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opensecura / 3p / openxla / iree / d3a152b3f3713b82cc85dc7f58c772aeaa8ff134
commitd3a152b3f3713b82cc85dc7f58c772aeaa8ff134[log] [tgz]
authorScott Todd <scotttodd@google.com>Mon Oct 16 08:32:08 2023 -0700
committerGitHub <noreply@github.com>Mon Oct 16 08:32:08 2023 -0700
tree29855716b4fa27f1e3c4d485e3dcb56ddd9a2933
parentebdb098b216c3e59a9977902823ede613f553f71 [diff]
Rebase PyTorch guide on SHARK-Turbine. (#15181)

Fixes https://github.com/openxla/iree/issues/15114 (future work can be
tracked elsewhere, this does the initial lift).

This refreshes the docs from pointing to a mix of
[torch-mlir](https://github.com/llvm/torch-mlir) and
[iree-torch](https://github.com/iree-org/iree-torch) to just pointing to
[SHARK-Turbine](https://github.com/nod-ai/SHARK-Turbine). Turbine is
built on top of torch-mlir and IREE and is under active development.
* Turbine supports both JIT and AOT workflows, and this PR documents
both
* The "AOT toolkit" has a simple API and an advanced API, but I've only
documented the simple API in this first PR

---

|  | |
|--------|--------|
| Current page | https://www.iree.dev/guides/ml-frameworks/pytorch/ |
| Preview of this PR |
https://scotttodd.github.io/iree/guides/ml-frameworks/pytorch/ |

---

Depends on https://github.com/openxla/iree/pull/15166 for one sample
Colab notebook.
7 files changed
tree: 29855716b4fa27f1e3c4d485e3dcb56ddd9a2933
  1. .devcontainer/
  2. .github/
  3. build_tools/
  4. compiler/
  5. docs/
  6. experimental/
  7. integrations/
  8. lib/
  9. llvm-external-projects/
  10. runtime/
  11. samples/
  12. tests/
  13. third_party/
  14. tools/
  15. .bazel_to_cmake.cfg.py
  16. .bazelignore
  17. .bazelrc
  18. .bazelversion
  19. .clang-format
  20. .dockerignore
  21. .git-blame-ignore-revs
  22. .gitignore
  23. .gitmodules
  24. .yamllint.yml
  25. AUTHORS
  26. BUILD.bazel
  27. CITATION.cff
  28. CMakeLists.txt
  29. configure_bazel.py
  30. CONTRIBUTING.md
  31. LICENSE
  32. README.md
  33. WORKSPACE
README.md

IREE: Intermediate Representation Execution Environment

IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.

See our website for project details, user guides, and instructions on building from source.

CI Status

Project Status

IREE is still in its early phase. We have settled down on the overarching infrastructure and are actively improving various software components as well as project logistics. It is still quite far from ready for everyday use and is made available without any support at the moment. With that said, we welcome any kind of feedback on any communication channels!

Communication Channels

Related Project Channels

  • MLIR topic within LLVM Discourse: IREE is enabled by and heavily relies on MLIR. IREE sometimes is referred to in certain MLIR discussions. Useful if you are also interested in MLIR evolution.

Architecture Overview

IREE Architecture IREE Architecture

See our website for more information.

Presentations and Talks

License

IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.