From 94e72e35b699b680a1489788794a57def3d40fc2 Mon Sep 17 00:00:00 2001
From: Danny McCormick <dannymccormick@google.com>
Date: Mon, 27 Mar 2023 12:16:42 -0400
Subject: [PATCH] Add links to tfma notebook for running in colab/github
 (#25903)

* Add links to tfma notebook for running in colab/github

* Missing comma

* Formatting

* Formatting
---
 examples/notebooks/beam-ml/tfma_beam.ipynb | 28 ++++++++++++++++++----
 1 file changed, 23 insertions(+), 5 deletions(-)

diff --git a/examples/notebooks/beam-ml/tfma_beam.ipynb b/examples/notebooks/beam-ml/tfma_beam.ipynb
index c0a485d6d475..c63a731f2d5e 100755
--- a/examples/notebooks/beam-ml/tfma_beam.ipynb
+++ b/examples/notebooks/beam-ml/tfma_beam.ipynb
@@ -32,16 +32,34 @@
       "cell_type": "markdown",
       "source": [
         "# TensorFlow Model Analysis in Beam\n",
-        "[TensorFlow Model Analysis (TFMA)](https://www.tensorflow.org/tfx/guide/tfma) is a library for performing model evaluation across different slices of data. TFMA performs its computations in a distributed manner over large amounts of data using Apache Beam.\n",
-        "\n",
-        "This example notebook illustrates how you can use TFMA to investigate and visualize the performance of a model as part of your Beam pipeline. Using TFMA enables scalable and flexible execution of your evaluation pipeline. This example uses [ExtractEvaluateAndWriteResults](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/ExtractEvaluateAndWriteResults), which is a `PTransform` that performs extraction and evaluation and writes results all in one step.\n",
         "\n",
-        "For additional information about TFMA, see the [TFMA basic notebook](https://www.tensorflow.org/tfx/tutorials/model_analysis/tfma_basic), which provides an in-depth look at its capabilities."
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/tfma_beam.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/tfma_beam.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\" />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
       ],
       "metadata": {
         "id": "1m9dEIsQAP_-"
       }
     },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GNbarEZsalS2"
+      },
+      "source": [
+        "[TensorFlow Model Analysis (TFMA)](https://www.tensorflow.org/tfx/guide/tfma) is a library for performing model evaluation across different slices of data. TFMA performs its computations in a distributed manner over large amounts of data using Apache Beam.\n",
+        "\n",
+        "This example notebook illustrates how you can use TFMA to investigate and visualize the performance of a model as part of your Beam pipeline. Using TFMA enables scalable and flexible execution of your evaluation pipeline. This example uses [ExtractEvaluateAndWriteResults](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/ExtractEvaluateAndWriteResults), which is a `PTransform` that performs extraction and evaluation and writes results all in one step.\n",
+        "\n",
+        "For additional information about TFMA, see the [TFMA basic notebook](https://www.tensorflow.org/tfx/tutorials/model_analysis/tfma_basic), which provides an in-depth look at its capabilities."
+      ]
+    },
     {
       "cell_type": "markdown",
       "source": [
@@ -739,4 +757,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}