wids-workflow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlrun\n",
    "from sys import path\n",
    "\n",
    "# Load the project with configuration\n",
    "\n",
    "project_name_base = 'widsdb2'\n",
    "project_path = 'conf'\n",
    "widsdb2_proj = mlrun.projects.load_project(project_path,  clone=True)\n",
    "\n",
    "project = widsdb2_proj\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['prep', 'train-wids', 'describe', 'test-classifier', 'tstprep', 'lightgbm-serving', 'live_tester', 'qnaserving']\n"
     ]
    }
   ],
   "source": [
    "#widsdb2_proj.functions\n",
    "project = widsdb2_proj\n",
    "names = [f.get('name') for f in widsdb2_proj.functions]\n",
    "print(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "livetester = project.set_function('hub://v2_model_tester', 'live_tester')\n",
    "\n",
    "project.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting conf/wf.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile {'conf/wf.py' }\n",
    "\n",
    "from kfp import dsl\n",
    "import mlrun\n",
    "from mlrun.platforms import auto_mount\n",
    "\n",
    "\n",
    "funcs = {}\n",
    "DATASET = 'train_enc'\n",
    "TST_DATASET = 'test_enc'\n",
    "LABELS =  'diabetes_mellitus'\n",
    "MODEL = 'lgbm_model'\n",
    "test_src = 'v3io:///projects/widsdb2/artifacts/raw_test_data.csv'\n",
    "train_src =  'v3io:///projects/widsdb2/artifacts/raw_train_data.csv'\n",
    "\n",
    "\n",
    "# Configure function resources and local settings\n",
    "def init_functions(functions: dict, project=None, secrets=None):\n",
    "    for f in functions.values():\n",
    "        f.apply(auto_mount())\n",
    "\n",
    "# Create a Kubeflow Pipelines pipeline\n",
    "@dsl.pipeline(\n",
    "    name=\"WidsDB2\",\n",
    "    description=\"This workflow implements the pipeline for data preprocessing, training model \"\n",
    "                \"serving for Widsdb2 dataset \\n\"\n",
    "                \n",
    ")\n",
    "\n",
    "def kfpipeline(source_url=train_src, test_url=test_src):\n",
    "\n",
    "    # Ingest the data set\n",
    "    ingest = funcs['prep'].as_step(\n",
    "        name=\"prep\",\n",
    "        handler='trdata_prep',\n",
    "        inputs={'src': source_url},\n",
    "        outputs=[DATASET])\n",
    "    \n",
    "     # Ingest the data set\n",
    "    test = funcs['tstprep'].as_step(\n",
    "        name=\"tstprep\",\n",
    "        handler='tstdata_prep',\n",
    "        inputs={'src': test_url},\n",
    "        outputs=[TST_DATASET])\n",
    "    \n",
    "      # Train a model   \n",
    "    train = funcs[\"train-wids\"].as_step(\n",
    "        name=\"train-wids\",\n",
    "        params={\"label_column\": LABELS},\n",
    "        inputs={\"dataset\": ingest.outputs[DATASET]},\n",
    "        outputs=['model', 'test_set'])\n",
    "  \n",
    "     # Deploy the model as a serverless function\n",
    "    deploy = funcs[\"lightgbm-serving\"].deploy_step(\n",
    "        models={f\"{MODEL}_v1\": train.outputs['model']})\n",
    "   \n",
    "    deployqna = funcs[\"qnaserving\"].deploy_step(\n",
    "        models={f\"bertqa_model_v1\": '/v3io/projects/widsdb2/util/qa.pkl'})\n",
    "  \n",
    "\n",
    "\n",
    "    #test out new model server (via REST API calls)\n",
    "    tester = funcs[\"live_tester\"].as_step(name='model-tester',\n",
    "        params={'addr': deploy.outputs['endpoint'], 'model': f\"{MODEL}_v1\", 'label_column':LABELS},\n",
    "        inputs={'table': train.outputs['test_set']})\n",
    "    \n",
    "    \n",
    "           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Register the workflow file as \"infer\"\n",
    "project.set_workflow('infer', 'wf.py')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "project.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'v3io:///projects/{{run.project}}/artifacts'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os \n",
    "from os import environ, path\n",
    "from mlrun import mlconf\n",
    "mlconf.artifact_path\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-07-06 12:48:57,074 [info] using in-cluster config.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Experiment link <a href=\"https://dashboard.default-tenant.app.mlops1.iguazio-c0.com/pipelines/#/experiments/details/4b8af965-6e38-4fe8-9598-3cbdcfdac1cf\" target=\"_blank\" >here</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run link <a href=\"https://dashboard.default-tenant.app.mlops1.iguazio-c0.com/pipelines/#/runs/details/e34aa5d6-ba4e-427b-bbb2-a569fab7b224\" target=\"_blank\" >here</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-07-06 12:48:58,093 [info] Pipeline run id=e34aa5d6-ba4e-427b-bbb2-a569fab7b224, check UI or DB for progress\n",
      "> 2021-07-06 12:48:58,094 [info] waiting for pipeline run completion\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Value must have type '<class 'int'>'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-a5cc33d74ae1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0martifact_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipeline_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"pipeline\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'{{wf.uid}}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mdirty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     watch=True)\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pythonlibs/jupyter-nb/lib/python3.7/site-packages/mlrun/projects/project.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, name, workflow_path, arguments, artifact_path, namespace, sync, watch, dirty, ttl)\u001b[0m\n\u001b[1;32m   1327\u001b[0m             \u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworkflow_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1328\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mwatch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1329\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_run_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnotifiers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRunNotifications\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwith_slack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1330\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pythonlibs/jupyter-nb/lib/python3.7/site-packages/mlrun/projects/project.py\u001b[0m in \u001b[0;36mget_run_status\u001b[0;34m(self, workflow_id, timeout, expected_statuses, notifiers)\u001b[0m\n\u001b[1;32m   1387\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1388\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnotifiers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1389\u001b[0;31m             \u001b[0mnotifiers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mruns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1390\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhad_errors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pythonlibs/jupyter-nb/lib/python3.7/site-packages/mlrun/utils/helpers.py\u001b[0m in \u001b[0;36mpush\u001b[0;34m(self, message, runs)\u001b[0m\n\u001b[1;32m    670\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 672\u001b[0;31m             \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mruns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    674\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mruns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pythonlibs/jupyter-nb/lib/python3.7/site-packages/mlrun/utils/helpers.py\u001b[0m in \u001b[0;36m_get_html\u001b[0;34m(self, message, runs)\u001b[0m\n\u001b[1;32m    678\u001b[0m         \u001b[0mhtml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"<h2>Run Results</h2>\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m         \u001b[0mhtml\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"<br>click the hyper links below to see detailed results<br>\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\u001b[0;31m         \u001b[0mhtml\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mruns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    681\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhtml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mhtml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pythonlibs/jupyter-nb/lib/python3.7/site-packages/mlrun/lists.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(self, display, classes, short)\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m         \u001b[0mhtml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mruns_to_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshort\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mhtml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pythonlibs/jupyter-nb/lib/python3.7/site-packages/mlrun/render.py\u001b[0m in \u001b[0;36mruns_to_html\u001b[0;34m(df, display, classes, short)\u001b[0m\n\u001b[1;32m    378\u001b[0m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpand_error\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    379\u001b[0m     \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"error\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 380\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moption_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"display.max_colwidth\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    381\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mget_tblframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pythonlibs/jupyter-nb/lib/python3.7/site-packages/pandas/_config/config.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mpat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\u001b[0;31m             \u001b[0m_set_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msilent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pythonlibs/jupyter-nb/lib/python3.7/site-packages/pandas/_config/config.py\u001b[0m in \u001b[0;36m_set_option\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    125\u001b[0m         \u001b[0mo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_registered_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m             \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m         \u001b[0;31m# walk the nested dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pythonlibs/jupyter-nb/lib/python3.7/site-packages/pandas/_config/config.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    770\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0m_type\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    771\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Value must have type '{typ!s}'\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 772\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtyp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    773\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    774\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Value must have type '<class 'int'>'"
     ]
    }
   ],
   "source": [
    "pipeline_path = mlconf.artifact_path\n",
    "\n",
    "run_id = project.run(\n",
    "    'infer',\n",
    "    arguments={}, \n",
    "    artifact_path=os.path.join(pipeline_path, \"pipeline\", '{{wf.uid}}'),\n",
    "    dirty=True,\n",
    "    watch=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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