Open Deep Learning Compiler Stack ============================================== [](./LICENSE) [](http://mode-gpu.cs.washington.edu:8080/job/dmlc/job/tvm/job/master/) [Installation](docs/how_to/install.md) | [Documentation](http://docs.tvmlang.org) | [Tutorials](http://tutorials.tvmlang.org) | [Operator Inventory](topi) | [FAQ](docs/faq.md) | [Contributors](CONTRIBUTORS.md) | [Community](http://tvmlang.org/community.html) | [Release Notes](NEWS.md) 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. Checkout the [tvm stack homepage](http://tvmlang.org/) for more information. License ------- © Contributors Licensed under an [Apache-2.0](https://github.com/dmlc/tvm/blob/master/LICENSE) license. Contribute to TVM ----------------- TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. - [Contributor Guide](docs/how_to/contribute.md) - Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) - Please also update [NEWS.md](NEWS.md) on changes and improvements in API and codes. Acknowledgement --------------- We learnt a lot from the following projects when building TVM. - [Halide](https://github.com/halide/Halide): TVM uses [HalideIR](https://github.com/dmlc/HalideIR) as data structure for arithematic simplification and low level lowering. We also learnt 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.