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TVM示例展示 README.md，Makefile，CMakeLists.txt

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 TVM示例展示 README.md，Makefile，CMakeLists.txt

  1. TVM/README.md

 

<img src=https://raw.githubusercontent.com/apache/tvm-site/main/images/logo/tvm-logo-small.png width=128/> Open Deep Learning Compiler Stack

==============================================

[Documentation](https://tvm.apache.org/docs) |

[Contributors](CONTRIBUTORS.md) |

[Community](https://tvm.apache.org/community) |

[Release Notes](NEWS.md)  

 

[![Build Status](https://ci.tlcpack.ai/buildStatus/icon?job=tvm/main)](https://ci.tlcpack.ai/job/tvm/job/main/)

[![WinMacBuild](https://github.com/apache/tvm/workflows/WinMacBuild/badge.svg)](https://github.com/apache/tvm/actions?query=workflow%3AWinMacBuild)

 

Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the

productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends.

TVM works with deep learning frameworks to provide end to end compilation to different backends.

 

License

-------

TVM is licensed under the [Apache-2.0](LICENSE) license.

   

Getting Started

---------------

Check out the [TVM Documentation](https://tvm.apache.org/docs/) site for installation instructions, tutorials, examples, and more.

The [Getting Started with TVM](https://tvm.apache.org/docs/tutorials/get_started/introduction.html) tutorial is a great

place to start.

 

Contribute to TVM

-----------------

TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community.

Check out the [Contributor Guide](https://tvm.apache.org/docs/contribute/).

 

Acknowledgement

---------------

We learned a lot from the following projects when building TVM.

- [Halide](https://github.com/halide/Halide): Part of TVM's TIR and arithmetic simplification module

  originates from Halide. We also learned and adapted some part of lowering pipeline from Halide.

- [Loopy](https://github.com/inducer/loopy): use of integer set analysis and its loop transformation primitives.

- [Theano](https://github.com/Theano/Theano): the design inspiration of symbolic scan operator for recurrence.

 

 2. TVM/MakeFile

 

  

 

 3. TVM/CMakeLists.txt

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

 

  

 

  

 

  

 

  

 

  

 

  

 

  

 

  

 

  

 

  

 

 参考链接：

https://github.com/apache/tvm/

 

标签：md,CMakeLists,github,示例,TVM,https,apache,tvm,com
来源： https://www.cnblogs.com/wujianming-110117/p/15583357.html