notebooks/server.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nuclio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%nuclio: setting kind to 'nuclio'\n"
     ]
    }
   ],
   "source": [
    "%nuclio config kind = \"nuclio\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%nuclio: setting 'FEATURES_TABLE' environment variable\n",
      "%nuclio: setting 'PREDICTIONS_TABLE' environment variable\n",
      "%nuclio: setting 'prediction_col' environment variable\n",
      "%nuclio: setting '# model_path' environment variable\n",
      "%nuclio: setting 'model_path' environment variable\n",
      "%nuclio: setting 'model_name' environment variable\n",
      "%nuclio: setting 'model_class' environment variable\n",
      "%nuclio: setting 'model_col' environment variable\n",
      "%nuclio: setting 'model_class_col' environment variable\n",
      "%nuclio: setting 'worker_col' environment variable\n",
      "%nuclio: setting 'hostname_col' environment variable\n",
      "%nuclio: setting 'timestamp_col' environment variable\n",
      "%nuclio: setting 'orig_timestamp_col' environment variable\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "%nuclio: cannot find \"=\" in line\n"
     ]
    }
   ],
   "source": [
    "%%nuclio env\n",
    "\n",
    "FEATURES_TABLE = /User/demo-network-operations/data/ \n",
    "#streaming/features\n",
    "PREDICTIONS_TABLE = /User/demo-network-operations/streaming/predictions\n",
    "prediction_col = predictions\n",
    "# model_path = /User/demo-network-operations/artifacts/model/2/model.pkl\n",
    "model_path = store://network-operations/train_model#f2ea18dd-504d-4d14-83d2-a326fe5afc37\n",
    "model_name = netops_predictor_v1\n",
    "model_class = MLRunModel\n",
    "model_col = model\n",
    "model_class_col = class\n",
    "worker_col = worker\n",
    "hostname_col = hostname\n",
    "timestamp_col = when\n",
    "orig_timestamp_col = timestamp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nuclio: start-code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import cloudpickle\n",
    "import numpy as np\n",
    "import json\n",
    "from mlrun import get_or_create_ctx\n",
    "import socket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data_parquet(context):\n",
    "    mpath = [os.path.join(context.features_table, file) for file in os.listdir(context.features_table) if file.endswith(('parquet', 'pq'))]\n",
    "    files_by_updated = sorted(mpath, key=os.path.getmtime, reverse=True)\n",
    "    return pd.read_parquet(files_by_updated[:1][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_to_parquet(context, df: pd.DataFrame):\n",
    "    print('Saving features to Parquet')\n",
    "    \n",
    "    # Need to fix timestamps from ns to ms if we write to parquet \n",
    "    # And add this model name to indexes\n",
    "    keys = list([name if name != context.orig_timestamp_col else context.timestamp_col for name in df.index.names]) + [context.model_col, context.model_class_col, context.worker_col, context.hostname_col]\n",
    "    df = df.reset_index()\n",
    "    df[context.timestamp_col] = df.pop(context.orig_timestamp_col).astype('datetime64[ms]')\n",
    "    \n",
    "    # Fix indexes\n",
    "    df = df.set_index(keys)\n",
    "    \n",
    "    # Save parquet\n",
    "    first_timestamp = df.index[0][0].strftime('%Y%m%dT%H%M%S')\n",
    "    last_timestamp = df.index[-1][0].strftime('%Y%m%dT%H%M%S')\n",
    "    filename = first_timestamp + '-' + last_timestamp + '.parquet'\n",
    "    filepath = os.path.join(context.predictions_table, filename)\n",
    "    with open(filepath, 'wb+') as f:\n",
    "        df.to_parquet(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_context(context):\n",
    "    # Set vars from env\n",
    "    setattr(context, 'model_name', os.getenv('model_name', 'netops_model'))\n",
    "    setattr(context, 'model_col', os.getenv('model_col', 'model'))\n",
    "    setattr(context, 'model_class_col', os.getenv('model_class_col', 'class'))\n",
    "    setattr(context, 'worker_col', os.getenv('worker_col', 'worker'))\n",
    "    setattr(context, 'hostname_col', os.getenv('hostname_col', 'hostname'))\n",
    "    setattr(context, 'timestamp_col', os.getenv('timestamp_col', 'when'))\n",
    "    setattr(context, 'orig_timestamp_col', os.getenv('orig_timestamp_col', 'timestamp'))\n",
    "    setattr(context, 'features_table', os.getenv('FEATURES_TABLE', 'netops_features'))\n",
    "    setattr(context, 'predictions_table', os.getenv('PREDICTIONS_TABLE', 'netops_predictions'))\n",
    "    setattr(context, 'prediction_col', os.getenv('prediction_col', 'prediction'))\n",
    "    \n",
    "    # Load model\n",
    "    model_path = os.environ['model_path']\n",
    "    if model_path.startswith('store://'):\n",
    "        mlctx = get_or_create_ctx('inference')\n",
    "        model = mlctx.get_dataitem(model_path)\n",
    "        model_path = os.path.join(model.url, 'model.pkl')\n",
    "    with open(model_path, 'rb') as f:\n",
    "        model = cloudpickle.load(f)\n",
    "    setattr(context, 'model', model)\n",
    "    setattr(context, 'model_class', type(model).__name__)\n",
    "    \n",
    "     # Create saving directory if needed\n",
    "    filepath = os.path.join(context.predictions_table)\n",
    "    if not os.path.exists(filepath):\n",
    "        os.makedirs(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def handler(context, event):\n",
    "    \n",
    "    if getattr(event.trigger, 'kind', 'cron') == 'cron':\n",
    "        # Get latest parquets\n",
    "        df = pd.read_parquet('/User/demo-network-operations/artifacts/selected_features.parquet').iloc[:, :-1] #get_data_parquet(context)\n",
    "\n",
    "        # Predict\n",
    "        df[context.prediction_col] = context.model.predict(df.values)\n",
    "        \n",
    "        # Add server metadata\n",
    "        df[context.model_col] = context.model_name\n",
    "        df[context.model_class_col] = context.model_class\n",
    "        df[context.worker_col] = context.worker_id\n",
    "        df[context.hostname_col] = socket.gethostname()\n",
    "\n",
    "        # Save\n",
    "        save_to_parquet(context, df)\n",
    "    else:\n",
    "        body = json.loads(event.body)\n",
    "        feats = np.asarray(body['instances'])\n",
    "        result: np.ndarray = context.model.predict(feats)\n",
    "        return result.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nuclio: end-code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Local test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2020-08-10 10:50:29,470 [info] logging run results to: http://mlrun-api:8080\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/conda/lib/python3.6/site-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator DecisionTreeClassifier from version 0.23.1 when using version 0.22.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n",
      "/conda/lib/python3.6/site-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator RandomForestClassifier from version 0.23.1 when using version 0.22.1. This might lead to breaking code or invalid results. Use at your own risk.\n",
      "  UserWarning)\n"
     ]
    }
   ],
   "source": [
    "init_context(context)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving features to Parquet\n"
     ]
    }
   ],
   "source": [
    "event = nuclio.Event(body='', trigger={'kind': 'cron'})\n",
    "out = handler(context, event)\n",
    "out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlrun import code_to_function, mount_v3io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mlrun.runtimes.function.RemoteRuntime at 0x7f79ef4e4080>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn = code_to_function('inference-server',\n",
    "                      kind='nuclio',\n",
    "                      project='network-operations')\n",
    "fn.spec.base_spec['spec']['build']['baseImage'] = 'mlrun/ml-models'\n",
    "fn.apply(mount_v3io())\n",
    "fn.add_trigger('cron', nuclio.triggers.CronTrigger(interval='1m'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2020-08-09 13:09:30,711 [info] function spec saved to path: ../src/inference-server.yaml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.runtimes.function.RemoteRuntime at 0x7f79ef4e4080>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn.save()\n",
    "fn.export('../src/inference-server.yaml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2020-08-09 13:09:30,717 [info] deploy started\n",
      "[nuclio] 2020-08-09 13:09:37,916 (info) Build complete\n",
      "[nuclio] 2020-08-09 13:09:47,072 done updating network-operations-inference-server, function address: 192.168.224.209:32279\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'http://192.168.224.209:32279'"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn.deploy(project='network-operations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>predictions</th>\n",
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       "    <tr>\n",
       "      <th>when</th>\n",
       "      <th>company</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Natasha_Harbors</th>\n",
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      ],
      "text/plain": [
       "                                                                                                                                                               cpu_utilization  \\\n",
       "when                    company                     data_center      device        model               class                  worker hostname                                    \n",
       "2020-06-30 06:42:17.454 Wilson_LLC                  Zachary_Drives   6001003522699 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        66.939139   \n",
       "                                                    Obrien_Mountain  0966571261270 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        72.492707   \n",
       "                                                                     8069812479542 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        69.116878   \n",
       "                        Bennett__Delacruz_and_Walls Natasha_Harbors  5863502247054 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        64.944107   \n",
       "                                                                     4285071567351 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        78.641128   \n",
       "...                                                                                                                                                                        ...   \n",
       "2020-06-30 07:42:12.454 Wilson_LLC                  Obrien_Mountain  8069812479542 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        59.574487   \n",
       "                        Bennett__Delacruz_and_Walls Natasha_Harbors  5863502247054 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd       100.000000   \n",
       "                                                                     4285071567351 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd       100.000000   \n",
       "                                                    Dominique_Branch 4579248894449 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        69.053014   \n",
       "                                                                     7731097392608 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        69.629988   \n",
       "\n",
       "                                                                                                                                                                  latency  \\\n",
       "when                    company                     data_center      device        model               class                  worker hostname                               \n",
       "2020-06-30 06:42:17.454 Wilson_LLC                  Zachary_Drives   6001003522699 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd    0.537279   \n",
       "                                                    Obrien_Mountain  0966571261270 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd    0.000000   \n",
       "                                                                     8069812479542 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd    2.606934   \n",
       "                        Bennett__Delacruz_and_Walls Natasha_Harbors  5863502247054 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd    1.571046   \n",
       "                                                                     4285071567351 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd    0.000000   \n",
       "...                                                                                                                                                                   ...   \n",
       "2020-06-30 07:42:12.454 Wilson_LLC                  Obrien_Mountain  8069812479542 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd    0.000000   \n",
       "                        Bennett__Delacruz_and_Walls Natasha_Harbors  5863502247054 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd  100.000000   \n",
       "                                                                     4285071567351 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd  100.000000   \n",
       "                                                    Dominique_Branch 4579248894449 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd    0.064657   \n",
       "                                                                     7731097392608 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd    0.000000   \n",
       "\n",
       "                                                                                                                                                               packet_loss  \\\n",
       "when                    company                     data_center      device        model               class                  worker hostname                                \n",
       "2020-06-30 06:42:17.454 Wilson_LLC                  Zachary_Drives   6001003522699 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd     0.000000   \n",
       "                                                    Obrien_Mountain  0966571261270 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd     4.961308   \n",
       "                                                                     8069812479542 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd     0.000000   \n",
       "                        Bennett__Delacruz_and_Walls Natasha_Harbors  5863502247054 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd     0.172451   \n",
       "                                                                     4285071567351 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd     0.000000   \n",
       "...                                                                                                                                                                    ...   \n",
       "2020-06-30 07:42:12.454 Wilson_LLC                  Obrien_Mountain  8069812479542 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd     0.000000   \n",
       "                        Bennett__Delacruz_and_Walls Natasha_Harbors  5863502247054 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd    50.000000   \n",
       "                                                                     4285071567351 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd    50.000000   \n",
       "                                                    Dominique_Branch 4579248894449 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd     0.000000   \n",
       "                                                                     7731097392608 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd     1.153888   \n",
       "\n",
       "                                                                                                                                                               throughput  \\\n",
       "when                    company                     data_center      device        model               class                  worker hostname                               \n",
       "2020-06-30 06:42:17.454 Wilson_LLC                  Zachary_Drives   6001003522699 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd  256.482190   \n",
       "                                                    Obrien_Mountain  0966571261270 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd  264.122648   \n",
       "                                                                     8069812479542 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd  263.528599   \n",
       "                        Bennett__Delacruz_and_Walls Natasha_Harbors  5863502247054 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd  241.149554   \n",
       "                                                                     4285071567351 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd  263.688823   \n",
       "...                                                                                                                                                                   ...   \n",
       "2020-06-30 07:42:12.454 Wilson_LLC                  Obrien_Mountain  8069812479542 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd  269.816306   \n",
       "                        Bennett__Delacruz_and_Walls Natasha_Harbors  5863502247054 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd    0.000000   \n",
       "                                                                     4285071567351 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd    0.000000   \n",
       "                                                    Dominique_Branch 4579248894449 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd  255.943689   \n",
       "                                                                     7731097392608 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd  238.072156   \n",
       "\n",
       "                                                                                                                                                               predictions  \n",
       "when                    company                     data_center      device        model               class                  worker hostname                               \n",
       "2020-06-30 06:42:17.454 Wilson_LLC                  Zachary_Drives   6001003522699 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        False  \n",
       "                                                    Obrien_Mountain  0966571261270 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        False  \n",
       "                                                                     8069812479542 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        False  \n",
       "                        Bennett__Delacruz_and_Walls Natasha_Harbors  5863502247054 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        False  \n",
       "                                                                     4285071567351 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        False  \n",
       "...                                                                                                                                                                    ...  \n",
       "2020-06-30 07:42:12.454 Wilson_LLC                  Obrien_Mountain  8069812479542 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        False  \n",
       "                        Bennett__Delacruz_and_Walls Natasha_Harbors  5863502247054 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd         True  \n",
       "                                                                     4285071567351 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd         True  \n",
       "                                                    Dominique_Branch 4579248894449 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        False  \n",
       "                                                                     7731097392608 netops_predictor_v1 RandomForestClassifier NaN    jupyter-558bf7fbc8-sq5kd        False  \n",
       "\n",
       "[5759 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_parquet('../streaming/predictions/20200630T064217-20200630T074212.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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