Add examples as it was requested in an email.

PiperOrigin-RevId: 254202446

site/en/r2/tutorials/distribute/keras.ipynb

@@ -683,6 +683,21 @@
         "  print ('Eval loss: {}, Eval Accuracy: {}'.format(eval_loss, eval_acc))"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "MUZwaz4AKjtD"
+      },
+      "source": [
+        "### Examples and Tutorials\n",
+        "Here are some examples for using distribution strategy with keras fit/compile:\n",
+        "1. [Transformer](https://github.com/tensorflow/models/blob/master/official/transformer/v2/transformer_main.py) example trained using `tf.distribute.MirroredStrategy`\n",
+        "2. [NCF](https://github.com/tensorflow/models/blob/master/official/recommendation/ncf_keras_main.py) example trained using `tf.distribute.MirroredStrategy`.\n",
+        "\n",
+        "More examples listed in the [Distribution Strategy Guide](https://www.tensorflow.org/guide/distribute_strategy#examples_and_tutorials)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {

site/en/r2/tutorials/distribute/training_loops.ipynb

@@ -713,6 +713,26 @@
         "If you use `tf.metrics.Mean` to track loss across the two replicas, the result is different. In this example, you end up with a `total` of 3.50 and `count` of 2, which results in `total`/`count` = 1.75  when `result()` is called on the metric. Loss calculated with `tf.keras.Metrics` is scaled by an additional factor that is equal to the number of replicas in sync."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "xisYJaV9KZTN"
+      },
+      "source": [
+        "### Examples and Tutorials\n",
+        "Here are some examples for using distribution strategy with custom training loops:\n",
+        "\n",
+        "1. [Tutorial](../tutorials/distribute/training_loops.ipynb) to train MNIST using `MirroredStrategy`.\n",
+        "2. [DenseNet](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/models/densenet/distributed_train.py) example using `MirroredStrategy`.\n",
+        "1. [BERT](https://github.com/tensorflow/models/blob/master/official/bert/run_classifier.py) example trained using `MirroredStrategy` and `TPUStrategy`.\n",
+        "This example is particularly helpful for understanding how to load from a checkpoint and generate periodic checkpoints during distributed training etc.\n",
+        "2. [NCF](https://github.com/tensorflow/models/blob/master/official/recommendation/ncf_keras_main.py) example trained using `MirroredStrategy` that can be enabled using the `keras_use_ctl` flag.\n",
+        "3. [NMT](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/models/nmt_with_attention/distributed_train.py) example trained using `MirroredStrategy`.\n",
+        "\n",
+        "More examples listed in the [Distribution Strategy Guide](https://www.tensorflow.org/guide/distribute_strategy#examples_and_tutorials)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {