TVM示例展示 README.md，Makefile，CMakeLists.txt
- 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
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[Documentation](https://tvm.apache.org/docs) |
[Contributors](CONTRIBUTORS.md) |
[Community](https://tvm.apache.org/community) |
[Release Notes](NEWS.md)
[](https://ci.tlcpack.ai/job/tvm/job/main/)
[](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
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TVM is licensed under the [Apache-2.0](LICENSE) license.
Getting Started
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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
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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/