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# IREE: An Experimental MLIR Execution Environment
-**DISCLAIMER**: This is not an officially supported Google product. It's an
-experimental playground for low-level/tightly integrated machine learning
-libraries that make use of modern hardware acceleration APIs and techniques (see
-[non goals](#non-goals)).
+**DISCLAIMER**: This is an early phase project that we hope will graduate into a
+supported form someday, but it is far from ready for everyday use and is made
+available without any support. With that said, feel free to browse the issues
+and reach out on the
+[iree-discuss mailing list](https://groups.google.com/forum/#!forum/iree-discuss).
## Table of Contents
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consider the IREE experiment a success if what resulted was some worked examples
that help advance the entire ecosystem!
-## Non-Goals
-
-* Replace parts of the supported TensorFlow ecosystem of tools: The authors
- within Google work closely with TensorFlow and contribute to it regularly.
- However, IREE is exploring some different angles of the problem and is
- experimental. We will seek to leverage anything of value that we learn in an
- appropriate way to make TensorFlow better over time, but the two should not
- be conflated.
-* Providing an [SLA](https://en.wikipedia.org/wiki/Service-level_agreement) of
- any kind: IREE is infrastructure research, not a supported product. If it
- gains mind-share or traction, we would revisit that in conjunction with
- finding a more permanent way to align it with the broader constellation of
- ML tooling.
-
## Milestones
We are currently just at the starting line, with basic
