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| <img src=https://raw.githubusercontent.com/apache/tvm-site/main/images/logo/tvm-logo-small.png width=128/> Open Machine Learning Compiler Framework | |
| ============================================== | |
| [Documentation](https://tvm.apache.org/docs) | | |
| [Contributors](CONTRIBUTORS.md) | | |
| [Community](https://tvm.apache.org/community) | | |
| [Release Notes](NEWS.md) | |
| Apache TVM is an open machine learning compilation framework, | |
| following the following principles: | |
| - Python-first development that enables quick customization of machine learning compiler pipelines. | |
| - Universal deployment to bring models into minimum deployable modules. | |
| 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/get_started/overview.html) tutorial is a great | |
| place to start. | |
| Contribute to TVM | |
| ----------------- | |
| TVM adopts the Apache committer model. We aim to create an open-source project maintained and owned by the community. | |
| Check out the [Contributor Guide](https://tvm.apache.org/docs/contribute/). | |
| History and Acknowledgement | |
| --------------------------- | |
| TVM started as a research project for deep learning compilation. | |
| The first version of the project benefited a lot from the following projects: | |
| - [Halide](https://github.com/halide/Halide): Part of TVM's TIR and arithmetic simplification module | |
| originates from Halide. We also learned and adapted some parts of the 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. | |
| Since then, the project has gone through several rounds of redesigns. | |
| The current design is also drastically different from the initial design, following the | |
| development trend of the ML compiler community. | |
| The most recent version focuses on a cross-level design with TensorIR as the tensor-level representation | |
| and Relax as the graph-level representation and Python-first transformations. | |
| The project's current design goal is to make the ML compiler accessible by enabling most | |
| transformations to be customizable in Python and bringing a cross-level representation that can jointly | |
| optimize computational graphs, tensor programs, and libraries. The project is also a foundation | |
| infra for building Python-first vertical compilers for domains, such as LLMs. |