debug: internal error

notebooks/community/model_garden/model_garden_keras_stable_diffusion.ipynb

@@ -44,7 +44,7 @@
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
-        "  <td>\n",
+        "  <td>                                                                                               <td>\n",
         "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_keras_stable_diffusion.ipynb\">\n",
         "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
         "Open in Vertex AI Workbench\n",
@@ -282,11 +282,8 @@
         "from io import BytesIO\n",
         "\n",
         "import matplotlib.pyplot as plt\n",
-        "from google.cloud import storage\n",
         "from PIL import Image\n",
         "\n",
-        "GCS_URI_PREFIX = \"gs://\"\n",
-        "\n",
         "# Training constants.\n",
         "TRAINING_JOB_PREFIX = \"train\"\n",
         "TRAIN_CONTAINER_URI = \"us-docker.pkg.dev/vertex-ai-restricted/vertex-vision-model-garden-dockers/keras-train:latest\"\n",
@@ -320,21 +317,6 @@
         "    return gcs_path\n",
         "\n",
         "\n",
-        "def download_gcs_file_to_local(gcs_uri: str, local_path: str):\n",
-        "    \"\"\"Download a gcs file to a local path.\n",
-        "\n",
-        "    Args:\n",
-        "      gcs_uri: A string of file path on GCS.\n",
-        "      local_path: A string of local file path.\n",
-        "    \"\"\"\n",
-        "    if not gcs_uri.startswith(GCS_URI_PREFIX):\n",
-        "        raise ValueError(f\"{gcs_uri} is not a GCS path starting with {GCS_URI_PREFIX}.\")\n",
-        "    client = storage.Client()\n",
-        "    os.makedirs(os.path.dirname(local_path), exist_ok=True)\n",
-        "    with open(local_path, \"wb\") as f:\n",
-        "        client.download_blob_to_file(gcs_uri, f)\n",
-        "\n",
-        "\n",
         "def deploy_model(model_path, service_account):\n",
         "\n",
         "    deploy_model_name = get_job_name_with_datetime(DEPLOY_JOB_PREFIX)\n",
@@ -438,11 +420,7 @@
         "from keras_cv.models import StableDiffusion\n",
         "\n",
         "model = StableDiffusion(img_height=RESOLUTION, img_width=RESOLUTION, jit_compile=True)\n",
-        "if model_path.startswith(GCS_URI_PREFIX):\n",
-        "    local_model_path = \"/tmp/saved_model.h5\"\n",
-        "    download_gcs_file_to_local(model_path, local_model_path)\n",
-        "    model.diffusion_model.load_weights(local_model_path)\n",
-        "elif model_path:\n",
+        "if model_path:\n",
         "    model.diffusion_model.load_weights(model_path)"
       ]
     },
@@ -590,7 +568,7 @@
       },
       "source": [
         "## Finetune models\n",
-        "This section shows how to finetune Keras Stable diffusion models with training dockers.\n",
+        "This section shows how to finetune Keras Stable diffusion models with trainig dockers.\n",
         "\n",
         "If you would like to use finetuned models, please go to the section `Run inferences`."
       ]

notebooks/community/model_garden/model_garden_proprietary_image_classification.ipynb

@@ -389,6 +389,7 @@
         "- `model_type`: The type of model for deployment.\n",
         "  - `EFFICIENTNET`: A model that is available in Vertex Model Garden image classification training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.\n",
         "  - `MAXVIT`: A model that is available in Vertex Model Garden image classification training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.\n",
+        "  - `COCA`: A model that is available in Vertex Model Garden image classification training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.\n",
         "- `checkpoint_name`: Optional. The field is reserved for Model Garden model training, based on the provided pre-trained model checkpoint.\n",
         "- `trainer_config`: Optional. The field is usually used together with the Model Garden model training when passing the customized configs for the trainer.\n",
         "\n",
@@ -456,7 +457,7 @@
         "METRIC_SPEC_VALUE = \"maximize\"\n",
         "SEARCH_ALGORITHM = \"random\"\n",
         "MEASUREMENT_SELECTION = \"best\"\n",
-        "MODEL_TYPE = \"MAXVIT\"  # @param {type:\"string\"} one of the values [\"MAXVIT\", \"EFFICIENTNET\"]\n",
+        "MODEL_TYPE = \"COCA\"  # @param {type:\"string\"} one of the values [\"COCA\", \"MAXVIT\", \"EFFICIENTNET\"]\n",
         "\n",
         "job = aiplatform.AutoMLImageTrainingJob(\n",
         "    display_name=get_job_name_with_datetime(TRAINING_JOB_PREFIX),\n",

notebooks/official/automl/automl_forecasting_bqml_arima_plus_comparison.ipynb

@@ -88,8 +88,8 @@
         "- Make a batch prediction with the BigQuery ML model.\n",
         "- Create a Vertex AI `Dataset` resource.\n",
         "- Train the Vertex AI Forecasting model.\n",
-        "- View the Model evaluation.\n",
-        "- Make a batch prediction with the Model.\n"
+        "- View the Vertex AI Model Evaluation results.\n",
+        "- Make a batch prediction with the Vertex AI Forecasting model.\n"
       ]
     },
     {

notebooks/official/prediction/custom_batch_prediction_feature_filter.ipynb

@@ -154,11 +154,10 @@
       "outputs": [],
       "source": [
         "# Install the packages\n",
-        "! pip3 install --upgrade --quiet google-cloud-aiplatform \\\n",
-        "                                 google-cloud-storage \\\n",
-        "                                 google-cloud-bigquery \\\n",
-        "                                 pyarrow \\\n",
-        "                                 db-dtypes\n"
+        "! pip3 install --upgrade google-cloud-aiplatform \\\n",
+        "                                    google-cloud-storage \\\n",
+        "                                    google-cloud-bigquery \\\n",
+        "                                    pyarrow -q"
       ]
     },
     {
@@ -353,7 +352,7 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
+        "BUCKET_URI = \"gs://your-bucket-name-unique\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -373,7 +372,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l {REGION} -p {PROJECT_ID} {BUCKET_URI}"
+        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -1017,7 +1016,7 @@
         "    display_name=JOB_NAME,\n",
         "    script_path=\"task.py\",\n",
         "    container_uri=TRAIN_IMAGE,\n",
-        "    requirements=[\"google-cloud-bigquery>=2.20.0\", \"db-dtypes\", \"protobuf==3.20.3\"],\n",
+        "    requirements=[\"google-cloud-bigquery>=2.20.0\", \"db-dtypes\"],\n",
         "    model_serving_container_image_uri=DEPLOY_IMAGE,\n",
         ")\n",
         "\n",
@@ -1440,7 +1439,12 @@
         "\n",
         "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud 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 notebook."
+        "Otherwise, you can delete the individual resources you created in this notebook:\n",
+        "\n",
+        "- Training Job\n",
+        "- Model\n",
+        "- Cloud Storage Bucket\n",
+        "- BigQuery Dataset"
       ]
     },
     {
@@ -1457,9 +1461,6 @@
         "# Delete the training job\n",
         "job.delete()\n",
         "\n",
-        "# Delete the dataset\n",
-        "dataset.delete()\n",
-        "\n",
         "# Delete the model\n",
         "model.delete()\n",
         "\n",