Merge branch 'main' into ml_ops2_automl

notebooks/community/ml_ops/stage3/get_started_with_automl_pipeline_components.ipynb

@@ -40,7 +40,7 @@
         "  </td>\n",
         "  <td>\n",
         "    <a href=\"https://console.cloud.google.com/ai/platform/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_automl_pipeline_components.ipynb\">\n",
-        "      Open in Google Cloud Notebooks\n",
+        "      Open in Vertex AI Workbench\n",
         "    </a>\n",
         "  </td>\n",
         "</table>\n",
@@ -291,7 +291,8 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_NAME = \"gs://[your-bucket-name]\"  # @param {type:\"string\"}"
+        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
+        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
@@ -302,8 +303,8 @@
       },
       "outputs": [],
       "source": [
-        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
-        "    BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
+        "if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n",
+        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
       ]
     },
     {
@@ -323,7 +324,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
+        "! gsutil mb -l $REGION $BUCKET_URI"
       ]
     },
     {
@@ -343,7 +344,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
+        "! gsutil ls -al $BUCKET_URI"
       ]
     },
     {
@@ -406,9 +407,9 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectCreator $BUCKET_NAME\n",
+        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectCreator $BUCKET_URI\n",
         "\n",
-        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectViewer $BUCKET_NAME"
+        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectViewer $BUCKET_URI"
       ]
     },
     {
@@ -490,7 +491,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_NAME)"
+        "aip.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -612,9 +613,7 @@
       },
       "outputs": [],
       "source": [
-        "from google_cloud_pipeline_components import aiplatform as gcc_aip\n",
-        "\n",
-        "PIPELINE_ROOT = \"{}/pipeline_root/automl_icn_training\".format(BUCKET_NAME)\n",
+        "PIPELINE_ROOT = \"{}/pipeline_root/automl_icn_training\".format(BUCKET_URI)\n",
         "DEPLOY_COMPUTE = \"n1-standard-4\"\n",
         "\n",
         "\n",
@@ -629,6 +628,7 @@
         "    project: str = PROJECT_ID,\n",
         "    region: str = REGION,\n",
         "):\n",
+        "    from google_cloud_pipeline_components import aiplatform as gcc_aip\n",
         "\n",
         "    dataset_op = gcc_aip.ImageDatasetCreateOp(\n",
         "        project=project,\n",
@@ -677,6 +677,7 @@
         "        endpoint=endpoint_op.outputs[\"endpoint\"],\n",
         "        automatic_resources_min_replica_count=1,\n",
         "        automatic_resources_max_replica_count=1,\n",
+        "        traffic_split={\"0\": 100},\n",
         "    )"
       ]
     },
@@ -688,7 +689,7 @@
       "source": [
         "### Get test item(s)\n",
         "\n",
-        "Now do a batch prediction to your Vertex model. You will use arbitrary examples out of the dataset as a test items. Don't be concerned that the examples were likely used in training the model -- we just want to demonstrate how to make a prediction."
+        "In the pipeline, you do a batch prediction to your Vertex model. You will use arbitrary examples out of the dataset as a test items. Don't be concerned that the examples were likely used in training the model -- we just want to demonstrate how to make a prediction."
       ]
     },
     {
@@ -733,11 +734,11 @@
         "file_1 = test_item_1.split(\"/\")[-1]\n",
         "file_2 = test_item_2.split(\"/\")[-1]\n",
         "\n",
-        "! gsutil cp $test_item_1 $BUCKET_NAME/$file_1\n",
-        "! gsutil cp $test_item_2 $BUCKET_NAME/$file_2\n",
+        "! gsutil cp $test_item_1 $BUCKET_URI/$file_1\n",
+        "! gsutil cp $test_item_2 $BUCKET_URI/$file_2\n",
         "\n",
-        "test_item_1 = BUCKET_NAME + \"/\" + file_1\n",
-        "test_item_2 = BUCKET_NAME + \"/\" + file_2"
+        "test_item_1 = BUCKET_URI + \"/\" + file_1\n",
+        "test_item_2 = BUCKET_URI + \"/\" + file_2"
       ]
     },
     {
@@ -770,7 +771,7 @@
         "\n",
         "import tensorflow as tf\n",
         "\n",
-        "gcs_input_uri = BUCKET_NAME + \"/test.jsonl\"\n",
+        "gcs_input_uri = BUCKET_URI + \"/test.jsonl\"\n",
         "with tf.io.gfile.GFile(gcs_input_uri, \"w\") as f:\n",
         "    data = {\"content\": test_item_1, \"mime_type\": \"image/jpeg\"}\n",
         "    f.write(json.dumps(data) + \"\\n\")\n",
@@ -911,9 +912,21 @@
         "print(\"automl-image-training-job\")\n",
         "artifacts = print_pipeline_output(pipeline, \"automl-image-training-job\")\n",
         "print(\"\\n\\n\")\n",
+        "output = !gsutil cat $artifacts\n",
+        "output = json.loads(output[0])\n",
+        "model_id = output[\"artifacts\"][\"model\"][\"artifacts\"][0][\"metadata\"][\"resourceName\"]\n",
+        "print(\"\\n\")\n",
+        "print(endpoint_id)\n",
         "print(\"endpoint-create\")\n",
         "artifacts = print_pipeline_output(pipeline, \"endpoint-create\")\n",
         "print(\"\\n\\n\")\n",
+        "output = !gsutil cat $artifacts\n",
+        "output = json.loads(output[0])\n",
+        "endpoint_id = output[\"artifacts\"][\"endpoint\"][\"artifacts\"][0][\"metadata\"][\n",
+        "    \"resourceName\"\n",
+        "]\n",
+        "print(\"\\n\")\n",
+        "print(endpoint_id)\n",
         "print(\"model-deploy\")\n",
         "artifacts = print_pipeline_output(pipeline, \"model-deploy\")\n",
         "print(\"\\n\\n\")\n",
@@ -929,7 +942,12 @@
         "    output[\"artifacts\"][\"batchpredictionjob\"][\"artifacts\"][0][\"metadata\"][\n",
         "        \"gcsOutputDirectory\"\n",
         "    ]\n",
-        ")"
+        ")\n",
+        "output = !gsutil cat $artifacts\n",
+        "output = json.loads(output[0])\n",
+        "batch_job_id = output[\"artifacts\"][\"batchpredictionjob\"][\"artifacts\"][0][\"metadata\"][\n",
+        "    \"resourceName\"\n",
+        "]"
       ]
     },
     {
@@ -954,6 +972,149 @@
         "pipeline.delete()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "endpoint_load:mbsdk"
+      },
+      "source": [
+        "#### Load an endpoint\n",
+        "\n",
+        "The 'Endpoint' initializer will load an endpoint from an endpoint identifier."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "endpoint_load:mbsdk"
+      },
+      "outputs": [],
+      "source": [
+        "endpoint = aip.Endpoint(endpoint_id)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "make_prediction"
+      },
+      "source": [
+        "## Send a online prediction request\n",
+        "\n",
+        "Send a online prediction to your deployed model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "get_test_item"
+      },
+      "source": [
+        "### Get test item\n",
+        "\n",
+        "You will use an arbitrary example out of the dataset as a test item. Don't be concerned that the example was likely used in training the model -- we just want to demonstrate how to make a prediction."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "get_test_item:automl,icn,csv"
+      },
+      "outputs": [],
+      "source": [
+        "test_item = !gsutil cat $IMPORT_FILE | head -n1\n",
+        "if len(str(test_item[0]).split(\",\")) == 3:\n",
+        "    _, test_item, test_label = str(test_item[0]).split(\",\")\n",
+        "else:\n",
+        "    test_item, test_label = str(test_item[0]).split(\",\")\n",
+        "\n",
+        "print(test_item, test_label)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "predict_request:mbsdk,icn"
+      },
+      "source": [
+        "### Make the prediction\n",
+        "\n",
+        "Now that your `Model` resource is deployed to an `Endpoint` resource, you can do online predictions by sending prediction requests to the Endpoint resource.\n",
+        "\n",
+        "#### Request\n",
+        "\n",
+        "Since in this example your test item is in a Cloud Storage bucket, you open and read the contents of the image using `tf.io.gfile.Gfile()`. To pass the test data to the prediction service, you encode the bytes into base64 -- which makes the content safe from modification while transmitting binary data over the network.\n",
+        "\n",
+        "The format of each instance is:\n",
+        "\n",
+        "    { 'content': { 'b64': base64_encoded_bytes } }\n",
+        "\n",
+        "Since the `predict()` method can take multiple items (instances), send your single test item as a list of one test item.\n",
+        "\n",
+        "#### Response\n",
+        "\n",
+        "The response from the `predict()` call is a Python dictionary with the following entries:\n",
+        "\n",
+        "- `ids`: The internal assigned unique identifiers for each prediction request.\n",
+        "- `displayNames`: The class names for each class label.\n",
+        "- `confidences`: The predicted confidence, between 0 and 1, per class label.\n",
+        "- `deployed_model_id`: The Vertex AI identifier for the deployed Model resource which did the predictions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "predict_request:mbsdk,icn"
+      },
+      "outputs": [],
+      "source": [
+        "import base64\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "with tf.io.gfile.GFile(test_item, \"rb\") as f:\n",
+        "    content = f.read()\n",
+        "\n",
+        "# The format of each instance should conform to the deployed model's prediction input schema.\n",
+        "instances = [{\"content\": base64.b64encode(content).decode(\"utf-8\")}]\n",
+        "\n",
+        "prediction = endpoint.predict(instances=instances)\n",
+        "\n",
+        "print(prediction)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9d347472d5ba"
+      },
+      "source": [
+        "#### Delete the Vertex AI Model, Endpoint and BatchPredictionJob resources\n",
+        "\n",
+        "Next, undelpoy and delete the Vertex AI Model, Endpoint and BatchPredictionJob resources."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "baa3e1071f7b"
+      },
+      "outputs": [],
+      "source": [
+        "endpoint.undeploy_all()\n",
+        "endpoint.delete()\n",
+        "\n",
+        "model = aip.Model(model_id)\n",
+        "model.delete()\n",
+        "\n",
+        "batch_job = aip.BatchPredictionJob(batch_job_id)\n",
+        "batch_job.delete()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -965,17 +1126,7 @@
         "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
         "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
         "\n",
-        "Otherwise, you can delete the individual resources you created in this tutorial:\n",
-        "\n",
-        "- Dataset\n",
-        "- Pipeline\n",
-        "- Model\n",
-        "- Endpoint\n",
-        "- AutoML Training Job\n",
-        "- Batch Job\n",
-        "- Custom Job\n",
-        "- Hyperparameter Tuning Job\n",
-        "- Cloud Storage Bucket"
+        "Otherwise, you can delete the individual resources you created in this tutorial."
       ]
     },
     {
@@ -989,58 +1140,9 @@
         "delete_all = True\n",
         "\n",
         "if delete_all:\n",
-        "    # Delete the dataset using the Vertex dataset object\n",
-        "    try:\n",
-        "        if \"dataset\" in globals():\n",
-        "            dataset.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
         "\n",
-        "    # Delete the model using the Vertex model object\n",
-        "    try:\n",
-        "        if \"model\" in globals():\n",
-        "            model.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    # Delete the endpoint using the Vertex endpoint object\n",
-        "    try:\n",
-        "        if \"endpoint\" in globals():\n",
-        "            endpoint.undeploy_all()\n",
-        "            endpoint.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    # Delete the AutoML or Pipeline training job\n",
-        "    try:\n",
-        "        if \"dag\" in globals():\n",
-        "            dag.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    # Delete the custom training job\n",
-        "    try:\n",
-        "        if \"job\" in globals():\n",
-        "            job.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    # Delete the batch prediction job using the Vertex batch prediction object\n",
-        "    try:\n",
-        "        if \"batch_predict_job\" in globals():\n",
-        "            batch_predict_job.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    # Delete the hyperparameter tuning job using the Vertex hyperparameter tuning object\n",
-        "    try:\n",
-        "        if \"hpt_job\" in globals():\n",
-        "            hpt_job.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    if \"BUCKET_NAME\" in globals():\n",
-        "        ! gsutil rm -r $BUCKET_NAME"
+        "    if \"BUCKET_URI\" in globals():\n",
+        "        ! gsutil rm -r $BUCKET_URI"
       ]
     }
   ],

notebooks/community/ml_ops/stage3/get_started_with_bqml_pipeline_components.ipynb

@@ -1128,12 +1128,12 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "74523c042a74"
+        "id": "e776f9a3bdc4"
       },
       "source": [
-        "#### Delete the Vertex model and endpoint\n",
+        "#### Delete the Vertex AI Model and Endpoint resources\n",
         "\n",
-        "Next, undelpoy and delete the Vertex Model and Endpoint resource."
+        "Next, undelpoy and delete the Vertex AI Model and Endpoint resources."
       ]
     },
     {

notebooks/community/ml_ops/stage3/get_started_with_custom_training_pipeline_components.ipynb

@@ -94,6 +94,12 @@
         "    - Training a Vertex AI custom trained model.\n",
         "    - Test the serving binary with a batch prediction job.\n",
         "    - Deploying a Vertex AI custom trained model.\n",
+        "- Execute a Vertex AI pipeline.\n",
+        "- Construct a pipeline for:\n",
+        "     - Construct a custom training component.\n",
+        "     - Convert custom training component to CustomTrainingJobOp.\n",
+        "     - Training a Vertex AI custom trained model using the converted component.\n",
+        "    - Deploying a Vertex AI custom trained model.\n",
         "- Execute a Vertex AI pipeline."
       ]
     },
@@ -291,7 +297,8 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_NAME = \"gs://[your-bucket-name]\"  # @param {type:\"string\"}"
+        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
+        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
@@ -302,8 +309,8 @@
       },
       "outputs": [],
       "source": [
-        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
-        "    BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
+        "if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n",
+        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
       ]
     },
     {
@@ -323,7 +330,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
+        "! gsutil mb -l $REGION $BUCKET_URI"
       ]
     },
     {
@@ -343,7 +350,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
+        "! gsutil ls -al $BUCKET_URI"
       ]
     },
     {
@@ -490,7 +497,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_NAME)"
+        "aip.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -1017,7 +1024,7 @@
         "! rm -f custom.tar custom.tar.gz\n",
         "! tar cvf custom.tar custom\n",
         "! gzip custom.tar\n",
-        "! gsutil cp custom.tar.gz $BUCKET_NAME/trainer_flowers.tar.gz"
+        "! gsutil cp custom.tar.gz $BUCKET_URI/trainer_flowers.tar.gz"
       ]
     },
     {
@@ -1077,7 +1084,7 @@
       },
       "outputs": [],
       "source": [
-        "PIPELINE_ROOT = \"{}/pipeline_root/custom_icn_training\".format(BUCKET_NAME)\n",
+        "PIPELINE_ROOT = \"{}/pipeline_root/custom_icn_training\".format(BUCKET_URI)\n",
         "DEPLOY_COMPUTE = \"n1-standard-4\"\n",
         "\n",
         "\n",
@@ -1210,11 +1217,11 @@
         "file_1 = test_item_1.split(\"/\")[-1]\n",
         "file_2 = test_item_2.split(\"/\")[-1]\n",
         "\n",
-        "! gsutil cp $test_item_1 $BUCKET_NAME/$file_1\n",
-        "! gsutil cp $test_item_2 $BUCKET_NAME/$file_2\n",
+        "! gsutil cp $test_item_1 $BUCKET_URI/$file_1\n",
+        "! gsutil cp $test_item_2 $BUCKET_URI/$file_2\n",
         "\n",
-        "test_item_1 = BUCKET_NAME + \"/\" + file_1\n",
-        "test_item_2 = BUCKET_NAME + \"/\" + file_2"
+        "test_item_1 = BUCKET_URI + \"/\" + file_1\n",
+        "test_item_2 = BUCKET_URI + \"/\" + file_2"
       ]
     },
     {
@@ -1247,7 +1254,7 @@
         "\n",
         "import tensorflow as tf\n",
         "\n",
-        "gcs_input_uri = BUCKET_NAME + \"/test.jsonl\"\n",
+        "gcs_input_uri = BUCKET_URI + \"/test.jsonl\"\n",
         "with tf.io.gfile.GFile(gcs_input_uri, \"w\") as f:\n",
         "    data = {\"content\": test_item_1, \"mime_type\": \"image/jpeg\"}\n",
         "    f.write(json.dumps(data) + \"\\n\")\n",
@@ -1297,7 +1304,7 @@
         "        \"import_file\": IMPORT_FILE,\n",
         "        \"batch_files\": [gcs_input_uri],\n",
         "        \"display_name\": \"flowers\" + TIMESTAMP,\n",
-        "        \"python_package\": f\"{BUCKET_NAME}/trainer_flowers.tar.gz\",\n",
+        "        \"python_package\": f\"{BUCKET_URI}/trainer_flowers.tar.gz\",\n",
         "        \"python_module\": \"trainer.task\",\n",
         "        \"project\": PROJECT_ID,\n",
         "        \"region\": REGION,\n",
@@ -1361,14 +1368,29 @@
         "            + str(TASK_ID)\n",
         "            + \"/gcp_resources\"\n",
         "        )\n",
+        "        EVAL_METRICS = (\n",
+        "            PIPELINE_ROOT\n",
+        "            + \"/\"\n",
+        "            + PROJECT_NUMBER\n",
+        "            + \"/\"\n",
+        "            + JOB_ID\n",
+        "            + \"/\"\n",
+        "            + output_task_name\n",
+        "            + \"_\"\n",
+        "            + str(TASK_ID)\n",
+        "            + \"/evaluation_metrics\"\n",
+        "        )\n",
         "        if tf.io.gfile.exists(EXECUTE_OUTPUT):\n",
         "            ! gsutil cat $EXECUTE_OUTPUT\n",
-        "            break\n",
+        "            return EXECUTE_OUTPUT\n",
         "        elif tf.io.gfile.exists(GCP_RESOURCES):\n",
         "            ! gsutil cat $GCP_RESOURCES\n",
-        "            break\n",
+        "            return GCP_RESOURCES\n",
+        "        elif tf.io.gfile.exists(EVAL_METRICS):\n",
+        "            ! gsutil cat $EVAL_METRICS\n",
+        "            return EVAL_METRICS\n",
         "\n",
-        "    return EXECUTE_OUTPUT\n",
+        "    return None\n",
         "\n",
         "\n",
         "print(\"image-dataset-create\")\n",
@@ -1377,9 +1399,18 @@
         "print(\"custompythonpackagetrainingjob-run\")\n",
         "artifacts = print_pipeline_output(pipeline, \"custompythonpackagetrainingjob-run\")\n",
         "print(\"\\n\\n\")\n",
+        "output = !gsutil cat $artifacts\n",
+        "output = json.loads(output[0])\n",
+        "model_id = output[\"artifacts\"][\"model\"][\"artifacts\"][0][\"metadata\"][\"resourceName\"]\n",
+        "print(\"\\n\\n\")\n",
         "print(\"endpoint-create\")\n",
         "artifacts = print_pipeline_output(pipeline, \"endpoint-create\")\n",
         "print(\"\\n\\n\")\n",
+        "output = !gsutil cat $artifacts\n",
+        "output = json.loads(output[0])\n",
+        "endpoint_id = output[\"artifacts\"][\"endpoint\"][\"artifacts\"][0][\"metadata\"][\n",
+        "    \"resourceName\"\n",
+        "]\n",
         "print(\"model-deploy\")\n",
         "artifacts = print_pipeline_output(pipeline, \"model-deploy\")\n",
         "print(\"\\n\\n\")\n",
@@ -1392,7 +1423,13 @@
         "    output[\"artifacts\"][\"batchpredictionjob\"][\"artifacts\"][0][\"metadata\"][\n",
         "        \"gcsOutputDirectory\"\n",
         "    ]\n",
-        ")"
+        ")\n",
+        "output = !gsutil cat $artifacts\n",
+        "output = json.loads(output[0])\n",
+        "batch_job_id = output[\"artifacts\"][\"batchpredictionjob\"][\"artifacts\"][0][\"metadata\"][\n",
+        "    \"resourceName\"\n",
+        "]\n",
+        "print(\"\\n\\n\")"
       ]
     },
     {
@@ -1417,6 +1454,42 @@
         "pipeline.delete()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d614c239d74c"
+      },
+      "source": [
+        "#### Delete the Vertex AI Model, Endpoint and BatchPredictionJob resources\n",
+        "\n",
+        "Next, undelpoy and delete the Vertex AI Model, Endpoint and BathPredictionJob resources."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "417791a1a7e2"
+      },
+      "outputs": [],
+      "source": [
+        "endpoint = aip.Endpoint(endpoint_id)\n",
+        "endpoint.undeploy_all()\n",
+        "try:\n",
+        "    endpoint.delete()\n",
+        "except:\n",
+        "    pass\n",
+        "\n",
+        "model = aip.Model(model_id)\n",
+        "try:\n",
+        "    model.delete()\n",
+        "except:\n",
+        "    pass\n",
+        "\n",
+        "batch_job = aip.BatchPredictionJob(batch_job_id)\n",
+        "batch_job.delete()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1616,9 +1689,7 @@
       },
       "outputs": [],
       "source": [
-        "from google_cloud_pipeline_components import aiplatform as gcc_aip\n",
-        "\n",
-        "PIPELINE_ROOT = \"{}/pipeline_root/custom_cifar10_training\".format(BUCKET_NAME)\n",
+        "PIPELINE_ROOT = \"{}/pipeline_root/custom_cifar10_training\".format(BUCKET_URI)\n",
         "\n",
         "\n",
         "@dsl.pipeline(name=\"custom-model-training-sample-pipeline\")\n",
@@ -1632,6 +1703,8 @@
         "    location: str = REGION,\n",
         "    deploy_image: str = \"us-docker.pkg.dev/cloud-aiplatform/prediction/tf2-cpu.2-3:latest\",\n",
         "):\n",
+        "    from google_cloud_pipeline_components import aiplatform as gcc_aip\n",
+        "\n",
         "    custom_job_op = custom_job_training_op(\n",
         "        model_dir=model_dir,\n",
         "        lr=lr,\n",
@@ -1735,46 +1808,6 @@
         "PROJECT_NUMBER = pipeline.gca_resource.name.split(\"/\")[1]\n",
         "print(PROJECT_NUMBER)\n",
         "\n",
-        "\n",
-        "def print_pipeline_output(job, output_task_name):\n",
-        "    JOB_ID = job.name\n",
-        "    print(JOB_ID)\n",
-        "    for _ in range(len(job.gca_resource.job_detail.task_details)):\n",
-        "        TASK_ID = job.gca_resource.job_detail.task_details[_].task_id\n",
-        "        EXECUTE_OUTPUT = (\n",
-        "            PIPELINE_ROOT\n",
-        "            + \"/\"\n",
-        "            + PROJECT_NUMBER\n",
-        "            + \"/\"\n",
-        "            + JOB_ID\n",
-        "            + \"/\"\n",
-        "            + output_task_name\n",
-        "            + \"_\"\n",
-        "            + str(TASK_ID)\n",
-        "            + \"/executor_output.json\"\n",
-        "        )\n",
-        "        GCP_RESOURCES = (\n",
-        "            PIPELINE_ROOT\n",
-        "            + \"/\"\n",
-        "            + PROJECT_NUMBER\n",
-        "            + \"/\"\n",
-        "            + JOB_ID\n",
-        "            + \"/\"\n",
-        "            + output_task_name\n",
-        "            + \"_\"\n",
-        "            + str(TASK_ID)\n",
-        "            + \"/gcp_resources\"\n",
-        "        )\n",
-        "        if tf.io.gfile.exists(EXECUTE_OUTPUT):\n",
-        "            ! gsutil cat $EXECUTE_OUTPUT\n",
-        "            break\n",
-        "        elif tf.io.gfile.exists(GCP_RESOURCES):\n",
-        "            ! gsutil cat $GCP_RESOURCES\n",
-        "            break\n",
-        "\n",
-        "    return EXECUTE_OUTPUT\n",
-        "\n",
-        "\n",
         "print(\"custom-train-model\")\n",
         "artifacts = print_pipeline_output(pipeline, \"custom-train-model\")\n",
         "print(\"\\n\\n\")\n",
@@ -1784,9 +1817,17 @@
         "print(\"model-upload\")\n",
         "artifacts = print_pipeline_output(pipeline, \"model-upload\")\n",
         "print(\"\\n\\n\")\n",
+        "output = !gsutil cat $artifacts\n",
+        "output = json.loads(output[0])\n",
+        "model_id = output[\"artifacts\"][\"model\"][\"artifacts\"][0][\"metadata\"][\"resourceName\"]\n",
         "print(\"endpoint-create\")\n",
         "artifacts = print_pipeline_output(pipeline, \"endpoint-create\")\n",
         "print(\"\\n\\n\")\n",
+        "output = !gsutil cat $artifacts\n",
+        "output = json.loads(output[0])\n",
+        "endpoint_id = output[\"artifacts\"][\"endpoint\"][\"artifacts\"][0][\"metadata\"][\n",
+        "    \"resourceName\"\n",
+        "]\n",
         "print(\"model-deploy\")\n",
         "artifacts = print_pipeline_output(pipeline, \"model-deploy\")\n",
         "print(\"\\n\\n\")"
@@ -1814,6 +1855,49 @@
         "pipeline.delete()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "93e69fc8b9e3"
+      },
+      "source": [
+        "#### Delete the Vertex AI Model and Endpoint resource\n",
+        "\n",
+        "Next, undelpoy and delete the Vertex AI Model and Endpoint resources."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c9d18ae084b1"
+      },
+      "source": [
+        "#### Delete the Vertex model and endpoint\n",
+        "\n",
+        "Next, undelpoy and delete the Vertex Model and Endpoint resource."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dccf71121d0b"
+      },
+      "outputs": [],
+      "source": [
+        "endpoint.undeploy_all()\n",
+        "try:\n",
+        "    endpoint.delete()\n",
+        "except:\n",
+        "    pass\n",
+        "\n",
+        "model = aip.Model(model_id)\n",
+        "try:\n",
+        "    model.delete()\n",
+        "except:\n",
+        "    pass"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1825,17 +1909,7 @@
         "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
         "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
         "\n",
-        "Otherwise, you can delete the individual resources you created in this tutorial:\n",
-        "\n",
-        "- Dataset\n",
-        "- Pipeline\n",
-        "- Model\n",
-        "- Endpoint\n",
-        "- AutoML Training Job\n",
-        "- Batch Job\n",
-        "- Custom Job\n",
-        "- Hyperparameter Tuning Job\n",
-        "- Cloud Storage Bucket"
+        "Otherwise, you can delete the individual resources you created in this tutorial."
       ]
     },
     {
@@ -1849,58 +1923,8 @@
         "delete_all = True\n",
         "\n",
         "if delete_all:\n",
-        "    # Delete the dataset using the Vertex dataset object\n",
-        "    try:\n",
-        "        if \"dataset\" in globals():\n",
-        "            dataset.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    # Delete the model using the Vertex model object\n",
-        "    try:\n",
-        "        if \"model\" in globals():\n",
-        "            model.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    # Delete the endpoint using the Vertex endpoint object\n",
-        "    try:\n",
-        "        if \"endpoint\" in globals():\n",
-        "            endpoint.undeploy_all()\n",
-        "            endpoint.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    # Delete the AutoML or Pipeline training job\n",
-        "    try:\n",
-        "        if \"dag\" in globals():\n",
-        "            dag.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    # Delete the custom training job\n",
-        "    try:\n",
-        "        if \"job\" in globals():\n",
-        "            job.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    # Delete the batch prediction job using the Vertex batch prediction object\n",
-        "    try:\n",
-        "        if \"batch_predict_job\" in globals():\n",
-        "            batch_predict_job.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    # Delete the hyperparameter tuning job using the Vertex hyperparameter tuning object\n",
-        "    try:\n",
-        "        if \"hpt_job\" in globals():\n",
-        "            hpt_job.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    if \"BUCKET_NAME\" in globals():\n",
-        "        ! gsutil rm -r $BUCKET_NAME"
+        "    if \"BUCKET_URI\" in globals():\n",
+        "        ! gsutil rm -r $BUCKET_URI"
       ]
     }
   ],