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Rm oneflow export and experimental (#5589)
Shenghang Tsai authored 
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* remove enable_eager_execution() in oneflow/python

remove @experimental_api decorator for apis, except that ones
having same name with single client api.

* remove all import oneflow.experimental in doctest

* replace oneflow.experimental with oneflow in docstr

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* remove experimental in test/modules and test/tensor

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* oneflow.python => oneflow

* rm absolute_import

* remove experimental in test/dataloader

* remove experimental call in nn/modules

* remove experimental from nn/ framework/ test_utils

* remove experimental in functionalpy

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* fix error when do multi case test on graph

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* check in src

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* from . import linalg
from . import autograd
from . import optim

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Co-authored-by: default avatarGehangZhang <649975329@qq.com>
Co-authored-by: default avatarYaoChi <later@usopp.net>
Co-authored-by: default avataroneflow-ci-bot <ci-bot@oneflow.org>
Co-authored-by: default avatarstrint <xiaoyulink@gmail.com>
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ci
cmake
docker
docs
oneflow
python
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.clang-format
.clang-tidy
.dockerignore
.gitignore
CMakeLists.txt
LICENSE
README.md
dev-requirements.txt
README.md

OneFlow

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

Simple CI Documentation Status

Latest News

Install OneFlow

System Requirements

  • Python 3.6, 3.7, 3.8 (3.9 is available in nightly version)

  • (Highly recommended) Upgrade pip

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

  • CUDA Toolkit Linux x86_64 Driver

    • 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.

    • Please upgrade your Nvidia driver to version 440.33 or above and install OneFlow for CUDA 10.2 if possible.

Install with Pip Package

  • To install latest stable release of OneFlow with CUDA support:

    python3 -m pip install -f https://release.oneflow.info oneflow==0.4.0+cu102

  • To install nightly release of OneFlow with CUDA support:

    python3 -m pip install oneflow -f https://staging.oneflow.info/branch/master/cu102

  • 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

  • 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

Build from Source

Clone Source Code

  • Option 1: Clone source code from GitHub

    git clone https://github.com/Oneflow-Inc/oneflow --depth=1

  • 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 OneFlow

  • Option 1: Build with Conda (recommended)

    Please refer to this repo

  • Option 2: Build in docker container (recommended)

    • Pull a docker image:

      docker pull oneflowinc/oneflow-manylinux2014-cuda10.2:0.1

      All images available : https://hub.docker.com/u/oneflowinc

    • In the root directory of OneFlow source code, run:

      python3 docker/package/manylinux/build_wheel.py --python_version=3.6

      This should produce .whl files in the directory wheelhouse

    • 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 --help or refer to the source code of the script.

  • Option 3: Build on bare metal

    • 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

    • In the root directory of OneFlow source code, run:

      mkdir build
cd build

    • Config the project, inside build directory:

      • If you are in China

        run this to config for CUDA:

        cmake .. -C ../cmake/caches/cn/cuda.cmake

        run 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.cmake

        run this to config for CPU-only:

        cmake .. -C ../cmake/caches/international/cpu.cmake

    • 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

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

Model Zoo and Benchmark

Communication

The Team

OneFlow was originally developed by OneFlow Inc and Zhejiang Lab.

License

Apache License 2.0