Addressed TW comments

Update dashboard instructions

.cloud-build/requirements.txt

@@ -11,3 +11,4 @@ google-cloud-storage
 google-cloud-build
 ratemate
 GitPython
+tqdm

notebooks/notebook_template_review.py

@@ -1153,7 +1153,7 @@ def add_index(path: str,
                 if linkbacks[_].startswith("vertex-ai"):
                     print(f' Learn more about <a href="https://cloud.google.com/{linkbacks[_]}" target="_blank">{replace_cl(tags[_])}</a>.\n')
                 else:
-                    print(f' Learn more about <a href="{linkbacks[_]}." target="_blank">{replace_cl(tags[_])}</a>.\n')
+                    print(f' Learn more about <a href="{linkbacks[_]}" target="_blank">{replace_cl(tags[_])}</a>.\n')
                     
         if args.steps:
             print("<devsite-expandable>\n")
@@ -1240,6 +1240,8 @@ def replace_cl(text : str ) -> str:
         'Vertex AI Prediction': '{{vertex_prediction_name}}',
         'Vertex TensorBoard': '{{vertex_tensorboard_name}}',
         'Vertex AI TensorBoard': '{{vertex_tensorboard_name}}',
+        'TensorBoard': '{{vertex_tensorboard_name}}',
+        'Tensorboard': '{{vertex_tensorboard_name}}',
         'Vertex ML Metadata': '{{vertex_metadata_name}}',
         'Vertex Pipelines': '{{vertex_pipelines_name}}',
         'Vertex AI Pipelines': '{{vertex_pipelines_name}}',
@@ -1265,6 +1267,8 @@ def replace_cl(text : str ) -> str:
         'Vertex AI': '{{vertex_ai_name}}',
         
         'Cloud Storage': '{{storage_name}}',
+        'GCS': '{{storage_name}}',
+        'GCP': '{{gcp_name}}',
         'TensorFlow Enterprise': '{{tf4gcp_name}}',
         'TensorFlow': '{{tensorflow_name}}',
     }

notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb

@@ -147,8 +147,7 @@
         "# Install the packages\n",
         "! pip3 install --upgrade google-cloud-aiplatform \\\n",
         "                        google-cloud-storage \\\n",
-        "                        google-cloud-bigquery \\\n",
-        "                        pyarrow"
+        "                        'google-cloud-bigquery[pandas]'"
       ]
     },
     {
@@ -632,12 +631,12 @@
         "    df_train_x, df_train_y = df_train, df_train.pop(LABEL_COLUMN)\n",
         "    df_validation_x, df_validation_y = df_validation, df_validation.pop(LABEL_COLUMN)\n",
         "\n",
-        "    y_train = np.asarray(df_train_y).astype(\"float32\")\n",
-        "    y_validation = np.asarray(df_validation_y).astype(\"float32\")\n",
+        "    y_train = tf.convert_to_tensor(np.asarray(df_train_y).astype(\"float32\"))\n",
+        "    y_validation = tf.convert_to_tensor(np.asarray(df_validation_y).astype(\"float32\"))\n",
         "\n",
         "    # Convert to numpy representation\n",
-        "    x_train = np.asarray(df_train_x) \n",
-        "    x_test = np.asarray(df_validation_x)\n",
+        "    x_train = tf.convert_to_tensor(np.asarray(df_train_x).astype(\"float32\"))\n",
+        "    x_test = tf.convert_to_tensor(np.asarray(df_validation_x).astype(\"float32\"))\n",
         "\n",
         "    # Convert to one-hot representation\n",
         "    num_species = len(df_train_y.unique())\n",
@@ -735,7 +734,7 @@
         "    display_name=JOB_NAME,\n",
         "    script_path=\"task.py\",\n",
         "    container_uri=\"us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-8:latest\",\n",
-        "    requirements=[\"google-cloud-bigquery>=2.20.0\", \"db-dtypes\"],\n",
+        "    requirements=[\"google-cloud-bigquery[pandas]\", \"protobuf<3.20.0\"],\n",
         "    model_serving_container_image_uri=\"us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest\",\n",
         ")\n",
         "\n",

notebooks/official/explainable_ai/sdk_automl_tabular_classification_online_explain.ipynb

@@ -64,7 +64,7 @@
         "\n",
         "This tutorial demonstrates how to use the Vertex AI SDK to create tabular classification models and do online prediction with explanation using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
         "\n",
-        "Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/tabular-data/overview) and [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview)."
+        "Learn more about [Classification for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview) and [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview)."
       ]
     },
     {

notebooks/official/migration/UJ14 Vertex SDK AutoML Video Classification.ipynb

@@ -29,7 +29,7 @@
         "id": "title:migration,new"
       },
       "source": [
-        "# Vertex AI: Vertex AI Migration: AutoML Video Classificaton\n",
+        "# Vertex AI: Vertex AI Migration: AutoML Video Classification\n",
         "\n",
         "<table align=\"left\">\n",
         "\n",
@@ -64,7 +64,7 @@
         "\n",
         "This tutorial demonstrates how to use the Vertex AI SDK for Python to train a AutoML video classification model and do a batch prediction.\n",
         "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [AutoML Video](https://cloud.google.com/vertex-ai/docs/tutorials/video-classification-automl/training)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Classification for video data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_videos)."
       ]
     },
     {

notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb

@@ -140,7 +140,7 @@
       "outputs": [],
       "source": [
         "! pip install --upgrade -q google-cloud-aiplatform \\\n",
-        "                            tensorflow==2.8 -q"
+        "                            tensorflow -q"
       ]
     },
     {

notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb

@@ -92,7 +92,7 @@
         "- Train a Automl Video Classification model on the `Vertex AI Dataset` resource.\n",
         "- Import the trained `AutoML Vertex AI Model resource` into the pipeline.\n",
         "- Run a batch prediction job inside the pipeline.\n",
-        "- Evaulate the AutoML model using the classification evaluation component.\n",
+        "- Evaluate the AutoML model using the classification evaluation component.\n",
         "- Import the classification metrics to the AutoML Vertex AI Model resource."
       ]
     },

notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb

@@ -93,7 +93,7 @@
         "- Upload the model as a Vertex AI Model resource.\n",
         "- Import a pre-trained `Vertex AI model resource` into the pipeline.\n",
         "- Run a `batch prediction` job in the pipeline.\n",
-        "- Evaulate the model using the `regression evaluation component`.\n",
+        "- Evaluate the model using the `regression evaluation component`.\n",
         "- Import the Regression Metrics to the Vertex AI model resource."
       ]
     },

notebooks/official/tabnet/ai-explanations-tabnet-algorithm.ipynb

@@ -29,7 +29,7 @@
         "id": "JAPoU8Sm5E6e"
       },
       "source": [
-        "# Vertex AI Explainations with TabNet models\n",
+        "# Vertex AI Explanations with TabNet models\n",
         "\n",
         "<table align=\"left\">\n",
         "\n",
@@ -86,7 +86,7 @@
         "- TabNet builtin algorithm\n",
         "\n",
         "The steps performed are:\n",
-        "* Setup the the project.\n",
+        "* Setup the project.\n",
         "* Download the prediction data of pretrain model onf Syn2 data.\n",
         "* Visualize and understand the feature importance based on the masks output.\n",
         "* Clean up the resource created by this tutorial."

notebooks/official/training/xgboost_data_parallel_training_on_cpu_using_dask.ipynb

@@ -830,13 +830,22 @@
         "    !  gcloud builds submit --timeout=1800s --region={REGION} --tag $TRAIN_IMAGE"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "41ZgDYPNfEvt"
+      },
+      "source": [
+        "## Run training job with SDK (Option 1) or with gcloud (Option 2)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "PtUycdZhCJvQ"
       },
       "source": [
-        "### Initialize Vertex AI SDK"
+        "### 1.1 Initialize Vertex AI SDK"
       ]
     },
     {
@@ -860,7 +869,16 @@
         "id": "_GB2j39BCXiy"
       },
       "source": [
-        "### Run a Vertex AI SDK CustomContainerTrainingJob"
+        "### 1.2 Run a Vertex AI SDK CustomContainerTrainingJob"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7udaU3jxfKs8"
+      },
+      "source": [
+        "You can specify the fields enable_web_access and enable_dashboard_access. The enable_web_access enables the interactive shell for the job and enable_dashboard_access allows the dask dashboard to be accessed."
       ]
     },
     {
@@ -887,9 +905,34 @@
         "    base_output_dir=gcs_output_uri_prefix,\n",
         "    replica_count=replica_count,\n",
         "    machine_type=machine_type,\n",
+        "    enable_dashboard_access=True,\n",
+        "    enable_web_access=True,\n",
+        "    sync=False,\n",
         ")"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ROqdvgBKFGKL"
+      },
+      "source": [
+        "Wait for a few minutes for the Custom Job to start"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rBYbvim_FJz0"
+      },
+      "outputs": [],
+      "source": [
+        "import time\n",
+        "\n",
+        "time.sleep(60 * 3)"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -902,13 +945,66 @@
         "print(f\"GCS Output URI Prefix: {gcs_output_uri_prefix}\")"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-0EJdcjPyjDK"
+      },
+      "source": [
+        "You can access the link to the Custom Job in the Cloud Console UI here:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZMZPrcqsyj0e"
+      },
+      "outputs": [],
+      "source": [
+        "print(\n",
+        "    f\"Custom Training Job URI: {custom_container_training_job._custom_job_console_uri()}\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7McPKJGxymLU"
+      },
+      "source": [
+        "Once the job is in the state \"RUNNING\", you can access the web access and dashboard access URIs here:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tiHdwJs1yoE0"
+      },
+      "outputs": [],
+      "source": [
+        "print(f\"Web Access and Dashboard URIs: {custom_container_training_job.web_access_uris}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9RDh5CzNywIA"
+      },
+      "source": [
+        "The interactive shell has the key with the format \"workerpool0-0\", while the dashboard uri has the key with the format \"workerpool0-0:\" + port number (workerpool0-0:8888 in this example). On the page for your Custom Job in the Cloud Console UI, you can also \"Launch web terminal\" for \"workerpool0-0\" for web access, or click \"Launch web terminal\" for \"workerpool0-0:\" + port number for dashboard access.\n",
+        "\n",
+        "Note that you can only access an interactive shell and dashboard while the job is running. If you don't see Launch web terminal in the UI or the URIs in the output of the Web Access and Dashboard URIs command, this might be because Vertex AI hasn't started running your job yet, or because the job has already finished or failed. If the job's Status is Queued or Pending, wait a minute; then try refreshing the page, or trying the command again."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "tVktIbToRpmR"
       },
       "source": [
-        "### Access the Dask dashboard"
+        "### 2. Run a CustomContainerTraining Job with gcloud"
       ]
     },
     {
@@ -917,7 +1013,7 @@
         "id": "uVvxLj8GRsM6"
       },
       "source": [
-        "You can also create a training job with gcloud command.  With gcloud command, you can specify the field enableWebAccess and enableDashboardAccess. enableWebAccess enables the interactive shell for the job and enableDashboardAccess allows the dask dashboard to be accessed."
+        "You can also create a training job with the gcloud command.  With the gcloud command, you can specify the field enableWebAccess and enableDashboardAccess. The enableWebAccess enables the interactive shell for the job and enableDashboardAccess allows the dask dashboard to be accessed."
       ]
     },
     {
@@ -975,10 +1071,16 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "onb40Ge0SVKh"
+        "id": "HAYihHT9ff9q"
       },
       "source": [
-        "Once the job is created. You can use the output `gcloud ai custom-jobs describe` command to print the field webAccessUris. The interactive shell has the key with the format \"workerpool0-0\", while the dashboard uri has the key with the format \"workerpool0-0:\" + port number. Note: You have to access the links while the job is running."
+        "#### Access the dashboard and interactive shell for a gcloud custom job\n",
+        "\n",
+        "Once the job is created, you can access the web access URI and dashboard access URI by using the `gcloud ai custom-jobs describe` command to print the field webAccessUris. The interactive shell has the key with the format \"workerpool0-0\", while the dashboard uri has the key with the format \"workerpool0-0:\" + port number (workerpool0-0:8888 in this example).\n",
+        "\n",
+        "You also can find the links in the Cloud Console UI. In the Cloud Console UI, in the Vertex AI section, go to Training and then Custom Jobs. Click on the name of your custom training job. On the page for your job, click \"Launch web terminal\" for \"workerpool0-0\" for web access, or click \"Launch web terminal\" for \"workerpool0-0:\" + port number for dashboard access.\n",
+        "\n",
+        "Note that you can only access an interactive shell and dashboard while the job is running. If you don't see Launch web terminal in the UI or the URIs in the output of the gcloud command, this might be because Vertex AI hasn't started running your job yet, or because the job has already finished or failed. If the job's Status is Queued or Pending, wait a minute; then try refreshing the page, or trying the gcloud command again."
       ]
     },
     {
@@ -1098,6 +1200,9 @@
       },
       "outputs": [],
       "source": [
+        "import logging\n",
+        "import traceback\n",
+        "\n",
         "# Set this to true only if you'd like to delete your bucket\n",
         "delete_bucket = False\n",
         "\n",
@@ -1106,7 +1211,11 @@
         "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
         "    ! gsutil rm -r $BUCKET_URI\n",
         "\n",
-        "custom_container_training_job.delete()"
+        "try:\n",
+        "    custom_container_training_job.delete()\n",
+        "except Exception as e:\n",
+        "    logging.error(traceback.format_exc())\n",
+        "    print(e)"
       ]
     }
   ],

notebooks/official/workbench/demand_forecasting/forecasting-retail-demand.ipynb

@@ -89,7 +89,7 @@
         "* Model with BigQuery and the ARIMA model\n",
         "* Evaluate the model\n",
         "* Evaluate the model results using BigQuery ML (on training data)\n",
-        "* Evalute the model results - MAE, MAPE, MSE, RMSE (on test data)\n",
+        "* Evaluate the model results - MAE, MAPE, MSE, RMSE (on test data)\n",
         "* Use the executor feature"
       ]
     },