Open Deep Learning Compiler Stack ============================================== [](./LICENSE) [](http://ci.tvm.ai:8080/job/tvm/job/master/) [Documentation](https://docs.tvm.ai) | [Contributors](CONTRIBUTORS.md) | [Community](https://tvm.ai/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](https://tvm.ai/) 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. Checkout the [Contributor Guide](https://docs.tvm.ai/contribute/) 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 arithmetic 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.