{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kfp\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = kfp.Client()\n",
    "import datetime\n",
    "iteration = datetime.datetime.now().strftime('%Y%m%d%H%M%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.getenv(\"ML_PIPELINE_SERVICE_PORT\"):\n",
    "    try: \n",
    "        client.get_experiment(experiment_name='Hybrid Pipelines - The One with the Two steps')\n",
    "    except:\n",
    "        hybrid_experiment = client.create_experiment(name='Hybrid Pipelines - The One with the Two steps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.getenv(\"ML_PIPELINE_SERVICE_PORT\"):\n",
    "    run = client.run_pipeline(client.get_experiment(experiment_name='Hybrid Pipelines - The One with the Two steps').id, \n",
    "                              'hybrid_pipeline-'+str(iteration), 'hybrid_pipeline.tar.gz', \n",
    "                              params={})\n"
   ]
  }
 ],
 "metadata": {
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   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
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 "nbformat": 4,
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