33 строки
1.8 KiB
Markdown
33 строки
1.8 KiB
Markdown
<img src=https://raw.githubusercontent.com/tqchen/tvm.ai/master/images/logo/tvm-logo-small.png width=128/> Open Deep Learning Compiler Stack
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==============================================
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[](./LICENSE)
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[](http://ci.tvm.ai:8080/job/tvm/job/master/)
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[Documentation](https://docs.tvm.ai) |
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[Contributors](CONTRIBUTORS.md) |
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[Community](https://tvm.ai/community.html) |
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[Release Notes](NEWS.md)
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TVM 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.
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TVM works with deep learning frameworks to provide end to end compilation to different backends.
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Checkout the [tvm stack homepage](https://tvm.ai/) for more information.
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License
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-------
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© Contributors Licensed under an [Apache-2.0](https://github.com/dmlc/tvm/blob/master/LICENSE) license.
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Contribute to TVM
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-----------------
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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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---------------
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We learnt 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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arithematic simplification and low level lowering. We also learnt 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.
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- [Theano](https://github.com/Theano/Theano): the design inspiration of symbolic scan operator for recurrence.
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