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. Neo-AI/TVM is a downstream branch of TVM that includes vendor- and product-specific features on top of the upstream codebase. Branches -------- * dev [![Build Status](https://neo-ai-ci.amazon-ml.com/job/tvm/job/dev/badge/icon)](https://neo-ai-ci.amazon-ml.com/job/tvm/job/dev/badge/icon) - This is the development branch with most update to date source code. 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.