Merge pull request #2156 from mohantym:new_api_load_data

PiperOrigin-RevId: 491664565

Author: copybara-github

site/en/tutorials/load_data/csv.ipynb

@@ -1066,7 +1066,7 @@
       "source": [
         "There is some overhead to parsing the CSV data. For small models this can be the bottleneck in training.\n",
         "\n",
-        "Depending on your use case, it may be a good idea to use `Dataset.cache` or `tf.data.experimental.snapshot`, so that the CSV data is only parsed on the first epoch.\n",
+        "Depending on your use case, it may be a good idea to use `Dataset.cache` or `tf.data.Dataset.snapshot`, so that the CSV data is only parsed on the first epoch.\n",
         "\n",
         "The main difference between the `cache` and `snapshot` methods is that `cache` files can only be used by the TensorFlow process that created them, but `snapshot` files can be read by other processes.\n",
         "\n",
@@ -1120,7 +1120,7 @@
         "id": "wN7uUBjmgNZ9"
       },
       "source": [
-        "Note: The `tf.data.experimental.snapshot` files are meant for *temporary* storage of a dataset while in use. This is *not* a format for long term storage. The file format is considered an internal detail, and not guaranteed between TensorFlow versions."
+        "Note: The `tf.data.Dataset.snapshot` files are meant for *temporary* storage of a dataset while in use. This is *not* a format for long term storage. The file format is considered an internal detail, and not guaranteed between TensorFlow versions."
       ]
     },
     {
@@ -1132,8 +1132,7 @@
       "outputs": [],
       "source": [
         "%%time\n",
-        "snapshot = tf.data.experimental.snapshot('titanic.tfsnap')\n",
-        "snapshotting = traffic_volume_csv_gz_ds.apply(snapshot).shuffle(1000)\n",
+        "snapshotting = traffic_volume_csv_gz_ds.snapshot('titanic.tfsnap').shuffle(1000)\n",
         "\n",
         "for i, (batch, label) in enumerate(snapshotting.shuffle(1000).repeat(20)):\n",
         "  if i % 40 == 0:\n",
@@ -1147,7 +1146,7 @@
         "id": "fUSSegnMCGRz"
       },
       "source": [
-        "If your data loading is slowed by loading CSV files, and `Dataset.cache` and `tf.data.experimental.snapshot` are insufficient for your use case, consider re-encoding your data into a more streamlined format."
+        "If your data loading is slowed by loading CSV files, and `Dataset.cache` and `tf.data.Dataset.snapshot` are insufficient for your use case, consider re-encoding your data into a more streamlined format."
       ]
     },
     {
@@ -1862,7 +1861,7 @@
       "source": [
         "For another example of increasing CSV performance by using large batches, refer to the [Overfit and underfit tutorial](../keras/overfit_and_underfit.ipynb).\n",
         "\n",
-        "This sort of approach may work, but consider other options like `Dataset.cache` and `tf.data.experimental.snapshot`, or re-encoding your data into a more streamlined format."
+        "This sort of approach may work, but consider other options like `Dataset.cache` and `tf.data.Dataset.snapshot`, or re-encoding your data into a more streamlined format."
       ]
     }
   ],