python3 -m oneflow --doctor
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Please refer to troubleshooting for common issues you might encounter when compiling and running OneFlow.
XRT
- You can check this doc to obtain more details about how to use XLA and TensorRT with OneFlow.
3 minutes to run MNIST.
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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.
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GitHub issues: any install, bug, feature issues.
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www.oneflow.org: brand related information.
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- QQ 群: 331883
- 微信号（加好友入交流群）: OneFlowXZS
- 知乎
OneFlow was originally developed by OneFlow Inc and Zhejiang Lab.
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