<li><a href="http://cs/f:devtools/kokoro/config/prod/$KOKORO_JOB_NAME">Codesearch - job definition</a></li>
<li><a href="http://cs/f:learning/brain/testing/kokoro/$(echo "$KOKORO_JOB_NAME" | sed 's!tensorflow/!!g')">Codesearch - build definition & scripts</a></li>
<li><a href="http://cs/$KOKORO_JOB_NAME">Codesearch - All references to this job</a></li>
</ul>

cff-version: 1.2.0
message: "If you use TensorFlow in your research, please cite it using these metadata. Software is available from tensorflow.org."
title: TensorFlow, Large-scale machine learning on heterogeneous systems

## TensorFlow models are programs

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

{
    "description": "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.",
    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }

        token: ${{ secrets.JENKINS_TOKEN }}
        base: ${{ github.event.inputs.release_branch }}
        branch: ${{ github.event.inputs.release_branch }}-${{ steps.cherrypick.outputs.SHORTSHA }}
        reviewers: learning-to-play
        body: |

# TODO(b/173549186): Move Google-internal TF code out of learning/brain
package_group(
    name = "internal",
    packages = [
        "//devtools/python/indexer/...",
        "//learning/brain/keras/...",
        "//learning/brain/mlir/...",
        "//learning/brain/tfrt/...",
        "//learning/lib/ami/simple_ml/...",
        "//learning/pathways/...",
        "//learning/serving/contrib/tfrt/mlir/canonical_ops/...",

  const lowercased_title = (title || '').toLowerCase();
  const onednn_assignees = ['penpornk'];
  if (lowercased_title.includes('onednn')) assignees = onednn_assignees;
  const intel_windows_assignees = ['nitins17', 'learning-to-play'];
  if (lowercased_title.includes('intel') &&
      lowercased_title.includes('windows') && domain.includes('intel.com'))
    assignees = intel_windows_assignees;

------------------- |
[![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/api_docs/) |

[TensorFlow](https://www.tensorflow.org/) is an end-to-end open source platform
for machine learning. It has a comprehensive, flexible ecosystem of
[tools](https://www.tensorflow.org/resources/tools),
[libraries](https://www.tensorflow.org/resources/libraries-extensions), and

        title: Update the RBE images to the latest container versions
        committer: TensorFlow Release Automation <******@****.***>
        token: ${{ secrets.JENKINS_TOKEN }}
        reviewers: mihaimaruseac,learning-to-play,nitins17
        body: |
          This PR was created by a GitHub Actions workflow to update all the SIG Build-based RBE containers to the most recent containers. See:

#define TENSORFLOW_C_EAGER_TFE_CONTEXT_INTERNAL_H_

#include "tensorflow/c/conversion_macros.h"
#include "tensorflow/c/eager/immediate_execution_context.h"

// Wraps a pointer to a context implementation.
//
// WARNING: Since the underlying object could be ref-counted a user of this
// interface cannot destruct the underlying context object. Instead, call
// TFE_DeleteContext who calls Release() on the context pointer and deletes