model_server_tester/model_server_tester.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Live Model Server Testing\n",
    "\n",
    "Test model server via HTTP calls "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nuclio: ignore\n",
    "import nuclio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%nuclio: setting spec.image to 'mlrun/mlrun'\n"
     ]
    }
   ],
   "source": [
    "%nuclio config spec.image = \"mlrun/mlrun\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import requests\n",
    "import json\n",
    "import numpy as np\n",
    "\n",
    "from datetime import datetime\n",
    "from mlrun.datastore import DataItem\n",
    "from mlrun.artifacts import get_model, ChartArtifact\n",
    "\n",
    "def model_server_tester(context,\n",
    "                        table: DataItem,\n",
    "                        addr: str, \n",
    "                        label_column: str = \"label\",\n",
    "                        model: str = '',\n",
    "                        match_err: bool = False,\n",
    "                        rows: int = 20):\n",
    "    \"\"\" Test a model server \n",
    "    \n",
    "    :param table:         csv/parquet table with test data\n",
    "    :param addr:          function address/url\n",
    "    :param label_column:  name of the label column in table\n",
    "    :param model:         tested model name \n",
    "    :param match_err:     raise error on validation (require proper test set)\n",
    "    :param rows:          number of rows to use from test set\n",
    "    \"\"\"\n",
    "        \n",
    "    table = table.as_df()\n",
    "\n",
    "    y_list = table.pop(label_column).values.tolist()\n",
    "    context.logger.info(f'testing with dataset against {addr}, model: {model}')\n",
    "    if rows and rows < table.shape[0]:\n",
    "        table = table.sample(rows)\n",
    "    \n",
    "    count = err_count = match = 0\n",
    "    times = []\n",
    "    for x, y in zip(table.values, y_list):\n",
    "        count += 1\n",
    "        event_data = json.dumps({\"instances\":[x.tolist()]})\n",
    "        had_err = False\n",
    "        try:\n",
    "            start = datetime.now()\n",
    "            resp = requests.put(f'{addr}/{model}/predict', json=event_data)\n",
    "            if not resp.ok:\n",
    "                context.logger.error(f'bad function resp!!\\n{resp.text}')\n",
    "                err_count += 1\n",
    "                continue\n",
    "            times.append((datetime.now()-start).microseconds)\n",
    "                \n",
    "        except OSError as err:\n",
    "            context.logger.error(f'error in request, data:{event_data}, error: {err}')\n",
    "            err_count += 1\n",
    "            continue\n",
    "            \n",
    "        y_resp = resp.json()[0]\n",
    "        if y == y_resp:\n",
    "            match += 1\n",
    "        \n",
    "    context.log_result('total_tests', count)\n",
    "    context.log_result('errors', err_count)\n",
    "    context.log_result('match', match)\n",
    "    if count - err_count > 0:\n",
    "        times_arr = np.array(times)\n",
    "        context.log_result('avg_latency', int(np.mean(times_arr)))\n",
    "        context.log_result('min_latency', int(np.amin(times_arr)))\n",
    "        context.log_result('max_latency', int(np.amax(times_arr)))\n",
    "        \n",
    "        chart = ChartArtifact('latency', header=['Test', 'Latency (microsec)'])\n",
    "        for i in range(len(times)):\n",
    "            chart.add_row([i+1, int(times[i])])\n",
    "        context.log_artifact(chart)\n",
    "\n",
    "    context.logger.info(f'run {count} tests, {err_count} errors and {match} match expected value')\n",
    "    \n",
    "    if err_count:\n",
    "        raise ValueError(f'failed on {err_count} tests of {count}')\n",
    "    \n",
    "    if match_err and match != count:\n",
    "        raise ValueError(f'only {match} results match out of {count}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nuclio: end-code\n",
    "# marks the end of a code section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deploy model server for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2020-10-28 16:43:54,679 [warning] warning!, server (0.5.3-rc1) and client (0.5.2) ver dont match\n",
      "> 2020-10-28 16:43:55,002 [info] deploy started\n",
      "[nuclio] 2020-10-28 16:45:17,274 (info) Build complete\n",
      "[nuclio] 2020-10-28 16:45:22,363 done updating default-model-server, function address: default-tenant.app.dsteam.iguazio-cd1.com:30150\n",
      "> 2020-10-28 16:45:22,369 [warning] warning!, server (0.5.3-rc1) and client (0.5.2) ver dont match\n"
     ]
    }
   ],
   "source": [
    "import mlrun\n",
    "project_name = 'sk-project'\n",
    "MODEL_PATH = 'https://s3.wasabisys.com/iguazio/models/iris/model.pkl'\n",
    "\n",
    "artifact_path = mlrun.set_environment(api_path = 'http://mlrun-api:8080',\n",
    "                                      artifact_path = os.path.abspath('./'))\n",
    "\n",
    "# import model server function from hub\n",
    "fn = mlrun.import_function('hub://model_server')\n",
    "fn.add_model(\"mymodel\", MODEL_PATH)\n",
    "address = fn.deploy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2020-10-28 16:45:22,418 [info] deploy started\n",
      "[nuclio] 2020-10-28 16:45:25,272 (info) Build complete\n",
      "[nuclio] 2020-10-28 16:45:34,868 done updating default-model-server, function address: default-tenant.app.dsteam.iguazio-cd1.com:30150\n"
     ]
    }
   ],
   "source": [
    "user_name = os.getenv('V3IO_USERNAME')\n",
    "\n",
    "fn.apply(mlrun.mount_v3io())\n",
    "fn.set_envs({'SERVING_MODEL_iris_dataset_v1': MODEL_PATH,\n",
    "             'INFERENCE_STREAM': 'users/{}/tststream'.format(user_name)})\n",
    "\n",
    "address = fn.deploy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run model server tester locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2020-10-28 16:45:34,916 [warning] warning!, server (0.5.3-rc1) and client (0.5.2) ver dont match\n",
      "> 2020-10-28 16:45:34,916 [info] starting run model_server_tester uid=c84fdd4dfacd447dbe417d709f5983f0  -> http://mlrun-api:8080\n",
      "> 2020-10-28 16:45:34,972 [warning] warning!, server (0.5.3-rc1) and client (0.5.2) ver dont match\n",
      "> 2020-10-28 16:45:35,264 [info] testing with dataset against http://default-tenant.app.dsteam.iguazio-cd1.com:30150, model: mymodel\n",
      "> 2020-10-28 16:45:35,967 [info] run 20 tests, 0 errors and 6 match expected value\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>project</th>\n",
       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>sk-project</td>\n",
       "      <td><div title=\"c84fdd4dfacd447dbe417d709f5983f0\"><a href=\"https://mlrun-ui.default-tenant.app.dsteam.iguazio-cd1.com/projects/sk-project/jobs/c84fdd4dfacd447dbe417d709f5983f0/info\" target=\"_blank\" >...9f5983f0</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Oct 28 16:45:34</td>\n",
       "      <td>completed</td>\n",
       "      <td>model_server_tester</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=admin</div><div class=\"dictlist\">kind=handler</div><div class=\"dictlist\">owner=admin</div><div class=\"dictlist\">host=jupyter-d87678b84-n4lcf</div></td>\n",
       "      <td><div title=\"https://s3.wasabisys.com/iguazio/data/iris/iris_dataset.csv\">table</div></td>\n",
       "      <td><div class=\"dictlist\">addr=http://default-tenant.app.dsteam.iguazio-cd1.com:30150</div><div class=\"dictlist\">model=mymodel</div></td>\n",
       "      <td><div class=\"dictlist\">total_tests=20</div><div class=\"dictlist\">errors=0</div><div class=\"dictlist\">match=6</div><div class=\"dictlist\">avg_latency=32619</div><div class=\"dictlist\">min_latency=26263</div><div class=\"dictlist\">max_latency=110524</div></td>\n",
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     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run c84fdd4dfacd447dbe417d709f5983f0 --project sk-project , !mlrun logs c84fdd4dfacd447dbe417d709f5983f0 --project sk-project\n",
      "> 2020-10-28 16:45:36,042 [info] run executed, status=completed\n"
     ]
    }
   ],
   "source": [
    "# run the function locally\n",
    "DATA_PATH  = 'https://s3.wasabisys.com/iguazio/data/iris/iris_dataset.csv'\n",
    "\n",
    "gen = mlrun.run_local(name='model_server_tester', \n",
    "                      handler=model_server_tester, \n",
    "                      params={'addr': address, 'model': 'mymodel'},\n",
    "                      inputs={'table': DATA_PATH},\n",
    "                      project=project_name, \n",
    "                      artifact_path=os.path.join(artifact_path, 'data')) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2020-10-28 16:45:43,591 [info] function spec saved to path: function.yaml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.runtimes.kubejob.KubejobRuntime at 0x7fde772f2f50>"
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     "execution_count": 8,
     "metadata": {},
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    }
   ],
   "source": [
    "test_func = mlrun.code_to_function(name='model_server_tester', \n",
    "                                   kind='job',\n",
    "                                   handler='model_server_tester',\n",
    "                                   description='test model servers',\n",
    "                                   categories=[\"ml\": \"test\"],\n",
    "                                   labels={\"author\": \"yaronh\"})\n",
    "\n",
    "test_func.export('function.yaml')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run remotely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2020-10-28 16:45:43,605 [warning] warning!, server (0.5.3-rc1) and client (0.5.2) ver dont match\n",
      "> 2020-10-28 16:45:43,606 [info] starting run model_server_tester uid=73cb7da1bfeb4b50afb29bce40a9c861  -> http://mlrun-api:8080\n",
      "> 2020-10-28 16:45:43,738 [info] Job is running in the background, pod: model-server-tester-btgvj\n"
     ]
    },
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       "        }\n",
       "    }\n",
       "}\n",
       "function expandPanel(el) {\n",
       "  const panelName = \"#\" + el.getAttribute('paneName');\n",
       "  console.log(el.title);\n",
       "\n",
       "  document.querySelector(panelName + \"-title\").innerHTML = el.title\n",
       "  iframe = document.querySelector(panelName + \"-body\");\n",
       "\n",
       "  const tblcss = `<style> body { font-family: Arial, Helvetica, sans-serif;}\n",
       "    #csv { margin-bottom: 15px; }\n",
       "    #csv table { border-collapse: collapse;}\n",
       "    #csv table td { padding: 4px 8px; border: 1px solid silver;} </style>`;\n",
       "\n",
       "  function csvToHtmlTable(str) {\n",
       "    return '<div id=\"csv\"><table><tr><td>' +  str.replace(/[\\n\\r]+$/g, '').replace(/[\\n\\r]+/g, '</td></tr><tr><td>')\n",
       "      .replace(/,/g, '</td><td>') + '</td></tr></table></div>';\n",
       "  }\n",
       "\n",
       "  function reqListener () {\n",
       "    if (el.title.endsWith(\".csv\")) {\n",
       "      iframe.setAttribute(\"srcdoc\", tblcss + csvToHtmlTable(this.responseText));\n",
       "    } else {\n",
       "      iframe.setAttribute(\"srcdoc\", this.responseText);\n",
       "    }\n",
       "    console.log(this.responseText);\n",
       "  }\n",
       "\n",
       "  const oReq = new XMLHttpRequest();\n",
       "  oReq.addEventListener(\"load\", reqListener);\n",
       "  oReq.open(\"GET\", el.title);\n",
       "  oReq.send();\n",
       "\n",
       "\n",
       "  //iframe.src = el.title;\n",
       "  const resultPane = document.querySelector(panelName + \"-pane\");\n",
       "  if (resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.remove(\"hidden\");\n",
       "  }\n",
       "}\n",
       "function closePanel(el) {\n",
       "  const panelName = \"#\" + el.getAttribute('paneName')\n",
       "  const resultPane = document.querySelector(panelName + \"-pane\");\n",
       "  if (!resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.add(\"hidden\");\n",
       "  }\n",
       "}\n",
       "\n",
       "</script>\n",
       "<div class=\"master-wrapper\">\n",
       "  <div class=\"block master-tbl\"><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>project</th>\n",
       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>sk-project</td>\n",
       "      <td><div title=\"73cb7da1bfeb4b50afb29bce40a9c861\"><a href=\"https://mlrun-ui.default-tenant.app.dsteam.iguazio-cd1.com/projects/sk-project/jobs/73cb7da1bfeb4b50afb29bce40a9c861/info\" target=\"_blank\" >...40a9c861</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Oct 28 16:45:43</td>\n",
       "      <td>running</td>\n",
       "      <td>model_server_tester</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=admin</div><div class=\"dictlist\">kind=job</div><div class=\"dictlist\">owner=admin</div></td>\n",
       "      <td><div title=\"https://s3.wasabisys.com/iguazio/data/iris/iris_dataset.csv\">table</div></td>\n",
       "      <td><div class=\"dictlist\">addr=http://default-tenant.app.dsteam.iguazio-cd1.com:30150</div><div class=\"dictlist\">model=mymodel</div></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></div>\n",
       "  <div id=\"resultd0b8d126-pane\" class=\"right-pane block hidden\">\n",
       "    <div class=\"pane-header\">\n",
       "      <span id=\"resultd0b8d126-title\" class=\"pane-header-title\">Title</span>\n",
       "      <span onclick=\"closePanel(this)\" paneName=\"resultd0b8d126\" class=\"close clickable\">&times;</span>\n",
       "    </div>\n",
       "    <iframe class=\"fileview\" id=\"resultd0b8d126-body\"></iframe>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run 73cb7da1bfeb4b50afb29bce40a9c861 --project sk-project , !mlrun logs 73cb7da1bfeb4b50afb29bce40a9c861 --project sk-project\n",
      "> 2020-10-28 16:45:43,790 [info] run executed, status=running\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.model.RunObject at 0x7fdeb84930d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_func.run(mlrun.NewTask(name='model_server_tester', \n",
    "                            handler=model_server_tester, \n",
    "                            params={'addr': address, 'model': 'mymodel'},\n",
    "                            inputs={'table': DATA_PATH},\n",
    "                            project=project_name, \n",
    "                            artifact_path=os.path.join(artifact_path, 'data')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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