Clarify multi_worker_with_keras.ipynb example code (#1)

Suggesting that the "mnist.py" file be renamed to something else like "mnist_setup.py" since the resulting "import mnist" is confusingly in conflict with the mnist package. This threw me off until I re-read the instructions, since I had placed the code in one file which resulted in errors executing mnist.mnist_dataset and mnist.build_and_compile_cnn_model.

site/en/tutorials/distribute/multi_worker_with_keras.ipynb

@@ -194,7 +194,7 @@
         "id": "fLW6D2TzvC-4"
       },
       "source": [
-        "Next, create an `mnist.py` file with a simple model and dataset setup. This Python file will be used by the worker-processes in this tutorial:"
+        "Next, create an `mnist_setup.py` file with a simple model and dataset setup. This Python file will be used by the worker-processes in this tutorial:"
       ]
     },
     {
@@ -205,7 +205,7 @@
       },
       "outputs": [],
       "source": [
-        "%%writefile mnist.py\n",
+        "%%writefile mnist_setup.py\n",
         "\n",
         "import os\n",
         "import tensorflow as tf\n",
@@ -256,11 +256,11 @@
       },
       "outputs": [],
       "source": [
-        "import mnist\n",
+        "import mnist_setup\n",
         "\n",
         "batch_size = 64\n",
-        "single_worker_dataset = mnist.mnist_dataset(batch_size)\n",
-        "single_worker_model = mnist.build_and_compile_cnn_model()\n",
+        "single_worker_dataset = mnist_setup.mnist_dataset(batch_size)\n",
+        "single_worker_model = mnist_setup.build_and_compile_cnn_model()\n",
         "single_worker_model.fit(single_worker_dataset, epochs=3, steps_per_epoch=70)"
       ]
     },
@@ -492,7 +492,7 @@
       "source": [
         "with strategy.scope():\n",
         "  # Model building/compiling need to be within `strategy.scope()`.\n",
-        "  multi_worker_model = mnist.build_and_compile_cnn_model()"
+        "  multi_worker_model = mnist_setup.build_and_compile_cnn_model()"
       ]
     },
     {
@@ -512,7 +512,7 @@
       "source": [
         "To actually run with `MultiWorkerMirroredStrategy` you'll need to run worker processes and pass a `TF_CONFIG` to them.\n",
         "\n",
-        "Like the `mnist.py` file written earlier, here is the `main.py` that each of the workers will run:"
+        "Like the `mnist_setup.py` file written earlier, here is the `main.py` that each of the workers will run:"
       ]
     },
     {
@@ -529,7 +529,7 @@
         "import json\n",
         "\n",
         "import tensorflow as tf\n",
-        "import mnist\n",
+        "import mnist_setup\n",
         "\n",
         "per_worker_batch_size = 64\n",
         "tf_config = json.loads(os.environ['TF_CONFIG'])\n",
@@ -538,11 +538,11 @@
         "strategy = tf.distribute.MultiWorkerMirroredStrategy()\n",
         "\n",
         "global_batch_size = per_worker_batch_size * num_workers\n",
-        "multi_worker_dataset = mnist.mnist_dataset(global_batch_size)\n",
+        "multi_worker_dataset = mnist_setup.mnist_dataset(global_batch_size)\n",
         "\n",
         "with strategy.scope():\n",
         "  # Model building/compiling need to be within `strategy.scope()`.\n",
-        "  multi_worker_model = mnist.build_and_compile_cnn_model()\n",
+        "  multi_worker_model = mnist_setup.build_and_compile_cnn_model()\n",
         "\n",
         "\n",
         "multi_worker_model.fit(multi_worker_dataset, epochs=3, steps_per_epoch=70)"
@@ -820,7 +820,7 @@
         "options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF\n",
         "\n",
         "global_batch_size = 64\n",
-        "multi_worker_dataset = mnist.mnist_dataset(batch_size=64)\n",
+        "multi_worker_dataset = mnist_setup.mnist_dataset(batch_size=64)\n",
         "dataset_no_auto_shard = multi_worker_dataset.with_options(options)"
       ]
     },
@@ -1146,7 +1146,7 @@
         "\n",
         "callbacks = [tf.keras.callbacks.BackupAndRestore(backup_dir='/tmp/backup')]\n",
         "with strategy.scope():\n",
-        "  multi_worker_model = mnist.build_and_compile_cnn_model()\n",
+        "  multi_worker_model = mnist_setup.build_and_compile_cnn_model()\n",
         "multi_worker_model.fit(multi_worker_dataset,\n",
         "                       epochs=3,\n",
         "                       steps_per_epoch=70,\n",