diff --git a/notebooks/explainer_examples.ipynb b/notebooks/explainer_examples.ipynb
index b13ed64a47..d75cdc69c4 100644
--- a/notebooks/explainer_examples.ipynb
+++ b/notebooks/explainer_examples.ipynb
@@ -79,7 +79,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Error from server (AlreadyExists): namespaces \"seldon\" already exists\r\n"
+      "Error from server (AlreadyExists): namespaces \"seldon\" already exists\n"
      ]
     }
    ],
@@ -103,7 +103,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Context \"kind-kind\" modified.\r\n"
+      "Context \"kind-kind\" modified.\n"
      ]
     }
    ],
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "scrolled": true
    },
@@ -177,7 +177,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io/income created\r\n"
+      "seldondeployment.machinelearning.seldon.io/income created\n"
      ]
     }
    ],
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -205,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "scrolled": true
    },
@@ -225,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "scrolled": true
    },
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
@@ -254,7 +254,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'data': {'names': ['t:0', 't:1'], 'tensor': {'shape': [1, 2], 'values': [0.8585304277244477, 0.14146957227555243]}}, 'meta': {}}\n"
+      "{'data': {'names': ['t:0', 't:1'], 'tensor': {'shape': [1, 2], 'values': [0.8585304277244477, 0.14146957227555243]}}, 'meta': {'requestPath': {'classifier': 'seldonio/sklearnserver:1.7.0-dev'}}}\n"
      ]
     }
    ],
@@ -273,14 +273,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{\"data\":{\"names\":[\"t:0\",\"t:1\"],\"ndarray\":[[0.8585304277244477,0.14146957227555243]]},\"meta\":{}}\r\n"
+      "{\"data\":{\"names\":[\"t:0\",\"t:1\"],\"ndarray\":[[0.8585304277244477,0.14146957227555243]]},\"meta\":{\"requestPath\":{\"classifier\":\"seldonio/sklearnserver:1.7.0-dev\"}}}\n"
      ]
     }
    ],
@@ -299,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {
     "scrolled": true
    },
@@ -327,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -336,9 +336,10 @@
      "text": [
       "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
       "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-      "100  1715  100  1624  100    91   8837    495 --:--:-- --:--:-- --:--:--  8826\n",
+      "100  2269  100  2178  100    91   8817    368 --:--:-- --:--:-- --:--:--  9149\n",
       "\u001b[1;39m[\n",
-      "  \u001b[0;32m\"Marital Status = Separated\"\u001b[0m\u001b[1;39m\n",
+      "  \u001b[0;32m\"Marital Status = Separated\"\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[0;32m\"Capital Gain <= 0.00\"\u001b[0m\u001b[1;39m\n",
       "\u001b[1;39m]\u001b[0m\n"
      ]
     }
@@ -351,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {
     "scrolled": true
    },
@@ -360,7 +361,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io \"income\" deleted\r\n"
+      "seldondeployment.machinelearning.seldon.io \"income\" deleted\n"
      ]
     }
    ],
@@ -379,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -414,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "metadata": {
     "scrolled": true
    },
@@ -423,7 +424,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io/movie created\r\n"
+      "seldondeployment.machinelearning.seldon.io/movie created\n"
      ]
     }
    ],
@@ -433,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
    "metadata": {
     "scrolled": true
    },
@@ -453,14 +454,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "deployment \"movie-default-explainer\" successfully rolled out\r\n"
+      "deployment \"movie-default-explainer\" successfully rolled out\n"
      ]
     }
    ],
@@ -470,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 23,
    "metadata": {
     "scrolled": true
    },
@@ -483,14 +484,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{\"data\":{\"names\":[\"t:0\",\"t:1\"],\"ndarray\":[[0.21266916924914636,0.7873308307508536]]},\"meta\":{}}\r\n"
+      "{\"data\":{\"names\":[\"t:0\",\"t:1\"],\"ndarray\":[[0.21266916924914636,0.7873308307508536]]},\"meta\":{\"requestPath\":{\"classifier\":\"seldonio/sklearnserver:1.7.0-dev\"}}}\n"
      ]
     }
    ],
@@ -502,7 +503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 27,
    "metadata": {
     "scrolled": true
    },
@@ -524,7 +525,7 @@
       "}\n",
       "\n",
       "Response:\n",
-      "{'data': {'names': ['t:0', 't:1'], 'ndarray': [[0.21266916924914636, 0.7873308307508536]]}, 'meta': {}}\n"
+      "{'data': {'names': ['t:0', 't:1'], 'ndarray': [[0.21266916924914636, 0.7873308307508536]]}, 'meta': {'requestPath': {'classifier': 'seldonio/sklearnserver:1.7.0-dev'}}}\n"
      ]
     }
    ],
@@ -537,17 +538,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1;39m[\r\n",
-      "  \u001b[0;32m\"emotionally\"\u001b[0m\u001b[1;39m,\r\n",
-      "  \u001b[0;32m\"vapid\"\u001b[0m\u001b[1;39m\r\n",
-      "\u001b[1;39m]\u001b[0m\r\n"
+      "\u001b[1;39m[\n",
+      "  \u001b[0;32m\"emotionally\"\u001b[0m\u001b[1;39m,\n",
+      "  \u001b[0;32m\"vapid\"\u001b[0m\u001b[1;39m\n",
+      "\u001b[1;39m]\u001b[0m\n"
      ]
     }
    ],
@@ -559,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 29,
    "metadata": {
     "scrolled": true
    },
@@ -580,7 +581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 30,
    "metadata": {
     "scrolled": true
    },
@@ -589,7 +590,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io \"movie\" deleted\r\n"
+      "seldondeployment.machinelearning.seldon.io \"movie\" deleted\n"
      ]
     }
    ],
@@ -608,14 +609,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Overwriting resources/cifar10_explainer.yaml\n"
+      "Writing resources/cifar10_explainer.yaml\n"
      ]
     }
    ],
@@ -645,14 +646,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io/cifar10-classifier created\r\n"
+      "seldondeployment.machinelearning.seldon.io/cifar10-classifier created\n"
      ]
     }
    ],
@@ -662,14 +663,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "deployment \"cifar10-classifier-default-0-cifar10-classifier\" successfully rolled out\r\n"
+      "Waiting for deployment \"cifar10-classifier-default-0-cifar10-classifier\" rollout to finish: 0 of 1 updated replicas are available...\n",
+      "deployment \"cifar10-classifier-default-0-cifar10-classifier\" successfully rolled out\n"
      ]
     }
    ],
@@ -679,14 +681,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "deployment \"cifar10-classifier-default-explainer\" successfully rolled out\r\n"
+      "Waiting for deployment \"cifar10-classifier-default-explainer\" rollout to finish: 0 of 1 updated replicas are available...\n",
+      "deployment \"cifar10-classifier-default-explainer\" successfully rolled out\n"
      ]
     }
    ],
@@ -696,22 +699,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: Logging before flag parsing goes to stderr.\n",
-      "W1106 08:48:27.877613 139714443523840 deprecation.py:506] From /home/clive/anaconda3/envs/seldon-core/lib/python3.6/site-packages/tensorflow_core/python/keras/initializers.py:143: calling RandomNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "W1106 08:48:27.892694 139714443523840 deprecation.py:506] From /home/clive/anaconda3/envs/seldon-core/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "If using Keras pass *_constraint arguments to layers.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -743,7 +733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -751,7 +741,7 @@
      "output_type": "stream",
      "text": [
       "{\n",
-      "    \"predictions\": [[8.98417127e-08, 1.35163679e-12, 5.20754609e-13, 9.01404201e-05, 4.04729e-12, 0.999909759, 9.77382086e-09, 1.30629796e-09, 5.39957488e-12, 3.7917457e-14]\n",
+      "    \"predictions\": [[8.98418833e-08, 1.35163929e-12, 5.20754609e-13, 9.01406747e-05, 4.04729e-12, 0.999909759, 9.77383952e-09, 1.30630051e-09, 5.39958529e-12, 3.7917457e-14]\n",
       "    ]\n",
       "}\n"
      ]
@@ -803,16 +793,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f11753fd128>"
+       "<matplotlib.image.AxesImage at 0x7f4c12ea8350>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -845,14 +835,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io \"cifar10-classifier\" deleted\r\n"
+      "seldondeployment.machinelearning.seldon.io \"cifar10-classifier\" deleted\n"
      ]
     }
    ],
@@ -873,7 +863,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -926,7 +916,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -957,7 +947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -969,14 +959,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Overwriting resources/wine_explainer.yaml\n"
+      "Writing resources/wine_explainer.yaml\n"
      ]
     }
    ],
@@ -1008,7 +998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 43,
    "metadata": {
     "scrolled": true
    },
@@ -1017,7 +1007,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io/wine created\r\n"
+      "seldondeployment.machinelearning.seldon.io/wine created\n"
      ]
     }
    ],
@@ -1027,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1045,7 +1035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 45,
    "metadata": {
     "scrolled": true
    },
@@ -1054,7 +1044,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "deployment \"wine-default-explainer\" successfully rolled out\r\n"
+      "Waiting for deployment \"wine-default-explainer\" rollout to finish: 0 of 1 updated replicas are available...\n",
+      "deployment \"wine-default-explainer\" successfully rolled out\n"
      ]
     }
    ],
@@ -1064,7 +1055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 46,
    "metadata": {
     "scrolled": true
    },
@@ -1084,7 +1075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 47,
    "metadata": {
     "scrolled": true
    },
@@ -1093,7 +1084,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'data': {'names': ['t:0', 't:1', 't:2'], 'tensor': {'shape': [1, 3], 'values': [-0.203700284044519, 0.8934751316557469, 2.2237213335499804]}}, 'meta': {}}\n"
+      "{'data': {'names': ['t:0', 't:1', 't:2'], 'tensor': {'shape': [1, 3], 'values': [-0.203700284044519, 0.8934751316557469, 2.2237213335499804]}}, 'meta': {'requestPath': {'classifier': 'seldonio/sklearnserver:1.7.0-dev'}}}\n"
      ]
     }
    ],
@@ -1115,14 +1106,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{\"data\":{\"names\":[\"t:0\",\"t:1\",\"t:2\"],\"ndarray\":[[-0.203700284044519,0.8934751316557469,2.2237213335499804]]},\"meta\":{}}\r\n"
+      "{\"data\":{\"names\":[\"t:0\",\"t:1\",\"t:2\"],\"ndarray\":[[-0.203700284044519,0.8934751316557469,2.2237213335499804]]},\"meta\":{\"requestPath\":{\"classifier\":\"seldonio/sklearnserver:1.7.0-dev\"}}}\n"
      ]
     }
    ],
@@ -1141,7 +1132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 49,
    "metadata": {
     "scrolled": true
    },
@@ -1158,7 +1149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1168,7 +1159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1178,14 +1169,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "\n",
-       "<div id='i6LGNF7M130CTTSBQ55AX'>\n",
+       "<div id='iURPIHA3P5YOY4BH8I7XW'>\n",
        "<div style='color: #900; text-align: center;'>\n",
        "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
        "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
@@ -1195,8 +1186,8 @@
        "</div></div>\n",
        " <script>\n",
        "   if (window.SHAP) SHAP.ReactDom.render(\n",
-       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"model output value\"], \"baseValue\": 0.6946151446768677, \"outValue\": 2.2237213335499804, \"link\": \"identity\", \"featureNames\": [\"alcohol\", \"malic_acid\", \"ash\", \"alcalinity_of_ash\", \"magnesium\", \"total_phenols\", \"flavanoids\", \"nonflavanoid_phenols\", \"proanthocyanins\", \"color_intensity\", \"hue\", \"od280/od315_of_diluted_wines\", \"proline\"], \"features\": {\"0\": {\"effect\": 0.02519584309330747, \"value\": -0.24226334}, \"1\": {\"effect\": 0.020305711809383897, \"value\": 0.26757916}, \"2\": {\"effect\": 0.035097641360549825, \"value\": 0.42085937}, \"3\": {\"effect\": 0.05816050187989985, \"value\": 0.7127641}, \"4\": {\"effect\": 0.03751338001139182, \"value\": 0.84067236}, \"5\": {\"effect\": 0.11918464616095326, \"value\": -1.27747161}, \"6\": {\"effect\": 0.1835946462971263, \"value\": -0.60582812}, \"7\": {\"effect\": -0.02385561962591798, \"value\": -0.9706341}, \"8\": {\"effect\": 0.07059461355703256, \"value\": -0.5873972}, \"9\": {\"effect\": 0.28349674832426286, \"value\": 2.42611713}, \"10\": {\"effect\": 0.29511633663385317, \"value\": -2.06608025}, \"11\": {\"effect\": 0.3483160972485988, \"value\": -1.55017035}, \"12\": {\"effect\": 0.07638564212267096, \"value\": -0.86659858}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
-       "    document.getElementById('i6LGNF7M130CTTSBQ55AX')\n",
+       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"model output value\"], \"baseValue\": 0.6946151446768677, \"outValue\": 2.2237213335499804, \"link\": \"identity\", \"featureNames\": [\"alcohol\", \"malic_acid\", \"ash\", \"alcalinity_of_ash\", \"magnesium\", \"total_phenols\", \"flavanoids\", \"nonflavanoid_phenols\", \"proanthocyanins\", \"color_intensity\", \"hue\", \"od280/od315_of_diluted_wines\", \"proline\"], \"features\": {\"0\": {\"effect\": 0.01279797794339499, \"value\": -0.24226334}, \"1\": {\"effect\": 0.017311146050300708, \"value\": 0.26757916}, \"2\": {\"effect\": 0.03164141506225282, \"value\": 0.42085937}, \"3\": {\"effect\": 0.059886789568193155, \"value\": 0.7127641}, \"4\": {\"effect\": 0.03491430892995573, \"value\": 0.84067236}, \"5\": {\"effect\": 0.12505578522728522, \"value\": -1.27747161}, \"6\": {\"effect\": 0.1749657177337629, \"value\": -0.60582812}, \"7\": {\"effect\": -0.0240509342708497, \"value\": -0.9706341}, \"8\": {\"effect\": 0.07006494555857645, \"value\": -0.5873972}, \"9\": {\"effect\": 0.2996303904067048, \"value\": 2.42611713}, \"10\": {\"effect\": 0.2990334626760711, \"value\": -2.06608025}, \"11\": {\"effect\": 0.3494129728007027, \"value\": -1.55017035}, \"12\": {\"effect\": 0.07844221118676176, \"value\": -0.86659858}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
+       "    document.getElementById('iURPIHA3P5YOY4BH8I7XW')\n",
        "  );\n",
        "</script>"
       ],
@@ -1204,7 +1195,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1228,7 +1219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -1237,7 +1228,7 @@
      "text": [
       "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
       "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-      "100  4129  100  3916  100   213   1267     68  0:00:03  0:00:03 --:--:--  1267\n",
+      "100  4141  100  3928  100   213   2189    118  0:00:01  0:00:01 --:--:--  2308\n",
       "\u001b[1;39m{\n",
       "  \u001b[0m\u001b[34;1m\"meta\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
       "    \u001b[0m\u001b[34;1m\"name\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"KernelShap\"\u001b[0m\u001b[1;39m,\n",
@@ -1265,53 +1256,53 @@
       "    \u001b[0m\u001b[34;1m\"shap_values\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\n",
       "      \u001b[1;39m[\n",
       "        \u001b[1;39m[\n",
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       "      \u001b[1;39m[\n",
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       "      \u001b[1;39m]\u001b[0m\u001b[1;39m,\n",
       "      \u001b[1;39m[\n",
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       "        \u001b[1;39m]\u001b[0m\u001b[1;39m\n",
       "      \u001b[1;39m]\u001b[0m\u001b[1;39m\n",
       "    \u001b[1;39m]\u001b[0m\u001b[1;39m,\n",
@@ -1367,19 +1358,19 @@
       "      \u001b[0m\u001b[34;1m\"importances\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
       "        \u001b[0m\u001b[34;1m\"0\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
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       "          \u001b[1;39m]\u001b[0m\u001b[1;39m,\n",
       "          \u001b[0m\u001b[34;1m\"names\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\n",
       "            \u001b[0;32m\"od280/od315_of_diluted_wines\"\u001b[0m\u001b[1;39m,\n",
@@ -1399,19 +1390,19 @@
       "        \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
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       "          \u001b[0m\u001b[34;1m\"ranked_effect\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\n",
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       "          \u001b[1;39m]\u001b[0m\u001b[1;39m,\n",
       "          \u001b[0m\u001b[34;1m\"names\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\n",
       "            \u001b[0;32m\"color_intensity\"\u001b[0m\u001b[1;39m,\n",
@@ -1421,29 +1412,29 @@
       "            \u001b[0;32m\"flavanoids\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"magnesium\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"ash\"\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;32m\"proanthocyanins\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"malic_acid\"\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;32m\"proanthocyanins\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"total_phenols\"\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;32m\"alcalinity_of_ash\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"alcohol\"\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;32m\"nonflavanoid_phenols\"\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;32m\"alcalinity_of_ash\"\u001b[0m\u001b[1;39m\n",
+      "            \u001b[0;32m\"nonflavanoid_phenols\"\u001b[0m\u001b[1;39m\n",
       "          \u001b[1;39m]\u001b[0m\u001b[1;39m\n",
       "        \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
       "        \u001b[0m\u001b[34;1m\"2\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
       "          \u001b[0m\u001b[34;1m\"ranked_effect\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\n",
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+      "            \u001b[0;39m0.06871646495343742\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.05831112850130049\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.03706436976364458\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.03633892265257299\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.022874111141031328\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.021654431619118508\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.02028068848580955\u001b[0m\u001b[1;39m\n",
       "          \u001b[1;39m]\u001b[0m\u001b[1;39m,\n",
       "          \u001b[0m\u001b[34;1m\"names\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\n",
       "            \u001b[0;32m\"od280/od315_of_diluted_wines\"\u001b[0m\u001b[1;39m,\n",
@@ -1451,31 +1442,31 @@
       "            \u001b[0;32m\"color_intensity\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"flavanoids\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"total_phenols\"\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;32m\"alcalinity_of_ash\"\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;32m\"proanthocyanins\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"proline\"\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;32m\"proanthocyanins\"\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;32m\"alcalinity_of_ash\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"ash\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"magnesium\"\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;32m\"malic_acid\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"nonflavanoid_phenols\"\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;32m\"alcohol\"\u001b[0m\u001b[1;39m\n",
+      "            \u001b[0;32m\"alcohol\"\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;32m\"malic_acid\"\u001b[0m\u001b[1;39m\n",
       "          \u001b[1;39m]\u001b[0m\u001b[1;39m\n",
       "        \u001b[1;39m}\u001b[0m\u001b[1;39m,\n",
       "        \u001b[0m\u001b[34;1m\"aggregated\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\n",
       "          \u001b[0m\u001b[34;1m\"ranked_effect\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\n",
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-      "            \u001b[0;39m0.08935471257017247\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;39m0.08677681420605043\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;39m0.07145470402894616\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;39m0.04396065591922266\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;39m0.0393106286780619\u001b[0m\u001b[1;39m\n",
+      "            \u001b[0;39m0.6900361047157916\u001b[0m\u001b[1;39m,\n",
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+      "            \u001b[0;39m0.3519210458824253\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.28144464732553215\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.26657031888076055\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.13937011449909226\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.13868568906334378\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.09219908597601678\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.08486125313101994\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.07443907179138509\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.05637682849919051\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;39m0.04879490568364603\u001b[0m\u001b[1;39m\n",
       "          \u001b[1;39m]\u001b[0m\u001b[1;39m,\n",
       "          \u001b[0m\u001b[34;1m\"names\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\n",
       "            \u001b[0;32m\"od280/od315_of_diluted_wines\"\u001b[0m\u001b[1;39m,\n",
@@ -1484,13 +1475,13 @@
       "            \u001b[0;32m\"flavanoids\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"total_phenols\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"proline\"\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;32m\"alcalinity_of_ash\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"proanthocyanins\"\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;32m\"ash\"\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;32m\"alcalinity_of_ash\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"magnesium\"\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;32m\"ash\"\u001b[0m\u001b[1;39m,\n",
       "            \u001b[0;32m\"malic_acid\"\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;32m\"nonflavanoid_phenols\"\u001b[0m\u001b[1;39m,\n",
-      "            \u001b[0;32m\"alcohol\"\u001b[0m\u001b[1;39m\n",
+      "            \u001b[0;32m\"alcohol\"\u001b[0m\u001b[1;39m,\n",
+      "            \u001b[0;32m\"nonflavanoid_phenols\"\u001b[0m\u001b[1;39m\n",
       "          \u001b[1;39m]\u001b[0m\u001b[1;39m\n",
       "        \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
       "      \u001b[1;39m}\u001b[0m\u001b[1;39m\n",
@@ -1508,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 54,
    "metadata": {
     "scrolled": true
    },
@@ -1517,7 +1508,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io \"wine\" deleted\r\n"
+      "seldondeployment.machinelearning.seldon.io \"wine\" deleted\n"
      ]
     }
    ],
@@ -1538,14 +1529,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Overwriting resources/mnist_rest_explainer.yaml\n"
+      "Writing resources/mnist_rest_explainer.yaml\n"
      ]
     }
    ],
@@ -1579,14 +1570,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io/tfserving created\r\n"
+      "seldondeployment.machinelearning.seldon.io/tfserving created\n"
      ]
     }
    ],
@@ -1596,7 +1587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -1614,7 +1605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -1644,7 +1635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1654,7 +1645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1666,7 +1657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1678,7 +1669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1688,7 +1679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -1697,7 +1688,7 @@
        "array([7, 2, 1, 0, 4, 1, 4, 9, 6, 9])"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1708,7 +1699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1720,7 +1711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1732,7 +1723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1741,7 +1732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -1793,14 +1784,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io \"tfserving\" deleted\r\n"
+      "seldondeployment.machinelearning.seldon.io \"tfserving\" deleted\n"
      ]
     }
    ],
@@ -1821,7 +1812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -1856,14 +1847,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io/income configured\r\n"
+      "seldondeployment.machinelearning.seldon.io/income created\n"
      ]
     }
    ],
@@ -1873,14 +1864,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "deployment \"income-default-0-income-model\" successfully rolled out\r\n"
+      "Waiting for deployment \"income-default-0-income-model\" rollout to finish: 0 of 1 updated replicas are available...\n",
+      "deployment \"income-default-0-income-model\" successfully rolled out\n"
      ]
     }
    ],
@@ -1890,7 +1882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 75,
    "metadata": {
     "scrolled": true
    },
@@ -1910,7 +1902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 77,
    "metadata": {
     "scrolled": true
    },
@@ -1919,7 +1911,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'data': {'names': [], 'tensor': {'shape': [1], 'values': [-1.2381880283355713]}}, 'meta': {'requestPath': {'income-model': 'seldonio/xgboostserver:1.6.0'}}}\n"
+      "{'data': {'names': [], 'tensor': {'shape': [1], 'values': [-1.2381880283355713]}}, 'meta': {'requestPath': {'income-model': 'seldonio/xgboostserver:1.7.0-dev'}}}\n"
      ]
     }
    ],
@@ -1938,7 +1930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1949,7 +1941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": 79,
    "metadata": {
     "scrolled": true
    },
@@ -1958,7 +1950,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Elapsed time: 12.412888288497925\n"
+      "Elapsed time: 8.356285572052002\n"
      ]
     }
    ],
@@ -1976,7 +1968,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1986,7 +1978,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1996,7 +1988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2026,7 +2018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2035,7 +2027,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -2088,14 +2080,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "\n",
-       "<div id='iSTYOK6OQBQR2FQ3HXXR4'>\n",
+       "<div id='iR6DC6FZ49KKZQBPN7SB0'>\n",
        "<div style='color: #900; text-align: center;'>\n",
        "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
        "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
@@ -2105,16 +2097,16 @@
        "</div></div>\n",
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+       "    document.getElementById('iR6DC6FZ49KKZQBPN7SB0')\n",
        "  );\n",
        "</script>"
       ],
       "text/plain": [
-       "<shap.plots._force.AdditiveForceArrayVisualizer at 0x7f76e286df98>"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 232,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2130,14 +2122,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io \"income\" deleted\r\n"
+      "seldondeployment.machinelearning.seldon.io \"income\" deleted\n"
      ]
     }
    ],
@@ -2160,14 +2152,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 255,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Writing resources/income_gpu_explainer.yaml\n"
+      "Overwriting resources/income_gpu_explainer.yaml\n"
      ]
     }
    ],
@@ -2201,14 +2193,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 256,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io/incomegpu unchanged\r\n"
+      "seldondeployment.machinelearning.seldon.io/incomegpu unchanged\n"
      ]
     }
    ],
@@ -2218,14 +2210,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 257,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "deployment \"incomegpu-default-0-income-model\" successfully rolled out\r\n"
+      "deployment \"incomegpu-default-0-income-model\" successfully rolled out\n"
      ]
     }
    ],
@@ -2235,7 +2227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 258,
+   "execution_count": 107,
    "metadata": {
     "scrolled": true
    },
@@ -2255,7 +2247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 246,
+   "execution_count": 110,
    "metadata": {
     "scrolled": true
    },
@@ -2264,7 +2256,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'data': {'names': [], 'tensor': {'shape': [1], 'values': [-1.2381880283355713]}}, 'meta': {'requestPath': {'income-model': 'seldonio/xgboostserver:1.6.0'}}}\n"
+      "None\n"
      ]
     }
    ],
@@ -2283,7 +2275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 247,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2294,14 +2286,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 248,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Elapsed time: 0.7827632427215576\n"
+      "Elapsed time: 0.004498720169067383\n"
      ]
     }
    ],
@@ -2310,9 +2302,7 @@
     "start = time.time()\n",
     "res = sc.explain(deployment_name=\"incomegpu\", predictor=\"default\", data=data[0:2000])\n",
     "end = time.time()\n",
-    "print(\"Elapsed time:\",end-start)\n",
-    "explanation = res.response\n",
-    "explanationStr = json.dumps(explanation)"
+    "print(\"Elapsed time:\",end-start)"
    ]
   },
   {
@@ -2324,27 +2314,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 249,
+   "execution_count": 141,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Explanation not successfull: are you running on GPU enabled cluster?\n"
+     ]
+    }
+   ],
    "source": [
     "from alibi.api.interfaces import Explanation\n",
-    "explanation = Explanation.from_json(explanationStr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "explanation.shap_values = np.array(explanation.shap_values)\n",
-    "explanation.raw[\"instances\"] = np.array(explanation.raw[\"instances\"])"
+    "\n",
+    "if res.success:\n",
+    "    explanation = res.response\n",
+    "    explanationStr = json.dumps(explanation)\n",
+    "    explanation = Explanation.from_json(explanationStr)\n",
+    "    \n",
+    "    explanation.shap_values = np.array(explanation.shap_values)\n",
+    "    explanation.raw[\"instances\"] = np.array(explanation.raw[\"instances\"])\n",
+    "else:\n",
+    "    explanation = None\n",
+    "    print(\"Explanation not successfull: are you running on GPU enabled cluster?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 251,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2374,16 +2372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 252,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "decoded_features = decode_data(data,explanation.feature_names,explanation.categorical_names)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [
     {
@@ -2436,69 +2425,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 254,
+   "execution_count": 146,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<div id='iZEZAV3JMTHE8NZ08JHQ6'>\n",
-       "<div style='color: #900; text-align: center;'>\n",
-       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
-       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
-       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
-       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
-       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
-       "</div></div>\n",
-       " <script>\n",
-       "   if (window.SHAP) SHAP.ReactDom.render(\n",
-       "    SHAP.React.createElement(SHAP.AdditiveForceArrayVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": -1.3114768034812518, \"link\": \"identity\", \"featureNames\": [\"Age\", \"Workclass\", \"Education\", \"Marital Status\", \"Occupation\", \"Relationship\", \"Race\", \"Sex\", \"Capital Gain\", \"Capital Loss\", \"Hours per week\", \"Country\"], \"explanations\": [{\"outValue\": -6.817621128730149, \"simIndex\": 1982.0, \"features\": {\"0\": {\"effect\": 0.8588343262672424, \"value\": 39}, \"1\": {\"effect\": -0.6025629043579102, \"value\": \"State-gov\"}, \"2\": {\"effect\": 0.4671325981616974, \"value\": \"Bachelors\"}, \"3\": {\"effect\": -1.0621572732925415, \"value\": \"Never-Married\"}, \"4\": {\"effect\": -0.25385570526123047, \"value\": \"Admin\"}, \"5\": {\"effect\": 0.006648284383118153, \"value\": \"Not-in-family\"}, \"6\": {\"effect\": 0.034072596579790115, \"value\": \"White\"}, \"7\": {\"effect\": 0.2868843376636505, \"value\": \"Male\"}, \"8\": {\"effect\": 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0.23517142236232758, \"value\": \"Male\"}, \"8\": {\"effect\": -0.6772942543029785, \"value\": 0}, \"9\": {\"effect\": -0.04433346167206764, \"value\": 0}, \"10\": {\"effect\": 0.7876796126365662, \"value\": 48}, \"11\": {\"effect\": 0.016884146258234978, \"value\": \"United-States\"}}}, {\"outValue\": 1.5871473782661847, \"simIndex\": 1602.0, \"features\": {\"0\": {\"effect\": 1.5406193733215332, \"value\": 54}, \"1\": {\"effect\": 0.283486545085907, \"value\": \"Self-emp-inc\"}, \"2\": {\"effect\": -0.3091975152492523, \"value\": \"High School grad\"}, \"3\": {\"effect\": 1.0667529106140137, \"value\": \"Married\"}, \"4\": {\"effect\": 0.20669624209403992, \"value\": \"Sales\"}, \"5\": {\"effect\": -0.009017696604132652, \"value\": \"Husband\"}, \"6\": {\"effect\": 0.016062920913100243, \"value\": \"White\"}, \"7\": {\"effect\": 0.2161381095647812, \"value\": \"Male\"}, \"8\": {\"effect\": -0.6078623533248901, \"value\": 0}, \"9\": {\"effect\": -0.018124660477042198, \"value\": 0}, \"10\": {\"effect\": 0.48461541533470154, \"value\": 45}, \"11\": {\"effect\": 0.028454890474677086, \"value\": \"United-States\"}}}, {\"outValue\": -2.1220227993127416, \"simIndex\": 499.0, \"features\": {\"0\": {\"effect\": -0.41733255982398987, \"value\": 28}, \"1\": {\"effect\": 0.3581458330154419, \"value\": \"?\"}, \"2\": {\"effect\": 0.4603385925292969, \"value\": \"Associates\"}, \"3\": {\"effect\": 1.6249290704727173, \"value\": \"Married\"}, \"4\": {\"effect\": -0.7111621499061584, \"value\": \"?\"}, \"5\": {\"effect\": 1.820984959602356, \"value\": \"Wife\"}, \"6\": {\"effect\": 0.08832985907793045, \"value\": \"White\"}, \"7\": {\"effect\": -0.2856278717517853, \"value\": \"Female\"}, \"8\": {\"effect\": -0.6438471674919128, \"value\": 0}, \"9\": {\"effect\": -0.020227737724781036, \"value\": 0}, \"10\": {\"effect\": -3.1451077461242676, \"value\": 3}, \"11\": {\"effect\": 0.060030922293663025, \"value\": \"United-States\"}}}, {\"outValue\": 0.4365494958091072, \"simIndex\": 1226.0, \"features\": {\"0\": {\"effect\": 0.6411802172660828, \"value\": 34}, \"1\": {\"effect\": 0.01721464842557907, \"value\": \"Private\"}, \"2\": {\"effect\": -0.2500118315219879, \"value\": \"High School grad\"}, \"3\": {\"effect\": 1.100028157234192, \"value\": \"Married\"}, \"4\": {\"effect\": -0.2941148579120636, \"value\": \"Blue-Collar\"}, \"5\": {\"effect\": 0.04320095106959343, \"value\": \"Husband\"}, \"6\": {\"effect\": 0.02782387286424637, \"value\": \"White\"}, \"7\": {\"effect\": 0.2098030298948288, \"value\": \"Male\"}, \"8\": {\"effect\": -0.6179112792015076, \"value\": 0}, \"9\": {\"effect\": 0.00858739297837019, \"value\": 0}, \"10\": {\"effect\": 0.8376113772392273, \"value\": 50}, \"11\": {\"effect\": 0.024614620953798294, \"value\": \"United-States\"}}}, {\"outValue\": -3.8993714760091613, \"simIndex\": 917.0, \"features\": {\"0\": {\"effect\": 0.1733766347169876, \"value\": 30}, \"1\": {\"effect\": -0.03467175364494324, \"value\": \"Private\"}, \"2\": {\"effect\": -0.38397547602653503, \"value\": \"High School grad\"}, \"3\": {\"effect\": -1.1408286094665527, \"value\": \"Never-Married\"}, \"4\": {\"effect\": 0.5847278833389282, \"value\": \"Sales\"}, \"5\": {\"effect\": 0.10139989107847214, \"value\": \"Not-in-family\"}, \"6\": {\"effect\": -0.5122212767601013, \"value\": \"Black\"}, \"7\": {\"effect\": -0.3607145845890045, \"value\": \"Female\"}, \"8\": {\"effect\": -0.6780633926391602, \"value\": 0}, \"9\": {\"effect\": -0.03951329365372658, \"value\": 0}, \"10\": {\"effect\": -0.3282739520072937, \"value\": 35}, \"11\": {\"effect\": 0.030863257125020027, \"value\": \"United-States\"}}}, {\"outValue\": -4.745443474597902, \"simIndex\": 164.0, \"features\": {\"0\": {\"effect\": -3.1670825481414795, \"value\": 19}, \"1\": {\"effect\": -0.01161868218332529, \"value\": \"Private\"}, \"2\": {\"effect\": -0.18665316700935364, \"value\": \"High School grad\"}, \"3\": {\"effect\": -0.9909377098083496, \"value\": \"Never-Married\"}, \"4\": {\"effect\": 0.3616717457771301, \"value\": \"Service\"}, \"5\": {\"effect\": 0.023373957723379135, \"value\": \"Not-in-family\"}, \"6\": {\"effect\": 0.06625711917877197, \"value\": \"White\"}, \"7\": {\"effect\": -0.48207807540893555, \"value\": \"Female\"}, \"8\": {\"effect\": -0.6860374212265015, \"value\": 0}, \"9\": {\"effect\": -0.08919309824705124, \"value\": 0}, \"10\": {\"effect\": 0.7345558404922485, \"value\": 45}, \"11\": {\"effect\": 0.9937753677368164, \"value\": \"?\"}}}, {\"outValue\": -3.4674665062722276, \"simIndex\": 421.0, \"features\": {\"0\": {\"effect\": -0.45160341262817383, \"value\": 26}, \"1\": {\"effect\": 0.05763600394129753, \"value\": \"Private\"}, \"2\": {\"effect\": -0.27882617712020874, \"value\": \"High School grad\"}, \"3\": {\"effect\": -1.0641330480575562, \"value\": \"Never-Married\"}, \"4\": {\"effect\": -0.1598862260580063, \"value\": \"Blue-Collar\"}, \"5\": {\"effect\": 0.11246982961893082, \"value\": \"Not-in-family\"}, \"6\": {\"effect\": 0.0398758128285408, \"value\": \"White\"}, \"7\": {\"effect\": 0.23947665095329285, \"value\": \"Male\"}, \"8\": {\"effect\": -0.6906842589378357, \"value\": 0}, \"9\": {\"effect\": -0.06035042926669121, \"value\": 0}, \"10\": {\"effect\": 0.08169082552194595, \"value\": 40}, \"11\": {\"effect\": 0.018344726413488388, \"value\": \"United-States\"}}}, {\"outValue\": -0.3487377661314317, \"simIndex\": 893.0, \"features\": {\"0\": {\"effect\": 1.2697372436523438, \"value\": 47}, \"1\": {\"effect\": 0.08165644854307175, \"value\": \"Private\"}, \"2\": {\"effect\": -0.2868095934391022, \"value\": \"High School grad\"}, \"3\": {\"effect\": -0.8756341934204102, \"value\": \"Never-Married\"}, \"4\": {\"effect\": 0.36567339301109314, \"value\": \"Sales\"}, \"5\": {\"effect\": 0.09358382970094681, \"value\": \"Not-in-family\"}, \"6\": {\"effect\": 0.01862419955432415, \"value\": \"White\"}, \"7\": {\"effect\": 0.23541788756847382, \"value\": \"Male\"}, \"8\": {\"effect\": -0.5882949829101562, \"value\": 0}, \"9\": {\"effect\": -0.04973408579826355, \"value\": 0}, \"10\": {\"effect\": 0.6580988168716431, \"value\": 70}, \"11\": {\"effect\": 0.04042007401585579, \"value\": \"United-States\"}}}, {\"outValue\": -0.7711843899252349, \"simIndex\": 966.0, \"features\": {\"0\": {\"effect\": 1.4220423698425293, \"value\": 50}, \"1\": {\"effect\": 0.01803247071802616, \"value\": \"Private\"}, \"2\": {\"effect\": -0.35709595680236816, \"value\": \"High School grad\"}, \"3\": {\"effect\": -0.8906754851341248, \"value\": \"Separated\"}, \"4\": {\"effect\": 0.8549636602401733, \"value\": \"White-Collar\"}, \"5\": {\"effect\": 0.0012713741743937135, \"value\": \"Unmarried\"}, \"6\": {\"effect\": 0.03183303773403168, \"value\": \"White\"}, \"7\": {\"effect\": 0.2649692893028259, \"value\": \"Male\"}, \"8\": {\"effect\": -0.6067368984222412, \"value\": 0}, \"9\": {\"effect\": -0.07962940633296967, \"value\": 0}, \"10\": {\"effect\": -0.17876747250556946, \"value\": 38}, \"11\": {\"effect\": 0.06008543074131012, \"value\": \"United-States\"}}}, {\"outValue\": -0.6867906144854137, \"simIndex\": 779.0, \"features\": {\"0\": {\"effect\": 0.36114370822906494, \"value\": 30}, \"1\": {\"effect\": -0.002917710691690445, \"value\": \"Private\"}, \"2\": {\"effect\": 0.813410222530365, \"value\": \"Bachelors\"}, \"3\": {\"effect\": -1.0464247465133667, \"value\": \"Never-Married\"}, \"4\": {\"effect\": 1.0187385082244873, \"value\": \"White-Collar\"}, \"5\": {\"effect\": -0.19962964951992035, \"value\": \"Unmarried\"}, \"6\": {\"effect\": 0.036761071532964706, \"value\": \"White\"}, \"7\": {\"effect\": -0.4436190128326416, \"value\": \"Female\"}, \"8\": {\"effect\": -0.6713176965713501, \"value\": 0}, \"9\": {\"effect\": -0.08544033765792847, \"value\": 0}, \"10\": {\"effect\": 0.7933281064033508, \"value\": 45}, \"11\": {\"effect\": 0.05065372586250305, \"value\": \"United-States\"}}}, {\"outValue\": -0.6329877628978797, \"simIndex\": 877.0, \"features\": {\"0\": {\"effect\": 1.0467451810836792, \"value\": 44}, \"1\": {\"effect\": 0.016698498278856277, \"value\": \"Private\"}, \"2\": {\"effect\": -0.2934010326862335, \"value\": \"High School grad\"}, \"3\": {\"effect\": -0.7074475288391113, \"value\": \"Separated\"}, \"4\": {\"effect\": -0.27598080039024353, \"value\": \"Blue-Collar\"}, \"5\": {\"effect\": 0.13358084857463837, \"value\": \"Not-in-family\"}, \"6\": {\"effect\": 0.03379252180457115, \"value\": \"White\"}, \"7\": {\"effect\": 0.2940659523010254, \"value\": \"Male\"}, \"8\": {\"effect\": -0.5790951251983643, \"value\": 0}, \"9\": {\"effect\": -0.06589973717927933, \"value\": 0}, \"10\": {\"effect\": 1.0525968074798584, \"value\": 50}, \"11\": {\"effect\": 0.022833455353975296, \"value\": \"United-States\"}}}, {\"outValue\": 1.1725132259506277, \"simIndex\": 1590.0, \"features\": {\"0\": {\"effect\": 1.1029430627822876, \"value\": 49}, \"1\": {\"effect\": 0.024334615096449852, \"value\": \"Private\"}, \"2\": {\"effect\": -0.2641621232032776, \"value\": \"High School grad\"}, \"3\": {\"effect\": 1.0741348266601562, \"value\": \"Married\"}, \"4\": {\"effect\": 0.23703451454639435, \"value\": \"Sales\"}, \"5\": {\"effect\": 0.04501748085021973, \"value\": \"Husband\"}, \"6\": {\"effect\": 0.007446437142789364, \"value\": \"White\"}, \"7\": {\"effect\": 0.21135739982128143, \"value\": \"Male\"}, \"8\": {\"effect\": -0.5685817003250122, \"value\": 0}, \"9\": {\"effect\": -0.009594932198524475, \"value\": 0}, \"10\": {\"effect\": 0.5832176208496094, \"value\": 60}, \"11\": {\"effect\": 0.040842827409505844, \"value\": \"United-States\"}}}, {\"outValue\": -1.3387710499611207, \"simIndex\": 775.0, \"features\": {\"0\": {\"effect\": -0.41214051842689514, \"value\": 75}, \"1\": {\"effect\": -0.3834167718887329, \"value\": \"Self-emp-not-inc\"}, \"2\": {\"effect\": 0.6519967317581177, \"value\": \"Masters\"}, \"3\": {\"effect\": -0.5359464287757874, \"value\": \"Separated\"}, \"4\": {\"effect\": 1.0085312128067017, \"value\": \"Professional\"}, \"5\": {\"effect\": 0.10048484802246094, \"value\": \"Not-in-family\"}, \"6\": {\"effect\": -0.07954546064138412, \"value\": \"White\"}, \"7\": {\"effect\": -0.6684372425079346, \"value\": \"Female\"}, \"8\": {\"effect\": -0.5139684677124023, \"value\": 0}, \"9\": {\"effect\": -0.04497610405087471, \"value\": 0}, \"10\": {\"effect\": 0.8247742056846619, \"value\": 50}, \"11\": {\"effect\": 0.025349749252200127, \"value\": \"United-States\"}}}, {\"outValue\": 2.4570359409752776, \"simIndex\": 1382.0, \"features\": {\"0\": {\"effect\": 1.0946673154830933, \"value\": 37}, \"1\": {\"effect\": 0.05878974124789238, \"value\": \"Private\"}, \"2\": {\"effect\": 0.7536140084266663, \"value\": \"Bachelors\"}, \"3\": {\"effect\": 1.1346228122711182, \"value\": \"Married\"}, \"4\": {\"effect\": 0.38331732153892517, \"value\": \"Sales\"}, \"5\": {\"effect\": 0.036191556602716446, \"value\": \"Husband\"}, \"6\": {\"effect\": 0.006409682333469391, \"value\": \"White\"}, \"7\": {\"effect\": 0.18153074383735657, \"value\": \"Male\"}, \"8\": {\"effect\": -0.5932030081748962, \"value\": 0}, \"9\": {\"effect\": -0.038061488419771194, \"value\": 0}, \"10\": {\"effect\": 0.7122400999069214, \"value\": 55}, \"11\": {\"effect\": 0.038393959403038025, \"value\": \"United-States\"}}}, {\"outValue\": -1.491172094925911, \"simIndex\": 658.0, \"features\": {\"0\": {\"effect\": 1.4917634725570679, \"value\": 51}, \"1\": {\"effect\": 0.03308901935815811, \"value\": \"State-gov\"}, \"2\": {\"effect\": -0.27455154061317444, \"value\": \"High School grad\"}, \"3\": {\"effect\": -0.8855282068252563, \"value\": \"Separated\"}, \"4\": {\"effect\": -0.20961928367614746, \"value\": \"Blue-Collar\"}, \"5\": {\"effect\": 0.1870083063840866, \"value\": \"Unmarried\"}, \"6\": {\"effect\": 0.020359361544251442, \"value\": \"White\"}, \"7\": {\"effect\": 0.3546438217163086, \"value\": \"Male\"}, \"8\": {\"effect\": -0.5953712463378906, \"value\": 0}, \"9\": {\"effect\": -0.08060996234416962, \"value\": 0}, \"10\": {\"effect\": -0.27125224471092224, \"value\": 38}, \"11\": {\"effect\": 0.05037321150302887, \"value\": \"United-States\"}}}], \"plot_cmap\": \"RdBu\", \"ordering_keys\": null, \"ordering_keys_time_format\": null}),\n",
-       "    document.getElementById('iZEZAV3JMTHE8NZ08JHQ6')\n",
-       "  );\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "<shap.plots._force.AdditiveForceArrayVisualizer at 0x7f76e29d8f60>"
-      ]
-     },
-     "execution_count": 254,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "shap.force_plot(\n",
-    "    explanation.expected_value[0], # 0 is a class index but we have single-output model\n",
-    "    explanation.shap_values[0] , \n",
-    "    decoded_features,  \n",
-    "    explanation.feature_names,\n",
-    ")"
+    "if explanation is not None:\n",
+    "    decoded_features = decode_data(data, explanation.feature_names, explanation.categorical_names)\n",
+    "    shap.force_plot(\n",
+    "        explanation.expected_value[0], # 0 is a class index but we have single-output model\n",
+    "        explanation.shap_values[0] , \n",
+    "        decoded_features,  \n",
+    "        explanation.feature_names,\n",
+    "    )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "seldondeployment.machinelearning.seldon.io \"incomegpu\" deleted\r\n"
+      "Error from server (NotFound): error when deleting \"resources/income_gpu_explainer.yaml\": seldondeployments.machinelearning.seldon.io \"incomegpu\" not found\n"
      ]
     }
    ],
    "source": [
     "!kubectl delete -f resources/income_gpu_explainer.yaml"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -2518,7 +2474,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.9"
   },
   "varInspector": {
    "cols": {
diff --git a/testing/scripts/kind_test_all.sh b/testing/scripts/kind_test_all.sh
index 9ecbf94a75..5da7782ce1 100755
--- a/testing/scripts/kind_test_all.sh
+++ b/testing/scripts/kind_test_all.sh
@@ -42,8 +42,8 @@ if [[ ${KIND_EXIT_VALUE} -eq 0 ]]; then
 
     run_end_to_end_tests() {
 
-        echo "Files changed in python folder:"
-        git --no-pager diff --exit-code --name-only origin/master ../../python
+        echo "Files changed in python or wrapper folder:"
+        git --no-pager diff --exit-code --name-only origin/master ../../python ../../wrappers/s2i/python
         PYTHON_MODIFIED=$?
         if [[ $PYTHON_MODIFIED -gt 0 ]]; then
             make s2i_build_base_images
@@ -111,8 +111,8 @@ if [[ ${KIND_EXIT_VALUE} -eq 0 ]]; then
             return 1
         fi
 
-        echo "Files changed in prepackaged folder:"
-        git --no-pager diff --exit-code --name-only origin/master ../../servers ../../integrations
+        echo "Files changed in prepackaged, python, or wrapper folder:"
+        git --no-pager diff --exit-code --name-only origin/master ../../servers ../../integrations ../../python ../../wrappers/s2i/python
         PREPACKAGED_MODIFIED=$?
         if [[ $PREPACKAGED_MODIFIED -gt 0 ]]; then
             make kind_build_prepackaged
diff --git a/wrappers/s2i/python/Dockerfile.local b/wrappers/s2i/python/Dockerfile.local
index d2bc429ef7..7d70d611fb 100644
--- a/wrappers/s2i/python/Dockerfile.local
+++ b/wrappers/s2i/python/Dockerfile.local
@@ -9,6 +9,9 @@ ARG PYTHON_VERSION
 RUN conda install --yes python=$PYTHON_VERSION conda=$CONDA_VERSION
 RUN apt-get update --yes && apt-get install --yes gcc make build-essential
 
+# Pin pip and setuptools
+RUN pip install pip==20.2 setuptools==46.1
+
 RUN mkdir microservice
 WORKDIR /microservice
 
diff --git a/wrappers/s2i/python/Dockerfile.redhat b/wrappers/s2i/python/Dockerfile.redhat
index 4e24d33d2f..3fc62156e3 100644
--- a/wrappers/s2i/python/Dockerfile.redhat
+++ b/wrappers/s2i/python/Dockerfile.redhat
@@ -9,8 +9,8 @@ ARG PYTHON_VERSION
 RUN conda install --yes python=$PYTHON_VERSION conda=$CONDA_VERSION
 RUN dnf install -y make automake gcc gcc-c++
 
-# Upgrade pip version
-RUN pip install pip==20.2
+# Pin pip and setuptools
+RUN pip install pip==20.2 setuptools==46.1
 
 RUN mkdir microservice
 WORKDIR /microservice