Run nbfmt on guide notebooks

markmcd · markmcd · commit 9eab37cc1b71 · 2023-06-13T10:20:45.000+08:00
In #2232, the nbfmt check was failing, so I've run it over all guides.
diff --git a/site/en/guide/basics.ipynb b/site/en/guide/basics.ipynb
@@ -956,7 +956,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "basics.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/core/distribution.ipynb b/site/en/guide/core/distribution.ipynb
@@ -13,6 +13,7 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
+        "cellView": "form",
         "id": "AwOEIRJC6Une"
       },
       "outputs": [],
@@ -337,9 +338,7 @@
         "\n",
         "Create a DTensor mesh that consists of a single batch dimension, where each device becomes a replica that receives a shard from the global batch. Use this mesh to instantiate an MLP mode with the following architecture:\n",
         "\n",
-        "Forward Pass: ReLU(784 x 700) x ReLU(700 x 500) x Softmax(500 x 10)\n",
-        "\n",
-        "\n"
+        "Forward Pass: ReLU(784 x 700) x ReLU(700 x 500) x Softmax(500 x 10)\n"
       ]
     },
     {
@@ -663,8 +662,7 @@
       "source": [
         "## Saving your model\n",
         "\n",
-        "The integration of `tf.saved_model` and DTensor is still under development. As of TensorFlow 2.9.0, tf.saved_model only accepts DTensor models with fully replicated variables. As a workaround, you can convert a DTensor model to a fully replicated one by reloading a checkpoint. However, after a model is saved, all DTensor annotations are lost and the saved signatures can only be used with regular Tensors. This tutorial will be updated to showcase the integration once it is solidified.\n",
-        "\n"
+        "The integration of `tf.saved_model` and DTensor is still under development. As of TensorFlow 2.9.0, tf.saved_model only accepts DTensor models with fully replicated variables. As a workaround, you can convert a DTensor model to a fully replicated one by reloading a checkpoint. However, after a model is saved, all DTensor annotations are lost and the saved signatures can only be used with regular Tensors. This tutorial will be updated to showcase the integration once it is solidified.\n"
       ]
     },
     {
@@ -690,7 +688,6 @@
         "FhGuhbZ6M5tl"
       ],
       "name": "distribution.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/core/matrix_core.ipynb b/site/en/guide/core/matrix_core.ipynb
@@ -13,6 +13,7 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
+        "cellView": "form",
         "id": "AwOEIRJC6Une"
       },
       "outputs": [],
@@ -718,7 +719,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "matrix_core.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/core/mlp_core.ipynb b/site/en/guide/core/mlp_core.ipynb
@@ -13,6 +13,7 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
+        "cellView": "form",
         "id": "AwOEIRJC6Une"
       },
       "outputs": [],
@@ -817,7 +818,6 @@
         "id": "_zZxO8iqBGZ-"
       },
       "source": [
-        "\n",
         "Load the saved model with `tf.saved_model.load` and examine its performance on the unseen test data."
       ]
     },
@@ -952,10 +952,8 @@
         "FhGuhbZ6M5tl"
       ],
       "name": "mlp_core.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
-    "gpuClass": "standard",
     "kernelspec": {
       "display_name": "Python 3",
       "name": "python3"
diff --git a/site/en/guide/core/quickstart_core.ipynb b/site/en/guide/core/quickstart_core.ipynb
@@ -579,7 +579,6 @@
         "rX8mhOLljYeM"
       ],
       "name": "quickstart_core.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/data_performance.ipynb b/site/en/guide/data_performance.ipynb
@@ -1153,7 +1153,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "data_performance.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/estimator.ipynb b/site/en/guide/estimator.ipynb
@@ -869,7 +869,6 @@
         "A_lvUsSLZzVg"
       ],
       "name": "estimator.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/function.ipynb b/site/en/guide/function.ipynb
@@ -1839,7 +1839,6 @@
   "metadata": {
     "colab": {
       "name": "function.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/intro_to_graphs.ipynb b/site/en/guide/intro_to_graphs.ipynb
@@ -859,7 +859,6 @@
   "metadata": {
     "colab": {
       "name": "intro_to_graphs.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/intro_to_modules.ipynb b/site/en/guide/intro_to_modules.ipynb
@@ -702,10 +702,10 @@
       ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {
         "id": "ds08u3touwe4t"
       },
-      "cell_type": "markdown",
       "source": [
         "Keras layers and models have a lot more extra features including:\n",
         "\n",
@@ -1086,10 +1086,10 @@
       ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {
         "id": "seLIUG2354s"
       },
-      "cell_type": "markdown",
       "source": [
         "### Checkpointing Keras models\n",
         "\n",
diff --git a/site/en/guide/jax2tf.ipynb b/site/en/guide/jax2tf.ipynb
@@ -479,10 +479,10 @@
       ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {
         "id": "FJdsKghBNF"
       },
-      "cell_type": "markdown",
       "source": [
         "## Train the model"
       ]
@@ -832,29 +832,17 @@
         "## Next steps\n",
         "You can learn more about [JAX](https://jax.readthedocs.io/en/latest/index.html) and [Flax](https://flax.readthedocs.io/en/latest) on their documentation websites which contain detailed guides and examples. If you're new to JAX, be sure to explore the [JAX 101 tutorials](https://jax.readthedocs.io/en/latest/jax-101/index.html), and check out the [Flax quickstart](https://flax.readthedocs.io/en/latest/getting_started.html). To learn more about converting JAX models to TensorFlow format, check out the [jax2tf](https://github.com/google/jax/tree/main/jax/experimental/jax2tf) utility on GitHub. If you're interested in converting JAX models to run in the browser with TensorFlow.js, visit [JAX on the Web with TensorFlow.js](https://blog.tensorflow.org/2022/08/jax-on-web-with-tensorflowjs.html). If you'd like to prepare JAX models to run in TensorFLow Lite, visit the [JAX Model Conversion For TFLite](https://www.tensorflow.org/lite/examples/jax_conversion/overview) guide."
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "4zgNWds-yaza"
-      },
-      "outputs": [],
-      "source": []
     }
   ],
   "metadata": {
     "accelerator": "GPU",
     "colab": {
-      "provenance": [],
+      "name": "jax2tf.ipynb",
       "toc_visible": true
     },
     "kernelspec": {
       "display_name": "Python 3",
       "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
     }
   },
   "nbformat": 4,
diff --git a/site/en/guide/migrate/evaluator.ipynb b/site/en/guide/migrate/evaluator.ipynb
@@ -13,6 +13,7 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
+        "cellView": "form",
         "id": "HMUDt0CiUJk9"
       },
       "outputs": [],
@@ -279,7 +280,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "evaluator.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/migrate/multi_worker_cpu_gpu_training.ipynb b/site/en/guide/migrate/multi_worker_cpu_gpu_training.ipynb
@@ -464,7 +464,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "multi_worker_cpu_gpu_training.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/migrate/saved_model.ipynb b/site/en/guide/migrate/saved_model.ipynb
@@ -755,7 +755,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "saved_model.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/migrate/tensorboard.ipynb b/site/en/guide/migrate/tensorboard.ipynb
@@ -279,7 +279,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "tensorboard.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/migrate/tpu_embedding.ipynb b/site/en/guide/migrate/tpu_embedding.ipynb
@@ -577,7 +577,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "tpu_embedding.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/saved_model.ipynb b/site/en/guide/saved_model.ipynb
@@ -104,7 +104,6 @@
         "id": "eLSOptpYhJvE"
       },
       "source": [
-        "\n",
         "For a quick introduction, this section exports a pre-trained Keras model and serves image classification requests with it. The rest of the guide will fill in details and discuss other ways to create SavedModels."
       ]
     },
@@ -996,7 +995,6 @@
   "metadata": {
     "colab": {
       "name": "saved_model.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/tensor.ipynb b/site/en/guide/tensor.ipynb
@@ -1564,7 +1564,6 @@
         "Tce3stUlHN0L"
       ],
       "name": "tensor.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {
diff --git a/site/en/guide/tensor_slicing.ipynb b/site/en/guide/tensor_slicing.ipynb
@@ -635,7 +635,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "tensor_slicing.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {

Original file line number	Diff line number	Diff line change
`@@ -702,10 +702,10 @@`
`702`	`702`	`]`
`703`	`703`	`},`
`704`	`704`	`{`
	`705`	`+ "cell_type": "markdown",`
`705`	`706`	`"metadata": {`
`706`	`707`	`"id": "ds08u3touwe4t"`
`707`	`708`	`},`
`708`		`- "cell_type": "markdown",`
`709`	`709`	`"source": [`
`710`	`710`	`"Keras layers and models have a lot more extra features including:\n",`
`711`	`711`	`"\n",`
`@@ -1086,10 +1086,10 @@`
`1086`	`1086`	`]`
`1087`	`1087`	`},`
`1088`	`1088`	`{`
	`1089`	`+ "cell_type": "markdown",`
`1089`	`1090`	`"metadata": {`
`1090`	`1091`	`"id": "seLIUG2354s"`
`1091`	`1092`	`},`
`1092`		`- "cell_type": "markdown",`
`1093`	`1093`	`"source": [`
`1094`	`1094`	`"### Checkpointing Keras models\n",`
`1095`	`1095`	`"\n",`