The Wayback Machine - https://web.archive.org/web/20230225072920/https://github.com/apache/tvm
Skip to content

apache/tvm

main
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

This PR applies appropriate changes to make sure the CI fails if micro_tvmc.sh tutorial fails. This issue was captured in #14074.
This PR also makes changes to avoid this breakage in bash script tutorials in future. In addition, this PR fixes the bug in running TVMC tutorial which happened due to renaming zephyr_board to board.
f7165a1

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
November 28, 2022 19:35
July 27, 2022 13:04
October 11, 2022 11:55

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

TVM is licensed under the Apache-2.0 license.

Getting Started

Check out the TVM Documentation site for installation instructions, tutorials, examples, and more. The Getting Started with TVM tutorial is a great place to start.

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.