https://github.com/rdma-from-gpu/tvm

https://github.com/rdma-from-gpu/tvm

Last synced: 12 days ago
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• Host: GitHub
• URL: https://github.com/rdma-from-gpu/tvm
• Owner: rdma-from-gpu
• License: apache-2.0
• Created: 2024-08-05T08:05:04.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-16T14:05:58.000Z (3 months ago)
• Last Synced: 2024-08-17T14:08:47.218Z (3 months ago)
• Language: Python
• Size: 77.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: NEWS.md
  • License: LICENSE
  • Codeowners: .github/CODEOWNERSHIP

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README

        


 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/tutorial/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.