Open deep learning compiler stack for cpu, gpu and specialized accelerators
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Lianmin Zheng 14de1dec78 [TUTORIAL] add tutorial for mali gpu (#313)
* add tutorial for mali gpu

* update submodule version

* fix doc error

* fix server error

* fix server in test
2018-05-29 08:47:00 -07:00
.github Update ISSUE_TEMPLATE.md 2018-04-17 10:09:12 -07:00
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dmlc-core@d3f7fbb53e SGXify graph runtime (#937) 2018-03-05 10:58:22 -08:00
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jvm [RPC] Refactor, introduce tracker (#1080) 2018-04-04 20:40:41 -07:00
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nnvm [TUTORIAL] add tutorial for mali gpu (#313) 2018-05-29 08:47:00 -07:00
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src [llvm] fixed issue with llvm 5 vs 6 (#1167) 2018-05-18 16:04:11 -07:00
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topi axis bounds checking in split (#1178) 2018-05-22 10:37:31 -07:00
tutorials fix wrong type declaration of float64 "log" in intrin_math.py (#1169) 2018-05-20 20:59:40 -07:00
verilog [CONTRIB/BLAS] Add CBLAS Example to contrib (#120) 2017-05-05 10:55:34 -07:00
web [RPC] Refactor, introduce tracker (#1080) 2018-04-04 20:40:41 -07:00
.gitignore [IOS] Improve the iOS RPC with exclusive filesys lock (#981) 2018-03-09 11:12:57 -08:00
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CMakeLists.txt Fix fatal error <CL/opencl.h: No such file> when building with CMake (#1045) 2018-03-24 10:12:36 -07:00
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README.md Update README.md 2018-04-25 10:41:34 -07:00

README.md

Open Deep Learning Compiler Stack

GitHub license Build Status

Installation | Documentation | Tutorials | Operator Inventory | FAQ | Contributors | Community | Release Notes

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 for more information.

License

© Contributors Licensed under an Apache-2.0 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.

Acknowledgement

We learnt a lot from the following projects when building TVM.

  • Halide: TVM uses HalideIR as data structure for arithematic simplification and low level lowering. We also learnt and adapted some part of lowering pipeline from Halide.
  • Loopy: use of integer set analysis and its loop transformation primitives.
  • Theano: the design inspiration of symbolic scan operator for recurrence.