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apache / tvm / 7d8a774a6bd87f4e36ced3202d4ee1f06594c104
commit7d8a774a6bd87f4e36ced3202d4ee1f06594c104[log] [tgz]
authorTianqi Chen <tqchen@users.noreply.github.com>Sat Jul 31 08:38:08 2021 -0400
committerGitHub <noreply@github.com>Sat Jul 31 08:38:08 2021 -0400
tree0c55d903445e87eb27377bc3f29d24c005cb4bb6
parent2a8950b5c031cf58f8caa1675b4503df9478f28a [diff]
[VTA] Recover rpc server support (#8604)
2 files changed
tree: 0c55d903445e87eb27377bc3f29d24c005cb4bb6
  1. .github/
  2. 3rdparty/
  3. apps/
  4. cmake/
  5. conda/
  6. docker/
  7. docs/
  8. golang/
  9. include/
  10. jvm/
  11. licenses/
  12. nnvm/
  13. python/
  14. rust/
  15. src/
  16. tests/
  17. tutorials/
  18. vta/
  19. web/
  20. .asf.yaml
  21. .clang-format
  22. .gitignore
  23. .gitmodules
  24. CMakeLists.txt
  25. conftest.py
  26. CONTRIBUTORS.md
  27. Jenkinsfile
  28. KEYS
  29. LICENSE
  30. Makefile
  31. mypy.ini
  32. NEWS.md
  33. NOTICE
  34. pyproject.toml
  35. pytest.ini
  36. README.md
  37. version.py
README.md

Open Deep Learning Compiler Stack

Documentation | Contributors | Community | Release Notes

Build Status WinMacBuild

Apache 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.

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. Check out the Contributor Guide.

Acknowledgement

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

  • Halide: Part of TVM's TIR and arithmetic simplification module originates from Halide. We also learned 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.