<commit_before># Multi-Level Intermediate Representation Overview

The MLIR project aims to define a common intermediate representation (IR) that
will unify the infrastructure required to execute high performance machine
learning models in TensorFlow and similar ML frameworks. This project will
include the application of HPC techniques, along with integration of search
algorithms like reinforcement learning. This project aims to reduce the cost to
bring up new hardware, and improve usability for existing TensorFlow users.

Note that this repository contains the core of the MLIR framework. The
TensorFlow compilers we are building on top of MLIR will be part of the
main TensorFlow repository soon.

## More resources

For more information on MLIR, please see:

*   [The MLIR draft specification](g3doc/LangRef.md), which describes the IR
    itself.
*   [The MLIR rationale document](g3doc/Rationale.md), covering motivation
    behind some decisions.
*   Previous external [talks](#mlir-talks).

Join the [MLIR mailing list](https://groups.google.com/a/tensorflow.org/forum/#!forum/mlir)
to hear about announcements and discussions.
Please be mindful of the [TensorFlow Code of Conduct](https://github.com/tensorflow/tensorflow/blob/master/CODE_OF_CONDUCT.md),
which pledges to foster an open and welcoming environment.

## What is MLIR for?

MLIR is intended to be a hybrid IR which can support multiple different
requirements in a unified infrastructure. For example, this includes:<commit_msg>    Add mention that we don't (yet) accept contributions in the README<commit_after># Multi-Level Intermediate Representation Overview

The MLIR project aims to define a common intermediate representation (IR) that
will unify the infrastructure required to execute high performance machine
learning models in TensorFlow and similar ML frameworks. This project will
include the application of HPC techniques, along with integration of search
algorithms like reinforcement learning. This project aims to reduce the cost to
bring up new hardware, and improve usability for existing TensorFlow users.

Note that this repository contains the core of the MLIR framework. The
TensorFlow compilers we are building on top of MLIR will be part of the
main TensorFlow repository soon.

# How to Contribute

We'd love to accept your patches and contributions to this project soon. But we
are not yet ready to accept community contributions at this time.

## More resources

For more information on MLIR, please see:

*   [The MLIR draft specification](g3doc/LangRef.md), which describes the IR
    itself.
*   [The MLIR rationale document](g3doc/Rationale.md), covering motivation
    behind some decisions.
*   Previous external [talks](#mlir-talks).

Join the [MLIR mailing list](https://groups.google.com/a/tensorflow.org/forum/#!forum/mlir)
to hear about announcements and discussions.
Please be mindful of the [TensorFlow Code of Conduct](https://github.com/tensorflow/tensorflow/blob/master/CODE_OF_CONDUCT.md),
which pledges to foster an open and welcoming environment.

## What is MLIR for?

MLIR is intended to be a hybrid IR which can support multiple different
requirements in a unified infrastructure. For example, this includes:
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