L3_Training_Models__M.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a12b0f0",
   "metadata": {},
   "source": [
    "# Lesson 3: Training Models in Low Carbon Regions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de50c3f",
   "metadata": {},
   "source": [
    "* Import libraries to train a model locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b567f06-6dc3-429c-823a-c68f14a1171c",
   "metadata": {
    "height": 98
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-03 15:00:14.874074: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-09-03 15:00:15.408989: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-09-03 15:00:15.544501: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2024-09-03 15:00:15.544526: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2024-09-03 15:00:17.664378: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2024-09-03 15:00:17.665789: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2024-09-03 15:00:17.665797: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import make_blobs\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a3fce95",
   "metadata": {},
   "source": [
    "* Create a dataset.\n",
    "\n",
    "This is an example from the [Advanced Learning Algorithms](https://www.coursera.org/learn/advanced-learning-algorithms) course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "47b67bc4-f6fb-410c-a9da-735c37685ee1",
   "metadata": {
    "height": 166
   },
   "outputs": [],
   "source": [
    "classes = 4\n",
    "m = 100\n",
    "centers = [[-5, 2], [-2, -2], [1, 2], [5, -2]]\n",
    "std = 1.0\n",
    "X_train, y_train = make_blobs(\n",
    "    n_samples=m, \n",
    "    centers=centers, \n",
    "    cluster_std=std,\n",
    "    random_state=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fcb41a9",
   "metadata": {},
   "source": [
    "* Create the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "43a4297a-cc98-47b7-95d4-703caecdccb7",
   "metadata": {
    "height": 132
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-03 15:00:20.263562: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2024-09-03 15:00:20.264867: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2024-09-03 15:00:20.264900: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (ip-172-30-114-194.us-west-1.compute.internal): /proc/driver/nvidia/version does not exist\n",
      "2024-09-03 15:00:20.267533: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "# Create the model\n",
    "model = Sequential(\n",
    "    [\n",
    "        Dense(2, activation = 'relu',   name = \"L1\"),\n",
    "        Dense(4, activation = 'linear', name = \"L2\")\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bdcb0dae-f2a5-440e-9699-64ed4e80e055",
   "metadata": {
    "height": 98
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(\n",
    "        from_logits=True),\n",
    "    optimizer=tf.keras.optimizers.Adam(0.01),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "065a4823",
   "metadata": {},
   "source": [
    "* Train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ef0d574-3899-4808-88c1-de3e551528c8",
   "metadata": {
    "height": 81
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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      "4/4 [==============================] - 0s 1ms/step - loss: 0.0356\n",
      "Epoch 150/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0356\n",
      "Epoch 151/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0350\n",
      "Epoch 152/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0343\n",
      "Epoch 153/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0336\n",
      "Epoch 154/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0329\n",
      "Epoch 155/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0324\n",
      "Epoch 156/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0321\n",
      "Epoch 157/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0317\n",
      "Epoch 158/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0314\n",
      "Epoch 159/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0311\n",
      "Epoch 160/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0307\n",
      "Epoch 161/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0303\n",
      "Epoch 162/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0298\n",
      "Epoch 163/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0293\n",
      "Epoch 164/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0290\n",
      "Epoch 165/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0286\n",
      "Epoch 166/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0283\n",
      "Epoch 167/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0284\n",
      "Epoch 168/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0278\n",
      "Epoch 169/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0274\n",
      "Epoch 170/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0271\n",
      "Epoch 171/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0269\n",
      "Epoch 172/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0266\n",
      "Epoch 173/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0263\n",
      "Epoch 174/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0260\n",
      "Epoch 175/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0257\n",
      "Epoch 176/200\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0255\n",
      "Epoch 177/200\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0252\n",
      "Epoch 178/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0250\n",
      "Epoch 179/200\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0247\n",
      "Epoch 180/200\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0244\n",
      "Epoch 181/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0242\n",
      "Epoch 182/200\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0239\n",
      "Epoch 183/200\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0237\n",
      "Epoch 184/200\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0235\n",
      "Epoch 185/200\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0232\n",
      "Epoch 186/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0230\n",
      "Epoch 187/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0228\n",
      "Epoch 188/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0225\n",
      "Epoch 189/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0225\n",
      "Epoch 190/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0223\n",
      "Epoch 191/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0221\n",
      "Epoch 192/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0219\n",
      "Epoch 193/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0217\n",
      "Epoch 194/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0215\n",
      "Epoch 195/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0213\n",
      "Epoch 196/200\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0211\n",
      "Epoch 197/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0209\n",
      "Epoch 198/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0208\n",
      "Epoch 199/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0206\n",
      "Epoch 200/200\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0204\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f00ac420e80>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train,y_train,\n",
    "    epochs=200\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7916c263",
   "metadata": {},
   "source": [
    "### Running on Google Cloud\n",
    "\n",
    "We will use Google Cloud's ML platform, Vertex AI. To run this code on Vertex AI, we first need to:\n",
    "1. Import and initialize the Vertex AI Python SDK\n",
    "2. Write the ML training code to a file\n",
    "3. Configure and submit a training job that runs our training code."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff5628f8",
   "metadata": {},
   "source": [
    "* Step 1. Initialize Vertex AI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6aa103e6-91f0-4506-b444-7af210ae0b05",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "from helper import authenticate\n",
    "CREDENTIALS, PROJECT_ID = authenticate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "165068fa-747a-4a43-aaae-24599ec361c3",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "from google.cloud import aiplatform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "95d98265-e5e2-4adf-9ef3-b7ab5e4dd7f7",
   "metadata": {
    "height": 64
   },
   "outputs": [],
   "source": [
    "aiplatform.init(project=PROJECT_ID,\n",
    "                credentials=CREDENTIALS,\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2205640c",
   "metadata": {},
   "source": [
    "* Step 2. Write the ML training code to a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e6c4ad22-f843-497a-8efe-03780857be08",
   "metadata": {
    "height": 676
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting task.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile task.py\n",
    "\n",
    "# import libraries\n",
    "import numpy as np\n",
    "from sklearn.datasets import make_blobs\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "# Create dataset\n",
    "classes = 4\n",
    "m = 100\n",
    "centers = [[-5, 2], [-2, -2], [1, 2], [5, -2]]\n",
    "std = 1.0\n",
    "X_train, y_train = make_blobs(\n",
    "    n_samples=m, \n",
    "    centers=centers, \n",
    "    cluster_std=std,\n",
    "    random_state=30)\n",
    "\n",
    "# Create the model\n",
    "model = Sequential(\n",
    "    [\n",
    "        Dense(2, activation = 'relu',   name = \"L1\"),\n",
    "        Dense(4, activation = 'linear', name = \"L2\")\n",
    "    ]\n",
    ")\n",
    "\n",
    "model.compile(\n",
    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(\n",
    "        from_logits=True),\n",
    "    optimizer=tf.keras.optimizers.Adam(0.01),\n",
    ")\n",
    "\n",
    "# Train\n",
    "model.fit(\n",
    "    X_train,y_train,\n",
    "    epochs=200\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "616ceab3-02f3-49f5-88bd-b560a4a3908d",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L3_Training_Models.ipynb  \u001b[0m\u001b[01;34m__pycache__\u001b[0m/  helper.py  task.py\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff21b5e4-5439-4721-a28b-109fc7230cf7",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "# import libraries\r\n",
      "import numpy as np\r\n",
      "from sklearn.datasets import make_blobs\r\n",
      "import tensorflow as tf\r\n",
      "from tensorflow.keras.models import Sequential\r\n",
      "from tensorflow.keras.layers import Dense\r\n",
      "\r\n",
      "# Create dataset\r\n",
      "classes = 4\r\n",
      "m = 100\r\n",
      "centers = [[-5, 2], [-2, -2], [1, 2], [5, -2]]\r\n",
      "std = 1.0\r\n",
      "X_train, y_train = make_blobs(\r\n",
      "    n_samples=m, \r\n",
      "    centers=centers, \r\n",
      "    cluster_std=std,\r\n",
      "    random_state=30)\r\n",
      "\r\n",
      "# Create the model\r\n",
      "model = Sequential(\r\n",
      "    [\r\n",
      "        Dense(2, activation = 'relu',   name = \"L1\"),\r\n",
      "        Dense(4, activation = 'linear', name = \"L2\")\r\n",
      "    ]\r\n",
      ")\r\n",
      "\r\n",
      "model.compile(\r\n",
      "    loss=tf.keras.losses.SparseCategoricalCrossentropy(\r\n",
      "        from_logits=True),\r\n",
      "    optimizer=tf.keras.optimizers.Adam(0.01),\r\n",
      ")\r\n",
      "\r\n",
      "# Train\r\n",
      "model.fit(\r\n",
      "    X_train,y_train,\r\n",
      "    epochs=200\r\n",
      ")\r\n"
     ]
    }
   ],
   "source": [
    "cat task.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2bfa47c",
   "metadata": {},
   "source": [
    "* Define the Custom Trainin Job."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fbb742d",
   "metadata": {},
   "source": [
    "> Note: Check here to find more information about [Prebuilt containers for custom training](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers)\n",
    "\n",
    "> Note: Check here to find more information about [Carbon free energy for Google Cloud regions](https://cloud.google.com/sustainability/region-carbon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "06e502c7-eda6-4c10-978d-aa730731dbb7",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "# Choose a location (with carbon free energy available for this example)\n",
    "REGION = 'us-central1'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86977ad6",
   "metadata": {},
   "source": [
    "* Create a store bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa25f631-175d-4700-bc30-70db90cb6bcb",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "58367b95-19e4-4e8b-a2c3-a1b0a256643e",
   "metadata": {
    "height": 132
   },
   "outputs": [],
   "source": [
    "def generate_uuid(length: int = 8) -> str:\n",
    "    return \"\".join(\n",
    "        random.choices(\n",
    "            string.ascii_lowercase + string.digits, \n",
    "            k=length))\n",
    "\n",
    "UUID = generate_uuid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2380693a-7167-4855-b516-e2837b4b9a97",
   "metadata": {
    "height": 47
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'31gc4sai'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The unique identifier\n",
    "UUID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "489859f0-5299-43a2-a718-6637dc37b41a",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "from google.cloud import storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "986c8fd6-6558-408e-bd04-74b301842acb",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "storage_client = storage.Client(project = PROJECT_ID,\n",
    "                                credentials = CREDENTIALS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cd33c614-ff8a-4acc-b8e1-5f7a530ab565",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "bucket_name = f'carbon-course-bucket-{UUID}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aa46eac4-6009-4489-bc6e-ed18b2db7dff",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'carbon-course-bucket-31gc4sai'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucket_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b9377f73-662c-45fb-ada9-0b6152b7fb40",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "bucket = storage_client.bucket(bucket_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "424eb498-ecc1-43d7-8e2d-812bf272ac73",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "bucket.create(location=REGION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "67cc2bb0-f678-4c5d-bf75-1b57911397cd",
   "metadata": {
    "height": 149
   },
   "outputs": [],
   "source": [
    "# Create the CustomTrainingJob\n",
    "job = aiplatform.CustomTrainingJob(\n",
    "    display_name='dlai-course-example',\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",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1cc56b5d-4108-4592-a580-bc0e587f0ce7",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training script copied to:\n",
      "gs://carbon-course-bucket-31gc4sai/aiplatform-2024-09-03-15-00-23.989-aiplatform_custom_trainer_script-0.1.tar.gz.\n",
      "Training Output directory:\n",
      "gs://carbon-course-bucket-31gc4sai/aiplatform-custom-training-2024-09-03-15-00-23.989\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": [
    "model = job.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4d65abae-b35b-4818-853a-4c68a44587f8",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "# Delete the bucket to clean up resources\n",
    "bucket.delete(force=True)"
   ]
  }
 ],
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