wehub-resource-sync
66 lines
3.4 KiB
Markdown
66 lines
3.4 KiB
Markdown
<!--- Licensed to the Apache Software Foundation (ASF) under one -->
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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
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==============================================
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[Documentation](https://tvm.apache.org/docs) |
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[Contributors](CONTRIBUTORS.md) |
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[Community](https://tvm.apache.org/community) |
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[Release Notes](NEWS.md)
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Apache TVM is an open machine learning compilation framework,
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following the following principles:
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- Python-first development that enables quick customization of machine learning compiler pipelines.
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- Universal deployment to bring models into minimum deployable modules.
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License
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-------
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TVM is licensed under the [Apache-2.0](LICENSE) license.
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Getting Started
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---------------
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Check out the [TVM Documentation](https://tvm.apache.org/docs/) site for installation instructions, tutorials, examples, and more.
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The [Getting Started with TVM](https://tvm.apache.org/docs/get_started/overview.html) tutorial is a great
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place to start.
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Contribute to TVM
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-----------------
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TVM adopts the Apache committer model. We aim to create an open-source project maintained and owned by the community.
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Check out the [Contributor Guide](https://tvm.apache.org/docs/contribute/).
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History and Acknowledgement
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---------------------------
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TVM started as a research project for deep learning compilation.
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The first version of the project benefited a lot from the following projects:
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- [Halide](https://github.com/halide/Halide): Part of TVM's TIR and arithmetic simplification module
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originates from Halide. We also learned and adapted some parts of the lowering pipeline from Halide.
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- [Loopy](https://github.com/inducer/loopy): use of integer set analysis and its loop transformation primitives.
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- [Theano](https://github.com/Theano/Theano): the design inspiration of symbolic scan operator for recurrence.
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Since then, the project has gone through several rounds of redesigns.
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The current design is also drastically different from the initial design, following the
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development trend of the ML compiler community.
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The most recent version focuses on a cross-level design with TensorIR as the tensor-level representation
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and Relax as the graph-level representation and Python-first transformations.
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The project's current design goal is to make the ML compiler accessible by enabling most
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transformations to be customizable in Python and bringing a cross-level representation that can jointly
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optimize computational graphs, tensor programs, and libraries. The project is also a foundation
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infra for building Python-first vertical compilers for domains, such as LLMs.
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