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Megvii Engine Team e0fffa5286 fix(mge/tensor): fix mem leak when getting value in trace 4 years ago
.github ci(imperative): run imperative python test 4 years ago
ci ci(imperative): run imperative python test 4 years ago
cmake fix(mgb/jit): add cmake target MLIRTosa for latest llvm-project 4 years ago
dnn feat(jit): add memfwd in jit executor opr 4 years ago
imperative fix(mge/tensor): fix mem leak when getting value in trace 4 years ago
scripts feat(dnn/atlas): add atlas stub 4 years ago
sdk refactor(mgb): move convolution mixin to search_policy 4 years ago
src chore(release): bump version 4 years ago
test feat(dnn): add cuda preprocess fusion 4 years ago
third_party chore(third_party): add range-v3 as third_party dependency 4 years ago
toolchains fix(ios): fix cmake ios cross build 4 years ago
tools feat(opr/nvof): add nvof operator 4 years ago
.gitattributes refactor(mge): delete old dump_with_testcase_mge 4 years ago
.gitignore chroe(.gitignore): add more ignore rules for git 4 years ago
.gitmodules chore(third_party): add range-v3 as third_party dependency 4 years ago
ACKNOWLEDGMENTS chore(third_party): add range-v3 as third_party dependency 4 years ago
CMakeLists.txt feat(dnn/atlas): add atlas stub 4 years ago
CODE_OF_CONDUCT.md MegEngine: Initial commit of MegEngine. 5 years ago
CONTRIBUTOR_LICENSE_AGREEMENT.md MegEngine: Initial commit of MegEngine. 5 years ago
LICENSE fix(license): correct and format the content of license to make it be detected by GitHub 4 years ago
README.md Update README.md 4 years ago
README_CN.md Update README.md 4 years ago
logo.png MegEngine: Initial commit of MegEngine. 5 years ago

README.md

MegEngine

English | 中文

MegEngine is a fast, scalable and easy-to-use deep learning framework, with auto-differentiation.

Installation

NOTE: MegEngine now supports Linux-64bit/Windows-64bit/MacOS-10.14+ (CPU-Only) Platforms with Python from 3.5 to 3.8. On Windows 10 you can either install the Linux distribution through Windows Subsystem for Linux (WSL) or install the Windows distribution directly.

Binaries

Commands to install from binaries via pip wheels are as follows:

python3 -m pip install megengine -f https://megengine.org.cn/whl/mge.html

Build from Source

Prerequisites

Most of the dependencies of MegEngine are located in third_party directory, which can be prepared by executing:

./third_party/prepare.sh
./third_party/install-mkl.sh

But some dependencies need to be Installed manually:

  • CUDA(>=10.1), cuDNN(>=7.6)are required when building MegEngine with CUDA support.
  • TensorRT(>=5.1.5) is required when building with TensorRT support.
  • LLVM/Clang(>=6.0) is required when building with Halide JIT support.
  • Python(>=3.5), Numpy, are required to build Python modules.

Build

MegEngine uses CMake as the build tool.
We provide the following scripts to facilitate building.

  • host_build.sh is to build MegEngine targeted to run on the same host machine.
    Please run the following command to get help information: 
    scripts/cmake-build/host_build.sh -h
  • cross_build_android_arm_inference.sh is to build MegEngine targeted to run at Android-ARM platforms.
    Please run the following command to get help information: 
    scripts/cmake-build/cross_build_android_arm_inference.sh -h
  • cross_build_linux_arm_inference.sh is to build MegEngine targeted to run at Linux-ARM platforms.
    Please run the following command to get help information: 
    scripts/cmake-build/cross_build_linux_arm_inference.sh -h
  • cross_build_ios_arm_inference.sh is to build MegEngine targeted to run iphone/iPad platforms.
    Please run the following command to get help information: 
    scripts/cmake-build/cross_build_ios_arm_inference.sh

Please refer to BUILD_README.md for more details.

How to Contribute

We believe we can build an open and friendly community and power humanity with AI.

How to contact us

Resources

License

MegEngine is Licensed under the Apache License, Version 2.0

Copyright (c) 2014-2020 Megvii Inc. All rights reserved.

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MegEngine 安装包中集成了使用 GPU 运行代码所需的 CUDA 环境，不用区分 CPU 和 GPU 版。 如果想要运行 GPU 程序，请确保机器本身配有 GPU 硬件设备并安装好驱动。 如果你想体验在云端 GPU 算力平台进行深度学习开发的感觉，欢迎访问 MegStudio 平台

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