Real_Time_Energy_Data_Low_Carbon_Training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d0e6deaf",
   "metadata": {},
   "source": [
    "# Real Time Energy Data for Low Carbon Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9644965e",
   "metadata": {},
   "source": [
    "> [Note] \n",
    "> This project is run by Vertex AI, which is only available after configuring Google Cloud environment. For other cloud provider users, please check the relevant training location services with your cloud providers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7836ab84",
   "metadata": {},
   "source": [
    "### Initialize Vertex AI and set up training environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3763854d",
   "metadata": {
    "height": 166
   },
   "outputs": [],
   "source": [
    "from helper import authenticate\n",
    "CREDENTIALS, PROJECT_ID = authenticate()\n",
    "\n",
    "from google.cloud import aiplatform\n",
    "\n",
    "aiplatform.init(project=PROJECT_ID,\n",
    "                credentials=CREDENTIALS,\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26f7f1fa",
   "metadata": {},
   "source": [
    "### Load the Electricity Maps API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fb1a4e6-2ced-4991-b460-d0d62cebf2a6",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "352921a6-c5fa-4976-b517-072fb7e18b05",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "from helper import load_env\n",
    "load_env()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "068ff8e8-0edf-47fb-90bd-96db9d62511e",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "from helper import load_emaps_api_key\n",
    "API_KEY = load_emaps_api_key()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9db9de9b-8428-4c3c-9b80-78c8451cb79b",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "# Iowa coordinates for this example\n",
    "coordinates = {\"lat\": 41.9216734, \"lon\": -93.3122705}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4463842e-a2e5-4bd9-a02f-371315e467bd",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "url= f\"https://api.electricitymap.org/v3/carbon-intensity/latest?lon={coordinates['lon']}&lat={coordinates['lat']}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff6b243b-5d82-4936-b84a-642a9ee1e976",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "request = requests.get(url,\n",
    "                       headers={\"auth-token\": API_KEY},)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "117a09f8-7595-473e-a7b9-4446759d6c6f",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'zone': 'US-MIDW-MISO',\n",
       " 'carbonIntensity': 564,\n",
       " 'datetime': '2024-07-18T08:00:00.000Z',\n",
       " 'updatedAt': '2024-07-18T07:49:45.314Z',\n",
       " 'createdAt': '2024-07-15T08:49:12.482Z',\n",
       " 'emissionFactorType': 'lifecycle',\n",
       " 'isEstimated': True,\n",
       " 'estimationMethod': 'TIME_SLICER_AVERAGE'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.loads(request.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6d8c9098-6a74-4e21-be60-ab5f674e26aa",
   "metadata": {
    "height": 1101
   },
   "outputs": [],
   "source": [
    "# Dictionary\n",
    "VERTEX_REGIONS = [\n",
    "    {\"id\": \"northamerica-northeast1\", \n",
    "     \"name\": \"Montréal\", \n",
    "     \"lat\": 45.5031824, \"lon\": -73.5698065},\n",
    "    {\"id\": \"northamerica-northeast2\", \n",
    "     \"name\": \"Toronto\", \n",
    "     \"lat\": 43.6534817, \"lon\": -79.3839347},\n",
    "    {\"id\": \"southamerica-east1\", \n",
    "     \"name\": \"São Paulo\", \n",
    "     \"lat\": -23.5506507, \"lon\": -46.6333824},\n",
    "    {\"id\": \"us-central1\", \n",
    "     \"name\": \"Iowa\", \n",
    "     \"lat\": 41.9216734, \"lon\": -93.3122705},\n",
    "    {\"id\": \"us-east1\", \n",
    "     \"name\": \"South Carolina\", \n",
    "     \"lat\": 33.6874388, \"lon\": -80.4363743},\n",
    "    {\"id\": \"us-east4\", \n",
    "     \"name\": \"Northern Virginia\", \n",
    "     \"lat\": -12.548285, \"lon\": 131.017405},\n",
    "    {\"id\": \"us-west1\", \n",
    "     \"name\": \"Oregon\", \n",
    "     \"lat\": 43.9792797, \"lon\": -120.737257},\n",
    "    {\"id\": \"us-west2\", \n",
    "     \"name\": \"Los Angeles\", \n",
    "     \"lat\": 34.0536909, \"lon\": -118.242766},\n",
    "    {\"id\": \"us-west4\", \n",
    "     \"name\": \"Las Vegas\", \n",
    "     \"lat\": 36.1672559, \"lon\": -115.148516},\n",
    "    {\"id\": \"asia-east2\", \n",
    "     \"name\": \"Hong Kong\", \n",
    "     \"lat\": 22.2793278, \"lon\": 114.1628131},\n",
    "    {\"id\": \"asia-northeast1\", \n",
    "     \"name\": \"Tokyo\", \"lat\": 35.6828387, \"lon\": 139.7594549},\n",
    "    {\"id\": \"asia-northeast3\", \n",
    "     \"name\": \"Seoul\", \n",
    "     \"lat\": 37.5666791, \"lon\": 126.9782914},\n",
    "    {\"id\": \"asia-south1\", \n",
    "     \"name\": \"Mumbai\", \n",
    "     \"lat\": 19.0785451, \"lon\": 72.878176},\n",
    "    {\"id\": \"asia-southeast1\", \n",
    "     \"name\": \"Singapore\", \n",
    "     \"lat\": 1.357107, \"lon\": 103.8194992},\n",
    "    {\"id\": \"asia-southeast2\", \n",
    "     \"name\": \"Jakarta\", \n",
    "     \"lat\": -6.1753942, \"lon\": 106.827183},\n",
    "    {\"id\": \"australia-southeast1\", \n",
    "     \"name\": \"Sydney\", \n",
    "     \"lat\": -33.8698439, \"lon\": 151.2082848},\n",
    "    {\"id\": \"europe-west1\", \n",
    "     \"name\": \"Belgium\", \n",
    "     \"lat\": 50.6402809, \"lon\": 4.6667145},\n",
    "    {\"id\": \"europe-west2\", \n",
    "     \"name\": \"London\", \n",
    "     \"lat\": 51.5073219, \"lon\": -0.1276474},\n",
    "    {\"id\": \"europe-west3\", \n",
    "     \"name\": \"Frankfurt\", \n",
    "     \"lat\": 50.1106444, \"lon\": 8.6820917},\n",
    "    {\"id\": \"europe-west4\", \n",
    "     \"name\": \"Netherlands\", \n",
    "     \"lat\": 52.2434979, \"lon\": 5.6343227},\n",
    "    {\"id\": \"europe-west6\", \n",
    "     \"name\": \"Zurich\", \"lat\": 47.3744489, \"lon\": 8.5410422},\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c4f13b",
   "metadata": {},
   "source": [
    "### Select a training location based on public carbon intensity reference page and define carbon intensity of the selected training region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1f8b499c-c678-4de0-b483-ae0f7a26d28a",
   "metadata": {
    "height": 370
   },
   "outputs": [],
   "source": [
    "def carbon_intensity(VERTEX_REGIONS, API_KEY, region):\n",
    "\n",
    "  region_obj = [r for r \n",
    "                in VERTEX_REGIONS \n",
    "                if region == r[\"id\"]][0]\n",
    "\n",
    "  url= f\"https://api.electricitymap.org/v3/carbon-intensity/latest?lon={region_obj['lon']}&lat={region_obj['lat']}\"\n",
    "\n",
    "  request = requests.get(url,\n",
    "                       headers={\"auth-token\": API_KEY},)\n",
    "\n",
    "  response = json.loads(request.content)\n",
    "\n",
    "  if \"carbonIntensity\" not in response.keys():\n",
    "    raise LookupError(\n",
    "        f\"Carbon intensity data not available \"\n",
    "        f\"from Electricity Maps for region {region}.\"\n",
    "    )\n",
    "\n",
    "  return int(response[\"carbonIntensity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b4cc2856-25b2-4c09-b134-f7ac41b6811f",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "region = 'asia-northeast3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d440a0d7-0bea-4df8-bcd5-6ed9cac53c76",
   "metadata": {
    "height": 64
   },
   "outputs": [],
   "source": [
    "region_obj = [r for r \n",
    "              in VERTEX_REGIONS \n",
    "              if region == r[\"id\"]][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1d9ff1d1-1ed6-4caa-9dac-fa106f97b88a",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'asia-northeast3',\n",
       " 'name': 'Seoul',\n",
       " 'lat': 37.5666791,\n",
       " 'lon': 126.9782914}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b9817af-ba0d-4833-8f41-58487e7e9da1",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "url= f\"https://api.electricitymap.org/v3/carbon-intensity/latest?lon={region_obj['lon']}&lat={region_obj['lat']}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "300b0847-3d01-4e26-9c8e-07d4f093014e",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "request = requests.get(url,\n",
    "                      headers={\"auth-token\": API_KEY},)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc44c73e-667a-4d0b-9bbc-2704b5a63f0e",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "response = json.loads(request.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0a8fe18-bc6b-481d-a4fe-f3a28682e7ec",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8f2b1fb2-245b-4a7b-9367-dc269668c36a",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "456"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(response[\"carbonIntensity\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9129bfd",
   "metadata": {},
   "source": [
    "### Define a cleanest function to select a training location from the region list by API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3d1ede78-430a-45f5-a795-ff2eef8bd491",
   "metadata": {
    "height": 370
   },
   "outputs": [],
   "source": [
    "def cleanest(VERTEX_REGIONS, API_KEY):\n",
    "\n",
    "  carbon_intensity_for_regions = []\n",
    "\n",
    "  for region in VERTEX_REGIONS:\n",
    "    try:\n",
    "      carbon_intensity_for_regions.append({\n",
    "        **region,\n",
    "        **{\"carbon_intensity\": carbon_intensity(\n",
    "            VERTEX_REGIONS, \n",
    "            API_KEY, \n",
    "            str(region[\"id\"]))}\n",
    "        })\n",
    "    except LookupError:  # Skip over errors for individual regions.\n",
    "        logging.warning(\n",
    "          f\"Could not get carbon intensity data \"\n",
    "            f\"for region {region['id']}, so it was skipped.\"\n",
    "        )\n",
    "\n",
    "  return min(carbon_intensity_for_regions, \n",
    "             key=lambda x: x[\"carbon_intensity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "978e2d72-ca2a-4f20-b48d-a3c40d1cc0b2",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "carbon_intensity_for_regions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b6228b9-a545-42ff-957f-6f36bec8ed90",
   "metadata": {
    "height": 149
   },
   "outputs": [],
   "source": [
    "for region in VERTEX_REGIONS:\n",
    "  carbon_intensity_for_regions.append({\n",
    "      **region,\n",
    "      **{\"carbon_intensity\": carbon_intensity(\n",
    "          VERTEX_REGIONS, \n",
    "          API_KEY, \n",
    "          str(region[\"id\"]))}\n",
    "      })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ad6a41e5-5512-448e-95b9-ea1c413ca483",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'id': 'northamerica-northeast1', 'name': 'Montréal', 'lat': 45.5031824, 'lon': -73.5698065, 'carbon_intensity': 30}, {'id': 'northamerica-northeast2', 'name': 'Toronto', 'lat': 43.6534817, 'lon': -79.3839347, 'carbon_intensity': 60}, {'id': 'southamerica-east1', 'name': 'São Paulo', 'lat': -23.5506507, 'lon': -46.6333824, 'carbon_intensity': 132}, {'id': 'us-central1', 'name': 'Iowa', 'lat': 41.9216734, 'lon': -93.3122705, 'carbon_intensity': 564}, {'id': 'us-east1', 'name': 'South Carolina', 'lat': 33.6874388, 'lon': -80.4363743, 'carbon_intensity': 966}, {'id': 'us-east4', 'name': 'Northern Virginia', 'lat': -12.548285, 'lon': 131.017405, 'carbon_intensity': 491}, {'id': 'us-west1', 'name': 'Oregon', 'lat': 43.9792797, 'lon': -120.737257, 'carbon_intensity': 344}, {'id': 'us-west2', 'name': 'Los Angeles', 'lat': 34.0536909, 'lon': -118.242766, 'carbon_intensity': 282}, {'id': 'us-west4', 'name': 'Las Vegas', 'lat': 36.1672559, 'lon': -115.148516, 'carbon_intensity': 575}, {'id': 'asia-east2', 'name': 'Hong Kong', 'lat': 22.2793278, 'lon': 114.1628131, 'carbon_intensity': 435}, {'id': 'asia-northeast1', 'name': 'Tokyo', 'lat': 35.6828387, 'lon': 139.7594549, 'carbon_intensity': 558}, {'id': 'asia-northeast3', 'name': 'Seoul', 'lat': 37.5666791, 'lon': 126.9782914, 'carbon_intensity': 456}, {'id': 'asia-south1', 'name': 'Mumbai', 'lat': 19.0785451, 'lon': 72.878176, 'carbon_intensity': 744}, {'id': 'asia-southeast1', 'name': 'Singapore', 'lat': 1.357107, 'lon': 103.8194992, 'carbon_intensity': 479}, {'id': 'asia-southeast2', 'name': 'Jakarta', 'lat': -6.1753942, 'lon': 106.827183, 'carbon_intensity': 652}, {'id': 'australia-southeast1', 'name': 'Sydney', 'lat': -33.8698439, 'lon': 151.2082848, 'carbon_intensity': 643}, {'id': 'europe-west1', 'name': 'Belgium', 'lat': 50.6402809, 'lon': 4.6667145, 'carbon_intensity': 125}, {'id': 'europe-west2', 'name': 'London', 'lat': 51.5073219, 'lon': -0.1276474, 'carbon_intensity': 257}, {'id': 'europe-west3', 'name': 'Frankfurt', 'lat': 50.1106444, 'lon': 8.6820917, 'carbon_intensity': 314}, {'id': 'europe-west4', 'name': 'Netherlands', 'lat': 52.2434979, 'lon': 5.6343227, 'carbon_intensity': 210}, {'id': 'europe-west6', 'name': 'Zurich', 'lat': 47.3744489, 'lon': 8.5410422, 'carbon_intensity': 56}]\n"
     ]
    }
   ],
   "source": [
    "print(carbon_intensity_for_regions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f863124",
   "metadata": {},
   "source": [
    "### Find the region with lowest carbon intensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7bfd1e2-272b-4197-9e62-7bd59c30a9c8",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "min(carbon_intensity_for_regions,\n",
    "    key=lambda x:x['carbon_intensity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cfec7146-68fd-45bc-95f3-9d2cf2f0b165",
   "metadata": {
    "height": 47
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'id': 'northamerica-northeast1', 'name': 'Montréal', 'lat': 45.5031824, 'lon': -73.5698065, 'carbon_intensity': 30}\n"
     ]
    }
   ],
   "source": [
    "cleanest_region = cleanest(VERTEX_REGIONS, API_KEY)\n",
    "print(cleanest_region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ea12266f-af26-464b-a1ca-30f5220e3b97",
   "metadata": {
    "height": 183
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.555555555555555"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Old carbon intensity region\n",
    "# old_ca = int(response[\"carbonIntensity\"])\n",
    "old_ca = 416\n",
    "\n",
    "# Carbon intensity from the region recently selected\n",
    "# new_ca = int(cleanest_region[\"carbonIntensity\"])\n",
    "new_ca = 36\n",
    "\n",
    "# Compare carbon intensity\n",
    "old_ca/new_ca"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eccd4149",
   "metadata": {},
   "source": [
    "### Define the Custom Training Job including task.py, storage bucket, display job name in Vertex AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8de653ad-2b75-4df9-b6f9-d94e61a5e582",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "REGION = 'northamerica-northeast1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "10637c65-26c2-465c-b7a2-427ee6d0693f",
   "metadata": {
    "height": 217
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import string\n",
    "\n",
    "def generate_uuid(length: int = 8) -> str:\n",
    "    return \"\".join(\n",
    "        random.choices(\n",
    "            string.ascii_lowercase + string.digits, \n",
    "            k=length))\n",
    " \n",
    "\n",
    "UUID = generate_uuid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4378030c-1805-42e5-ad5c-d25e041f2260",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "bucket_name = f'carbon-course-cleaner-bucket-{UUID}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2691696e",
   "metadata": {
    "height": 47
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'carbon-course-cleaner-bucket-85ebok0j'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucket_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c977f9f4",
   "metadata": {
    "height": 81
   },
   "outputs": [],
   "source": [
    "from google.cloud import storage\n",
    "\n",
    "storage_client = storage.Client(project = PROJECT_ID,\n",
    "                                credentials = CREDENTIALS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cdedcb35-1c49-4d84-bf08-f02ba5da5d08",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "bucket = storage_client.bucket(bucket_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5e744b20-2b7a-444a-9e6f-6bbe11bc67ee",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "bucket.create(location=REGION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "69933c2f-cec2-4dde-a11b-15003c76dc06",
   "metadata": {
    "height": 166
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training script copied to:\n",
      "gs://carbon-course-cleaner-bucket-85ebok0j/aiplatform-2024-07-18-08-49-25.274-aiplatform_custom_trainer_script-0.1.tar.gz.\n",
      "Training Output directory:\n",
      "gs://carbon-course-cleaner-bucket-85ebok0j/aiplatform-custom-training-2024-07-18-08-49-25.274\n",
      "View Training:\n",
      "https://console.cloud.google.com/ai/platform/locations/us-central1/training/3727316689851777024?project=362161713137\n",
      "View backing custom job:\n",
      "https://console.cloud.google.com/ai/platform/locations/us-central1/training/1619438550195896320?project=362161713137\n",
      "CustomTrainingJob projects/362161713137/locations/us-central1/trainingPipelines/3727316689851777024 current state:\n",
      "PipelineState.PIPELINE_STATE_RUNNING\n",
      "CustomTrainingJob projects/362161713137/locations/us-central1/trainingPipelines/3727316689851777024 current state:\n",
      "PipelineState.PIPELINE_STATE_RUNNING\n",
      "CustomTrainingJob projects/362161713137/locations/us-central1/trainingPipelines/3727316689851777024 current state:\n",
      "PipelineState.PIPELINE_STATE_RUNNING\n",
      "CustomTrainingJob projects/362161713137/locations/us-central1/trainingPipelines/3727316689851777024 current state:\n",
      "PipelineState.PIPELINE_STATE_RUNNING\n",
      "CustomTrainingJob projects/362161713137/locations/us-central1/trainingPipelines/3727316689851777024 current state:\n",
      "PipelineState.PIPELINE_STATE_RUNNING\n",
      "CustomTrainingJob run completed. Resource name: projects/362161713137/locations/us-central1/trainingPipelines/3727316689851777024 is not configured to upload a Model. Create the Training Pipeline with model_serving_container_image_uri and model_display_name passed in. Ensure that your training script saves to model to os.environ['AIP_MODEL_DIR']\n"
     ]
    }
   ],
   "source": [
    "job = aiplatform.CustomTrainingJob(\n",
    "    display_name='dlai-course-example-cleaner',\n",
    "    script_path='task.py',\n",
    "    container_uri='us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-14.py310:latest',\n",
    "    staging_bucket=f'gs://{bucket_name}',\n",
    "    location=REGION,)\n",
    "\n",
    "model = job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7eda2f61-3f69-48f9-b25d-ba0db837be4e",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "bucket.delete(force=True)"
   ]
  }
 ],
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