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<img src=https://raw.githubusercontent.com/apache/incubator-tvm-site/master/images/logo/tvm-logo-small.png width=128/> Open Deep Learning Compiler Stack
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==============================================
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[Documentation](https://docs.tvm.ai) |
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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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[![Build Status](https://ci.tvm.ai/buildStatus/icon?job=tvm/master)](https://ci.tvm.ai/job/tvm/job/master/)
Tianqi Chen committed
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[![Azure Pipeline](https://dev.azure.com/tvmai/tvm/_apis/build/status/windows_mac_build?branchName=master)](https://dev.azure.com/tvmai/tvm/_build/latest?definitionId=2&branchName=master)
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Apache TVM (incubating) is a compiler stack for deep learning systems. It is designed to close the gap between the
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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.
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License
-------
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© Contributors Licensed under an [Apache-2.0](LICENSE) license.
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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.
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Checkout the [Contributor Guide](https://docs.tvm.ai/contribute/)
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Acknowledgement
---------------
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We learned a lot from the following projects when building TVM.
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- [Halide](https://github.com/halide/Halide): TVM uses [HalideIR](https://github.com/dmlc/HalideIR) as data structure for
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  arithmetic simplification and low level lowering. We also learned and adapted some part of lowering pipeline from Halide.
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- [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.