diff --git a/docs/cudf/source/user_guide/10min.ipynb b/docs/cudf/source/user_guide/10min.ipynb
index 080fce3c55c..b9278151e64 100644
--- a/docs/cudf/source/user_guide/10min.ipynb
+++ b/docs/cudf/source/user_guide/10min.ipynb
@@ -2,7 +2,6 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e9357872",
    "metadata": {},
    "source": [
     "10 Minutes to cuDF and Dask-cuDF\n",
@@ -27,7 +26,6 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "92eed4cb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +45,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed6c6047",
    "metadata": {},
    "source": [
     "Object Creation\n",
@@ -56,7 +53,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aeedd961",
    "metadata": {},
    "source": [
     "Creating a `cudf.Series` and `dask_cudf.Series`."
@@ -65,7 +61,6 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "cf8b08e5",
    "metadata": {},
    "outputs": [
     {
@@ -92,7 +87,6 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "083a5898",
    "metadata": {},
    "outputs": [
     {
@@ -118,7 +112,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6346e1b1",
    "metadata": {},
    "source": [
     "Creating a `cudf.DataFrame` and a `dask_cudf.DataFrame` by specifying values for each column."
@@ -127,7 +120,6 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "83d1e7f5",
    "metadata": {},
    "outputs": [
     {
@@ -321,7 +313,6 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "71b61d62",
    "metadata": {},
    "outputs": [
     {
@@ -511,7 +502,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c7cb5abc",
    "metadata": {},
    "source": [
     "Creating a `cudf.DataFrame` from a pandas `Dataframe` and a `dask_cudf.Dataframe` from a `cudf.Dataframe`.\n",
@@ -522,7 +512,6 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "07a62244",
    "metadata": {},
    "outputs": [
     {
@@ -597,7 +586,6 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "f5cb0c65",
    "metadata": {},
    "outputs": [
     {
@@ -670,7 +658,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "025eac40",
    "metadata": {},
    "source": [
     "Viewing Data\n",
@@ -679,7 +666,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47a567e8",
    "metadata": {},
    "source": [
     "Viewing the top rows of a GPU dataframe."
@@ -688,7 +674,6 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "ab8cbdb8",
    "metadata": {},
    "outputs": [
     {
@@ -752,7 +737,6 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "2e923d8a",
    "metadata": {},
    "outputs": [
     {
@@ -815,7 +799,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61257b4b",
    "metadata": {},
    "source": [
     "Sorting by values."
@@ -824,7 +807,6 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "512770f9",
    "metadata": {},
    "outputs": [
     {
@@ -1014,7 +996,6 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "1a13993f",
    "metadata": {},
    "outputs": [
     {
@@ -1203,7 +1184,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "19bce4c4",
    "metadata": {},
    "source": [
     "Selection\n",
@@ -1214,7 +1194,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba55980e",
    "metadata": {},
    "source": [
     "Selecting a single column, which initially yields a `cudf.Series` or `dask_cudf.Series`. Calling `compute` results in a `cudf.Series` (equivalent to `df.a`)."
@@ -1223,7 +1202,6 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "885989a6",
    "metadata": {},
    "outputs": [
     {
@@ -1264,7 +1242,6 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "14a74255",
    "metadata": {},
    "outputs": [
     {
@@ -1304,7 +1281,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "498d79f2",
    "metadata": {},
    "source": [
     "## Selection by Label"
@@ -1312,7 +1288,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b8b8e13",
    "metadata": {},
    "source": [
     "Selecting rows from index 2 to index 5 from columns 'a' and 'b'."
@@ -1321,7 +1296,6 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "d40bc19c",
    "metadata": {},
    "outputs": [
     {
@@ -1394,7 +1368,6 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "7688535b",
    "metadata": {},
    "outputs": [
     {
@@ -1466,7 +1439,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a64ce7a",
    "metadata": {},
    "source": [
     "## Selection by Position"
@@ -1474,7 +1446,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dfba2bb2",
    "metadata": {},
    "source": [
     "Selecting via integers and integer slices, like numpy/pandas. Note that this functionality is not available for Dask-cuDF DataFrames."
@@ -1483,7 +1454,6 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "fb8d6d43",
    "metadata": {},
    "outputs": [
     {
@@ -1507,7 +1477,6 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "263231da",
    "metadata": {},
    "outputs": [
     {
@@ -1573,7 +1542,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2223b089",
    "metadata": {},
    "source": [
     "You can also select elements of a `DataFrame` or `Series` with direct index access."
@@ -1582,7 +1550,6 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "13f6158b",
    "metadata": {},
    "outputs": [
     {
@@ -1646,7 +1613,6 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "3cf4aa26",
    "metadata": {},
    "outputs": [
     {
@@ -1668,7 +1634,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff633b2d",
    "metadata": {},
    "source": [
     "## Boolean Indexing"
@@ -1676,7 +1641,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bbdef48f",
    "metadata": {},
    "source": [
     "Selecting rows in a `DataFrame` or `Series` by direct Boolean indexing."
@@ -1685,7 +1649,6 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "becb916f",
    "metadata": {},
    "outputs": [
     {
@@ -1763,7 +1726,6 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "b9475c43",
    "metadata": {},
    "outputs": [
     {
@@ -1840,7 +1802,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ecf982f5",
    "metadata": {},
    "source": [
     "Selecting values from a `DataFrame` where a Boolean condition is met, via the `query` API."
@@ -1849,7 +1810,6 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "fc2fc9f9",
    "metadata": {},
    "outputs": [
     {
@@ -1906,7 +1866,6 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "1a05a07f",
    "metadata": {},
    "outputs": [
     {
@@ -1962,7 +1921,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f8955a0",
    "metadata": {},
    "source": [
     "You can also pass local variables to Dask-cuDF queries, via the `local_dict` keyword. With standard cuDF, you may either use the `local_dict` keyword or directly pass the variable via the `@` keyword. Supported logical operators include `>`, `<`, `>=`, `<=`, `==`, and `!=`."
@@ -1971,7 +1929,6 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "49485a4b",
    "metadata": {},
    "outputs": [
     {
@@ -2029,7 +1986,6 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "0f3a9116",
    "metadata": {},
    "outputs": [
     {
@@ -2086,7 +2042,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c355af07",
    "metadata": {},
    "source": [
     "Using the `isin` method for filtering."
@@ -2095,7 +2050,6 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "f44a5a57",
    "metadata": {},
    "outputs": [
     {
@@ -2158,7 +2112,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79a50beb",
    "metadata": {},
    "source": [
     "## MultiIndex"
@@ -2166,7 +2119,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14e70234",
    "metadata": {},
    "source": [
     "cuDF supports hierarchical indexing of DataFrames using MultiIndex. Grouping hierarchically (see `Grouping` below) automatically produces a DataFrame with a MultiIndex."
@@ -2175,7 +2127,6 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "882973ed",
    "metadata": {},
    "outputs": [
     {
@@ -2202,7 +2153,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c10971cc",
    "metadata": {},
    "source": [
     "This index can back either axis of a DataFrame."
@@ -2211,7 +2161,6 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "5417aeb9",
    "metadata": {},
    "outputs": [
     {
@@ -2289,7 +2238,6 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "4d6fb4ff",
    "metadata": {},
    "outputs": [
     {
@@ -2363,7 +2311,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63dc11d8",
    "metadata": {},
    "source": [
     "Accessing values of a DataFrame with a MultiIndex. Note that slicing is not yet supported."
@@ -2372,7 +2319,6 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "3644920c",
    "metadata": {},
    "outputs": [
     {
@@ -2394,7 +2340,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "697a9a36",
    "metadata": {},
    "source": [
     "Missing Data\n",
@@ -2403,7 +2348,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86655274",
    "metadata": {},
    "source": [
     "Missing data can be replaced by using the `fillna` method."
@@ -2412,7 +2356,6 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "28b06c52",
    "metadata": {},
    "outputs": [
     {
@@ -2438,7 +2381,6 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "7fb6a126",
    "metadata": {},
    "outputs": [
     {
@@ -2463,7 +2405,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a0b732f",
    "metadata": {},
    "source": [
     "Operations\n",
@@ -2472,7 +2413,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e8b0464",
    "metadata": {},
    "source": [
     "## Stats"
@@ -2480,7 +2420,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7523512b",
    "metadata": {},
    "source": [
     "Calculating descriptive statistics for a `Series`."
@@ -2489,7 +2428,6 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "f7cb604e",
    "metadata": {},
    "outputs": [
     {
@@ -2510,7 +2448,6 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "b8957a5f",
    "metadata": {},
    "outputs": [
     {
@@ -2530,7 +2467,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71fa928a",
    "metadata": {},
    "source": [
     "## Applymap"
@@ -2538,7 +2474,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d98d6f7b",
    "metadata": {},
    "source": [
     "Applying functions to a `Series`. Note that applying user defined functions directly with Dask-cuDF is not yet implemented. For now, you can use [map_partitions](http://docs.dask.org/en/stable/generated/dask.dataframe.DataFrame.map_partitions.html) to apply a function to each partition of the distributed dataframe."
@@ -2547,17 +2482,8 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "5e627811",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mmccarty/miniconda3/envs/cudf_dev/lib/python3.8/site-packages/cudf/core/series.py:2223: FutureWarning: Series.applymap is deprecated and will be removed in a future cuDF release. Use Series.apply instead.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -2593,13 +2519,12 @@
     "def add_ten(num):\n",
     "    return num + 10\n",
     "\n",
-    "df['a'].applymap(add_ten)"
+    "df['a'].apply(add_ten)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "96cf628e",
    "metadata": {},
    "outputs": [
     {
@@ -2639,7 +2564,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd69c00a",
    "metadata": {},
    "source": [
     "## Histogramming"
@@ -2647,7 +2571,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39982866",
    "metadata": {},
    "source": [
     "Counting the number of occurrences of each unique value of variable."
@@ -2656,7 +2579,6 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "62808675",
    "metadata": {},
    "outputs": [
     {
@@ -2697,7 +2619,6 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "5b2a42ce",
    "metadata": {},
    "outputs": [
     {
@@ -2737,7 +2658,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d7e62e4",
    "metadata": {},
    "source": [
     "## String Methods"
@@ -2745,7 +2665,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e704eca",
    "metadata": {},
    "source": [
     "Like pandas, cuDF provides string processing methods in the `str` attribute of `Series`. Full documentation of string methods is a work in progress. Please see the cuDF API documentation for more information."
@@ -2754,7 +2673,6 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "c73e70bb",
    "metadata": {},
    "outputs": [
     {
@@ -2785,7 +2703,6 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "697c1c94",
    "metadata": {},
    "outputs": [
     {
@@ -2815,7 +2732,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dfc1371e",
    "metadata": {},
    "source": [
     "## Concat"
@@ -2823,7 +2739,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6fb9b53",
    "metadata": {},
    "source": [
     "Concatenating `Series` and `DataFrames` row-wise."
@@ -2832,7 +2747,6 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "60538bbd",
    "metadata": {},
    "outputs": [
     {
@@ -2864,7 +2778,6 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "17953847",
    "metadata": {},
    "outputs": [
     {
@@ -2895,7 +2808,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27f0d621",
    "metadata": {},
    "source": [
     "## Join"
@@ -2903,7 +2815,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd35f1a7",
    "metadata": {},
    "source": [
     "Performing SQL style merges. Note that the dataframe order is not maintained, but may be restored post-merge by sorting by the index."
@@ -2912,7 +2823,6 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "52ada00a",
    "metadata": {},
    "outputs": [
     {
@@ -3006,7 +2916,6 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "409fcf92",
    "metadata": {},
    "outputs": [
     {
@@ -3094,93 +3003,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9dcb86b",
-   "metadata": {},
-   "source": [
-    "## Append"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1f896819",
-   "metadata": {},
-   "source": [
-    "Appending values from another `Series` or array-like object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "9976c1ce",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mmccarty/miniconda3/envs/cudf_dev/lib/python3.8/site-packages/cudf/core/indexed_frame.py:2329: FutureWarning: append is deprecated and will be removed in a future version. Use concat instead.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0       1\n",
-       "1       2\n",
-       "2       3\n",
-       "3    <NA>\n",
-       "4       5\n",
-       "0       1\n",
-       "1       2\n",
-       "2       3\n",
-       "3    <NA>\n",
-       "4       5\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s.append(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "fe5c54ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0       1\n",
-       "1       2\n",
-       "2       3\n",
-       "3    <NA>\n",
-       "4       5\n",
-       "0       1\n",
-       "1       2\n",
-       "2       3\n",
-       "3    <NA>\n",
-       "4       5\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds2.append(ds2).compute()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9fa10ef3",
    "metadata": {},
    "source": [
     "## Grouping"
@@ -3188,7 +3010,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a6e41f5",
    "metadata": {},
    "source": [
     "Like pandas, cuDF and Dask-cuDF support the Split-Apply-Combine groupby paradigm."
@@ -3196,8 +3017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "id": "2a8cafa7",
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3209,7 +3029,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0179d60c",
    "metadata": {},
    "source": [
     "Grouping and then applying the `sum` function to the grouped data."
@@ -3217,8 +3036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "id": "7c56d186",
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -3281,7 +3099,7 @@
        "0         100   90  100         3"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3292,8 +3110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "id": "f8823b30",
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -3356,7 +3173,7 @@
        "0         100   90  100         3"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3367,7 +3184,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a84cb883",
    "metadata": {},
    "source": [
     "Grouping hierarchically then applying the `sum` function to grouped data."
@@ -3375,8 +3191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "2184e3ad",
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -3455,7 +3270,7 @@
        "0        1         27  30  27"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3466,8 +3281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "id": "4ec311c1",
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -3546,7 +3360,7 @@
        "0        1         27  30  27"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3557,7 +3371,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dedfeb1b",
    "metadata": {},
    "source": [
     "Grouping and applying statistical functions to specific columns, using `agg`."
@@ -3565,8 +3378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "id": "2563d8b2",
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -3625,7 +3437,7 @@
        "0         19   9.0  100"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3636,8 +3448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "id": "22c77e75",
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -3696,7 +3507,7 @@
        "0         19   9.0  100"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3707,7 +3518,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d074822",
    "metadata": {},
    "source": [
     "## Transpose"
@@ -3715,7 +3525,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16c0f0a8",
    "metadata": {},
    "source": [
     "Transposing a dataframe, using either the `transpose` method or `T` property. Currently, all columns must have the same type. Transposing is not currently implemented in Dask-cuDF."
@@ -3723,8 +3532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "id": "e265861e",
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -3779,7 +3587,7 @@
        "2  3  6"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3791,8 +3599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "id": "1fe9b972",
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -3844,7 +3651,7 @@
        "b  4  5  6"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3855,7 +3662,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ce02827",
    "metadata": {},
    "source": [
     "Time Series\n",
@@ -3864,7 +3670,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fec907ff",
    "metadata": {},
    "source": [
     "`DataFrames` supports `datetime` typed columns, which allow users to interact with and filter data based on specific timestamps."
@@ -3872,8 +3677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "id": "7a425d3f",
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -3934,7 +3738,7 @@
        "3 2018-11-23  0.103839"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3952,8 +3756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "id": "87f0e56e",
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -4014,7 +3817,7 @@
        "3 2018-11-23  0.103839"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4026,7 +3829,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d0e541c",
    "metadata": {},
    "source": [
     "Categoricals\n",
@@ -4035,7 +3837,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a36f9543",
    "metadata": {},
    "source": [
     "`DataFrames` support categorical columns."
@@ -4043,8 +3844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "id": "05bd8be8",
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -4117,7 +3917,7 @@
        "5   6     e"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4130,8 +3930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "id": "676b4963",
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -4204,7 +4003,7 @@
        "5   6     e"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4216,7 +4015,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e24f2e7b",
    "metadata": {},
    "source": [
     "Accessing the categories of a column. Note that this is currently not supported in Dask-cuDF."
@@ -4224,8 +4022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "id": "06310c36",
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -4234,7 +4031,7 @@
        "StringIndex(['a' 'b' 'e'], dtype='object')"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4245,7 +4042,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4eb6f858",
    "metadata": {},
    "source": [
     "Accessing the underlying code values of each categorical observation."
@@ -4253,8 +4049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
-   "id": "0f6db260",
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -4269,7 +4064,7 @@
        "dtype: uint8"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4280,8 +4075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
-   "id": "b87c4375",
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -4296,7 +4090,7 @@
        "dtype: uint8"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4307,7 +4101,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f816916",
    "metadata": {},
    "source": [
     "Converting Data Representation\n",
@@ -4316,7 +4109,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64a17f6d",
    "metadata": {},
    "source": [
     "## Pandas"
@@ -4324,7 +4116,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3acdcacc",
    "metadata": {},
    "source": [
     "Converting a cuDF and Dask-cuDF `DataFrame` to a pandas `DataFrame`."
@@ -4332,8 +4123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
-   "id": "d1fed919",
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -4418,7 +4208,7 @@
        "4  4  15  4         1         0"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4429,8 +4219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "id": "567c7363",
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -4515,7 +4304,7 @@
        "4  4  15  4         1         0"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4526,7 +4315,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2121453",
    "metadata": {},
    "source": [
     "## Numpy"
@@ -4534,7 +4322,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9faa2c5",
    "metadata": {},
    "source": [
     "Converting a cuDF or Dask-cuDF `DataFrame` to a numpy `ndarray`."
@@ -4542,8 +4329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "id": "5490d226",
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -4571,7 +4357,7 @@
        "       [19,  0, 19,  0,  0]])"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4582,8 +4368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
-   "id": "b77ac8ae",
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -4611,7 +4396,7 @@
        "       [19,  0, 19,  0,  0]])"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4622,7 +4407,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d24d30f",
    "metadata": {},
    "source": [
     "Converting a cuDF or Dask-cuDF `Series` to a numpy `ndarray`."
@@ -4630,8 +4414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
-   "id": "f71a0ba3",
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -4641,7 +4424,7 @@
        "       17, 18, 19])"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4652,8 +4435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
-   "id": "a45a74b5",
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -4663,7 +4445,7 @@
        "       17, 18, 19])"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4674,7 +4456,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d78a4d2",
    "metadata": {},
    "source": [
     "## Arrow"
@@ -4682,7 +4463,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e35b829",
    "metadata": {},
    "source": [
     "Converting a cuDF or Dask-cuDF `DataFrame` to a PyArrow `Table`."
@@ -4690,8 +4470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "id": "bb9e9a2a",
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -4711,7 +4490,7 @@
        "agg_col2: [[1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0]]"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4722,8 +4501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "id": "4d020de7",
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -4743,7 +4521,7 @@
        "agg_col2: [[1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0]]"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4754,7 +4532,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ace7b4f9",
    "metadata": {},
    "source": [
     "Getting Data In/Out\n",
@@ -4763,7 +4540,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "161abb12",
    "metadata": {},
    "source": [
     "## CSV"
@@ -4771,7 +4547,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e5dc381",
    "metadata": {},
    "source": [
     "Writing to a CSV file."
@@ -4779,8 +4554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "id": "3a59715f",
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4792,8 +4566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "id": "4ebe98ed",
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4802,7 +4575,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0479fc4f",
    "metadata": {},
    "source": [
     "Reading from a csv file."
@@ -4810,8 +4582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "id": "1a70e831",
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -5031,7 +4802,7 @@
        "19  19   0  19         0         0"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5043,8 +4814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "id": "4c3d9ca3",
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -5264,7 +5034,7 @@
        "19  19   0  19         0         0"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5276,7 +5046,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d739c6e",
    "metadata": {},
    "source": [
     "Reading all CSV files in a directory into a single `dask_cudf.DataFrame`, using the star wildcard."
@@ -5284,8 +5053,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
-   "id": "cb7187d2",
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -5685,7 +5453,7 @@
        "19  19   0  19         0         0"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5697,7 +5465,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0939a1e",
    "metadata": {},
    "source": [
     "## Parquet"
@@ -5705,7 +5472,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14e6a634",
    "metadata": {},
    "source": [
     "Writing to parquet files, using the CPU via PyArrow."
@@ -5713,8 +5479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
-   "id": "1812346f",
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5723,7 +5488,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "093cd0fe",
    "metadata": {},
    "source": [
     "Reading parquet files with a GPU-accelerated parquet reader."
@@ -5731,8 +5495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
-   "id": "2354b20b",
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -5952,7 +5715,7 @@
        "19  19   0  19         0         0"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5964,7 +5727,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "132c3ff2",
    "metadata": {},
    "source": [
     "Writing to parquet files from a `dask_cudf.DataFrame` using PyArrow under the hood."
@@ -5972,8 +5734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
-   "id": "c5d7686c",
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
@@ -5982,7 +5743,7 @@
        "(None,)"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5993,7 +5754,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d73d1dd",
    "metadata": {},
    "source": [
     "## ORC"
@@ -6001,7 +5761,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61b5f466",
    "metadata": {},
    "source": [
     "Reading ORC files."
@@ -6009,34 +5768,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
-   "id": "93364ff3",
+   "execution_count": 77,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/home/ashwin/workspace/rapids/cudf/python/cudf/cudf/tests/data/orc/TestOrcFile.test1.orc'"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import os\n",
     "from pathlib import Path\n",
-    "current_dir = os.path.dirname(os.path.realpath(\"__file__\"))\n",
-    "cudf_root = Path(current_dir).parents[3]\n",
-    "file_path = os.path.join(cudf_root, \"python\", \"cudf\", \"cudf\", \"tests\", \"data\", \"orc\", \"TestOrcFile.test1.orc\")\n",
-    "file_path"
+    "cudf_root = Path(\".\").absolute().parents[3]\n",
+    "orc_file = Path(\"python/cudf/cudf/tests/data/orc/TestOrcFile.test1.orc\")\n",
+    "file_path = cudf_root / orc_file"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
-   "id": "2b6785c7",
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -6127,7 +5871,7 @@
        "1  [{'key': 'chani', 'value': {'int1': 5, 'string...  "
       ]
      },
-     "execution_count": 80,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6139,7 +5883,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "238ce6a4",
    "metadata": {},
    "source": [
     "Dask Performance Tips\n",
@@ -6154,7 +5897,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3de9aeca",
    "metadata": {},
    "source": [
     "First, we set up a GPU cluster. With our `client` set up, Dask-cuDF computation will be distributed across the GPUs in the cluster."
@@ -6162,255 +5904,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
-   "id": "e4852d48",
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2022-04-21 13:26:06,860 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n",
-      "2022-04-21 13:26:06,904 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n"
+      "2022-05-12 22:41:08,024 - distributed.preloading - INFO - Import preload module: dask_cuda.initialize\n"
      ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-20d00fd5-c198-11ec-906c-c8d9d2247354</p>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "\n",
-       "        <tr>\n",
-       "        \n",
-       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
-       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> dask_cuda.LocalCUDACluster</td>\n",
-       "        \n",
-       "        </tr>\n",
-       "\n",
-       "        \n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\"></td>\n",
-       "            </tr>\n",
-       "        \n",
-       "\n",
-       "        </table>\n",
-       "\n",
-       "        \n",
-       "            <details>\n",
-       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
-       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
-       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
-       "    </div>\n",
-       "    <div style=\"margin-left: 48px;\">\n",
-       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCUDACluster</h3>\n",
-       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">47648c26</p>\n",
-       "        <table style=\"width: 100%; text-align: left;\">\n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Workers:</strong> 2\n",
-       "                </td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total threads:</strong> 2\n",
-       "                </td>\n",
-       "                <td style=\"text-align: left;\">\n",
-       "                    <strong>Total memory:</strong> 125.65 GiB\n",
-       "                </td>\n",
-       "            </tr>\n",
-       "            \n",
-       "            <tr>\n",
-       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
-       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
-       "</tr>\n",
-       "\n",
-       "            \n",
-       "        </table>\n",
-       "\n",
-       "        <details>\n",
-       "            <summary style=\"margin-bottom: 20px;\">\n",
-       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
-       "            </summary>\n",
-       "\n",
-       "            <div style=\"\">\n",
-       "    <div>\n",
-       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
-       "        <div style=\"margin-left: 48px;\">\n",
-       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
-       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-f28bff16-cb70-452c-b8af-b9299a8d7b20</p>\n",
-       "            <table style=\"width: 100%; text-align: left;\">\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Comm:</strong> tcp://127.0.0.1:33995\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Workers:</strong> 2\n",
-       "                    </td>\n",
-       "                </tr>\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Total threads:</strong> 2\n",
-       "                    </td>\n",
-       "                </tr>\n",
-       "                <tr>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Started:</strong> Just now\n",
-       "                    </td>\n",
-       "                    <td style=\"text-align: left;\">\n",
-       "                        <strong>Total memory:</strong> 125.65 GiB\n",
-       "                    </td>\n",
-       "                </tr>\n",
-       "            </table>\n",
-       "        </div>\n",
-       "    </div>\n",
-       "\n",
-       "    <details style=\"margin-left: 48px;\">\n",
-       "        <summary style=\"margin-bottom: 20px;\">\n",
-       "            <h3 style=\"display: inline;\">Workers</h3>\n",
-       "        </summary>\n",
-       "\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:40479\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 1\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:38985/status\" target=\"_blank\">http://127.0.0.1:38985/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 62.82 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33447\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /home/ashwin/workspace/rapids/cudf/docs/cudf/source/user_guide/dask-worker-space/worker-be7zg92w\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>GPU: </strong>NVIDIA RTX A6000\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>GPU memory: </strong> 47.51 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "        <div style=\"margin-bottom: 20px;\">\n",
-       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
-       "            <div style=\"margin-left: 48px;\">\n",
-       "            <details>\n",
-       "                <summary>\n",
-       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
-       "                </summary>\n",
-       "                <table style=\"width: 100%; text-align: left;\">\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Comm: </strong> tcp://127.0.0.1:40519\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Total threads: </strong> 1\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:40951/status\" target=\"_blank\">http://127.0.0.1:40951/status</a>\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Memory: </strong> 62.82 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>Nanny: </strong> tcp://127.0.0.1:39133\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\"></td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
-       "                            <strong>Local directory: </strong> /home/ashwin/workspace/rapids/cudf/docs/cudf/source/user_guide/dask-worker-space/worker-3v0c20ux\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>GPU: </strong>NVIDIA RTX A6000\n",
-       "                        </td>\n",
-       "                        <td style=\"text-align: left;\">\n",
-       "                            <strong>GPU memory: </strong> 47.54 GiB\n",
-       "                        </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "\n",
-       "                    \n",
-       "\n",
-       "                </table>\n",
-       "            </details>\n",
-       "            </div>\n",
-       "        </div>\n",
-       "        \n",
-       "\n",
-       "    </details>\n",
-       "</div>\n",
-       "\n",
-       "        </details>\n",
-       "    </div>\n",
-       "</div>\n",
-       "            </details>\n",
-       "        \n",
-       "\n",
-       "    </div>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "<Client: 'tcp://127.0.0.1:33995' processes=2 threads=2, memory=45.79 GiB>"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -6420,13 +5922,11 @@
     "from dask_cuda import LocalCUDACluster\n",
     "\n",
     "cluster = LocalCUDACluster()\n",
-    "client = Client(cluster)\n",
-    "client"
+    "client = Client(cluster)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "181e4d10",
    "metadata": {},
    "source": [
     "### Persisting Data\n",
@@ -6435,8 +5935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
-   "id": "d47a1142",
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -6512,7 +6011,7 @@
        "<dask_cudf.DataFrame | 20 tasks | 5 npartitions>"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6528,37 +6027,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "id": "c3cb612a",
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Thu Apr 21 13:26:07 2022       \r\n",
-      "+-----------------------------------------------------------------------------+\r\n",
-      "| NVIDIA-SMI 495.29.05    Driver Version: 495.29.05    CUDA Version: 11.5     |\r\n",
-      "|-------------------------------+----------------------+----------------------+\r\n",
-      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
-      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
-      "|                               |                      |               MIG M. |\r\n",
-      "|===============================+======================+======================|\r\n",
-      "|   0  Quadro GV100        Off  | 00000000:15:00.0 Off |                  Off |\r\n",
-      "| 39%   52C    P2    51W / 250W |   1115MiB / 32508MiB |      0%      Default |\r\n",
-      "|                               |                      |                  N/A |\r\n",
-      "+-------------------------------+----------------------+----------------------+\r\n",
-      "|   1  Quadro GV100        Off  | 00000000:2D:00.0 Off |                  Off |\r\n",
-      "| 43%   57C    P2    52W / 250W |    306MiB / 32498MiB |      0%      Default |\r\n",
-      "|                               |                      |                  N/A |\r\n",
-      "+-------------------------------+----------------------+----------------------+\r\n",
-      "                                                                               \r\n",
-      "+-----------------------------------------------------------------------------+\r\n",
-      "| Processes:                                                                  |\r\n",
-      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n",
-      "|        ID   ID                                                   Usage      |\r\n",
-      "|=============================================================================|\r\n",
-      "+-----------------------------------------------------------------------------+\r\n"
+      "Thu May 12 22:41:08 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA RTX A6000    On   | 00000000:65:00.0  On |                  Off |\n",
+      "| 30%   41C    P2    77W / 300W |   1380MiB / 49140MiB |      2%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|    0   N/A  N/A      1674      G                                     159MiB |\n",
+      "|    0   N/A  N/A      1950      G                                      47MiB |\n",
+      "|    0   N/A  N/A     13521      G                                     132MiB |\n",
+      "|    0   N/A  N/A    304797      G                                      36MiB |\n",
+      "|    0   N/A  N/A    488366      C                                     743MiB |\n",
+      "|    0   N/A  N/A    488425      C                                     257MiB |\n",
+      "+-----------------------------------------------------------------------------+\n"
      ]
     }
    ],
@@ -6568,7 +6068,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b98810c4",
    "metadata": {},
    "source": [
     "Because Dask is lazy, the computation has not yet occurred. We can see that there are twenty tasks in the task graph and we've used about 800 MB of memory. We can force computation by using `persist`. By forcing execution, the result is now explicitly in memory and our task graph only contains one task per partition (the baseline)."
@@ -6576,8 +6075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
-   "id": "a929577c",
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -6653,7 +6151,7 @@
        "<dask_cudf.DataFrame | 5 tasks | 5 npartitions>"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6665,28 +6163,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
-   "id": "8aa7c079",
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Thu Apr 21 13:26:08 2022       \r\n",
+      "Thu May 12 22:41:14 2022       \r\n",
       "+-----------------------------------------------------------------------------+\r\n",
-      "| NVIDIA-SMI 495.29.05    Driver Version: 495.29.05    CUDA Version: 11.5     |\r\n",
+      "| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |\r\n",
       "|-------------------------------+----------------------+----------------------+\r\n",
       "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
       "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
       "|                               |                      |               MIG M. |\r\n",
       "|===============================+======================+======================|\r\n",
-      "|   0  Quadro GV100        Off  | 00000000:15:00.0 Off |                  Off |\r\n",
-      "| 39%   52C    P2    52W / 250W |   1115MiB / 32508MiB |      3%      Default |\r\n",
-      "|                               |                      |                  N/A |\r\n",
-      "+-------------------------------+----------------------+----------------------+\r\n",
-      "|   1  Quadro GV100        Off  | 00000000:2D:00.0 Off |                  Off |\r\n",
-      "| 43%   57C    P2    51W / 250W |    306MiB / 32498MiB |      0%      Default |\r\n",
+      "|   0  NVIDIA RTX A6000    On   | 00000000:65:00.0  On |                  Off |\r\n",
+      "| 30%   42C    P2    77W / 300W |   1942MiB / 49140MiB |      0%      Default |\r\n",
       "|                               |                      |                  N/A |\r\n",
       "+-------------------------------+----------------------+----------------------+\r\n",
       "                                                                               \r\n",
@@ -6695,17 +6188,23 @@
       "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n",
       "|        ID   ID                                                   Usage      |\r\n",
       "|=============================================================================|\r\n",
+      "|    0   N/A  N/A      1674      G                                     159MiB |\r\n",
+      "|    0   N/A  N/A      1950      G                                      47MiB |\r\n",
+      "|    0   N/A  N/A     13521      G                                     132MiB |\r\n",
+      "|    0   N/A  N/A    304797      G                                      36MiB |\r\n",
+      "|    0   N/A  N/A    488366      C                                     743MiB |\r\n",
+      "|    0   N/A  N/A    488425      C                                     819MiB |\r\n",
       "+-----------------------------------------------------------------------------+\r\n"
      ]
     }
    ],
    "source": [
-    "!nvidia-smi"
+    "# Sleep to ensure the persist finishes and shows in the memory usage\n",
+    "!sleep 5; nvidia-smi"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ff9e14b6",
    "metadata": {},
    "source": [
     "Because we forced computation, we now have a larger object in distributed GPU memory."
@@ -6713,7 +6212,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb3b3dee",
    "metadata": {},
    "source": [
     "### Wait\n",
@@ -6724,8 +6222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
-   "id": "ef71bf00",
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6737,22 +6234,20 @@
     "ddf1 = dask_cudf.from_cudf(df1, npartitions=100)\n",
     "\n",
     "def func(df):\n",
-    "    time.sleep(random.randint(1, 60))\n",
+    "    time.sleep(random.randint(1, 10))\n",
     "    return (df + 5) * 3 - 11"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e1099ec0",
    "metadata": {},
    "source": [
-    "This function will do a basic transformation of every column in the dataframe, but the time spent in the function will vary due to the `time.sleep` statement randomly adding 1-60 seconds of time. We'll run this on every partition of our dataframe using `map_partitions`, which adds the task to our task-graph, and store the result. We can then call `persist` to force execution."
+    "This function will do a basic transformation of every column in the dataframe, but the time spent in the function will vary due to the `time.sleep` statement randomly adding 1-10 seconds of time. We'll run this on every partition of our dataframe using `map_partitions`, which adds the task to our task-graph, and store the result. We can then call `persist` to force execution."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
-   "id": "700dd799",
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6762,7 +6257,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5eb83a7e",
    "metadata": {},
    "source": [
     "However, some partitions will be done **much** sooner than others. If we had downstream processes that should wait for all partitions to be completed, we can enforce that behavior using `wait`."
@@ -6770,17 +6264,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "id": "73bccf94",
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "DoneAndNotDoneFutures(done={<Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-c8623f55c898739bdfb89533682776dc', 0)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-c8623f55c898739bdfb89533682776dc', 3)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-c8623f55c898739bdfb89533682776dc', 1)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-c8623f55c898739bdfb89533682776dc', 2)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-c8623f55c898739bdfb89533682776dc', 4)>}, not_done=set())"
+       "DoneAndNotDoneFutures(done={<Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-e00276b0bab130532f4e193ca28e94ac', 4)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-e00276b0bab130532f4e193ca28e94ac', 1)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-e00276b0bab130532f4e193ca28e94ac', 2)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-e00276b0bab130532f4e193ca28e94ac', 3)>, <Future: finished, type: cudf.core.dataframe.DataFrame, key: ('func-e00276b0bab130532f4e193ca28e94ac', 0)>}, not_done=set())"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6791,22 +6284,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "447301f5",
    "metadata": {},
    "source": [
     "With `wait`, we can safely proceed on in our workflow."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7e06fcf4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -6822,9 +6307,22 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.12"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,
- "nbformat_minor": 5
+ "nbformat_minor": 4
 }