代码拉取完成，页面将自动刷新
OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient. With OneFlow, it is easy to:
CUDA arch 60 or above
CUDA Toolkit version 10.0 or above
Nvidia driver version 440.33 or above
OneFlow will work on a minimum supported driver, and any driver beyond. For more information, please refer to CUDA compatibility documentation.
docker pull oneflowinc/oneflow:nightly-cuda11.7
(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 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
Please refer to troubleshooting for common issues you might encounter when compiling and running OneFlow.
GitHub issues: any install, bug, feature issues.
www.oneflow.org: brand related information.
OneFlow was originally developed by OneFlow Inc and Zhejiang Lab.
此处可能存在不合适展示的内容，页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容，可点击提交进行申诉，我们将尽快为您处理。