fix: assert

* feat: results viewer

* tune: comment

.cloud-build/execute_changed_notebooks_helper.py

@@ -44,6 +44,9 @@ from utils import NotebookProcessors, util
 WORKER_TIMEOUT_BUFFER_IN_SECONDS: int = 60 * 60
 PYTHON_VERSION = "3.9"  # Set default python version
 
+# rolling time window for accumulating build results for selecting notebooks
+MAX_RESULTS_AGE_SECONDS: int = (60 * 60) * 24 * 60  # 60 days
+
 
 def format_timedelta(delta: datetime.timedelta) -> str:
     """Formats a timedelta duration to [N days] %H:%M:%S format"""
@@ -86,8 +89,9 @@ class NotebookExecutionResult:
         else:
             return None
 
+
 def load_results(results_bucket: str,
-        results_file: str) -> Dict[str,Any]:
+                 results_file: str) -> Dict[str, Any]:
     '''
     Load accumulated notebook test results
     '''
@@ -101,11 +105,20 @@ def load_results(results_bucket: str,
         build_results_dir = os.path.dirname(results_file)
         blobs = client.list_blobs(results_bucket, prefix=build_results_dir)
         for blob in blobs:
+            time_created = blob.time_created.replace(tzinfo=None)
+            if (datetime.datetime.now().replace(tzinfo=None) - time_created).total_seconds() > MAX_RESULTS_AGE_SECONDS:
+                continue
+
             content = util.download_blob_into_memory(results_bucket, blob.name, download_as_text=True)
-            build_results = json.loads(content)
+
+            try:
+                build_results = json.loads(content)
+            except:
+                continue  # skip corrupted build results files
             for notebook in build_results:
                 if notebook in accumulative_results:
-                    accumulative_results[notebook] += build_results[notebook]
+                    accumulative_results[notebook]['passed'] += build_results[notebook]['passed']
+                    accumulative_results[notebook]['failed'] += build_results[notebook]['failed']
                 else:
                     accumulative_results[notebook] = build_results[notebook]
 
@@ -133,12 +146,17 @@ def select_notebook(changed_notebook: str,
     inferred_failure_rate = fail_count / (pass_count + fail_count)
 
     # If failure rate is high, the chance of testing should be higher
-    should_test_due_to_failure = random.uniform(0, 1) < inferred_failure_rate
+    should_test_due_to_failure = random.uniform(0, 1) <= inferred_failure_rate
 
     # Additionally, only test a percentage of these
-    should_test_due_to_random_subset = random.uniform(0, 1) < test_percent
+    should_test_due_to_random_subset = random.uniform(0, 1) <= (test_percent / 100)
 
-    return should_test_due_to_failure and should_test_due_to_random_subset
+    if should_test_due_to_failure or should_test_due_to_random_subset:
+        print(f"Selected: {changed_notebook}, {should_test_due_to_failure}, {should_test_due_to_random_subset}")
+        return True
+    else:
+        print(f"Not Selected: {changed_notebook}, pass {pass_count}, fail {fail_count}")
+        return False
 
 
 def _process_notebook(

.cloud-build/tests/select_notebooks_test.py

@@ -0,0 +1,33 @@
+import sys
+
+from execute_changed_notebooks_helper import (load_results, select_notebook)
+
+
+def test_load_results():
+    bucket: str = "cloud-build-notebooks-presubmit"
+    bucket_file: str = "build_results"
+
+    accum = load_results(bucket, bucket_file)
+
+    print(accum)
+
+    assert len(accum) > 0
+
+def test_select_notebook():
+    bucket: str = "cloud-build-notebooks-presubmit"
+    bucket_file: str = "build_results"
+
+    accum = load_results(bucket, bucket_file)
+
+    n_select = 0
+    n_notselect = 0
+    for notebook in accum:
+        if select_notebook(notebook, accum, 50):
+            n_select += 1
+        else:
+            n_notselect += 1
+
+    print(f"SELECTED {n_select}, NOT SELECTED {n_notselect}")
+
+    assert n_select > 0
+    assert n_notselect > 0

.cloud-build/utils/build_results_viewer.py

@@ -0,0 +1,22 @@
+'''
+Viewer for the weekly regression testing of the official notebooks
+
+Cloud Storage location: gs://cloud-build-notebooks-presubmit/build_results/
+'''
+import argparse
+import json
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--file', dest='file',
+                    default='build.json', type=str, help='build results file')
+import json
+
+with open('build.json', 'r') as f:
+    results = json.load(f)
+
+for item in results.items():
+    if item[1]['passed']:
+        print(f"{item[0]},PASSED")
+    else:
+        print(f"{item[0]},FAILED")

.github/workflows/linter/requirements.txt

@@ -2,9 +2,9 @@ git+https://github.com/tensorflow/docs
 ipython
 jupyter
 nbconvert
-black==22.12.0
+black==23.3.0
 pyupgrade==3.4.0
 isort==5.12.0
 flake8==6.0.0
-nbqa==1.5.3
+nbqa==1.7.0
 

notebooks/community/CODEOWNERS

@@ -42,6 +42,7 @@
 /notebooks/community/pipelines/google_cloud_pipeline_components_cloud_natural_language_pipeline.ipynb @Narwhalprime
 /notebooks/community/pipelines/google_cloud_pipeline_components_ready_to_go_text_classification_pipeline.ipynb @Narwhalprime
 /notebooks/community/feature_store/get_started_vertex_feature_store.ipynb @junkourata
+/notebooks/community/model_garden/model_garden_huggingface_local_inference.ipynb @dstnluong-google
 /notebooks/community/model_garden/model_garden_tfvision_image_classification.ipynb @genquan9
 /notebooks/community/model_garden/model_garden_tfvision_image_object_detection.ipynb @genquan9
 /notebooks/community/model_garden/model_garden_tfvision_image_segmentation.ipynb @genquan9

notebooks/community/model_garden/model_garden_huggingface_local_inference.ipynb

@@ -0,0 +1,358 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7d9bbf86da5e"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2023 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4dc4391f6be7"
+      },
+      "source": [
+        "# Vertex AI Model Garden - Hugging Face Local Inference\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_huggingface_local_inference.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_huggingface_local_inference.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <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_huggingface_local_inference.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",
+        "    </a>\n",
+        "    (a Python-3 GPU notebook with preinstalled HuggingFace/transformer libraries is recommended)\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4e8a0fdd6f44"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This notebook demonstrates how to run local inference with various Hugging Face models by using [Colab](https://colab.research.google.com/) and installing the necessary libraries or by deploying a [Vertex AI Workbench Instance](https://cloud.google.com/vertex-ai-workbench) with preinstalled transformer and diffuser libraries.\n",
+        "\n",
+        "### Objective\n",
+        "\n",
+        "* Run local inference with various transformer or diffusion models.\n",
+        "\n",
+        "### Costs\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "69453bf7230e"
+      },
+      "source": [
+        "## Before you begin"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "68990d91bc5f"
+      },
+      "source": [
+        "### Colab only"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3d342b32fb08"
+      },
+      "outputs": [],
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    ! pip3 install --upgrade google-cloud-aiplatform\n",
+        "    from google.colab import auth as google_auth\n",
+        "\n",
+        "    google_auth.authenticate_user()\n",
+        "    ! pip3 install --upgrade pip\n",
+        "    ! pip3 install torchvision==0.14.1\n",
+        "    ! pip3 install transformers==4.27.1\n",
+        "    ! pip3 install diffusers==0.15.1\n",
+        "    ! apt-get update\n",
+        "    ! apt-get install -y --no-install-recommends tesseract-ocr\n",
+        "    ! pip3 install tesseract==0.1.3\n",
+        "    ! pip3 install pytesseract==0.3.10\n",
+        "    ! pip3 install datasets==2.9.0\n",
+        "    ! pip3 install accelerate==0.18.0\n",
+        "    ! pip3 install triton==2.0.0.dev20221120\n",
+        "    ! pip3 install xformers==0.0.16\n",
+        "    ! pip3 install modelscope==1.4.2\n",
+        "    ! pip3 install open_clip_torch==2.17.1\n",
+        "    ! pip3 install pytorch-lightning==1.9.5\n",
+        "    ! pip3 install opencv-python-headless==4.7.0.72\n",
+        "    # Install gdown for downloading example training images.\n",
+        "    ! pip3 install gdown\n",
+        "    # Remove wrong cublas version.\n",
+        "    ! pip3 uninstall nvidia_cublas_cu11 --yes\n",
+        "\n",
+        "    # Restart the notebook kernel after installs.\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "05e23144b125"
+      },
+      "source": [
+        "### Workbench only\n",
+        "\n",
+        "1.   Follow [this link](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_huggingfacE_local_inference.ipynb) to deploy the notebook to a Vertex AI Workbench Instance.\n",
+        "2.   Select `Create a new Notebook`.\n",
+        "3.   Click `Advanced Options`.\n",
+        "4.   In the **Environment** tab, select `Custom Container` for **Environment**.\n",
+        "5.   Set the **Docker container image** field to `us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/transformers-notebook`.\n",
+        "6.   In the **Machine Type** tab, select a 1 `T4` GPU and select `Install NVIDIA GPU driver automatically for me`.\n",
+        "7.   Click `Create` to create the Vertex AI Workbench instance.\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ad1a690839d5"
+      },
+      "source": [
+        "## Sample code"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0a4008240483"
+      },
+      "source": [
+        "#### [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) (Text-to-image)\n",
+        "Generate photo-realistic images given any text input."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5ec6b474d1be"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "from diffusers import StableDiffusionPipeline\n",
+        "\n",
+        "model_id = \"runwayml/stable-diffusion-v1-5\"\n",
+        "pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n",
+        "pipe = pipe.to(\"cuda\")\n",
+        "\n",
+        "prompt = \"a photo of an astronaut riding a horse on mars\"\n",
+        "image = pipe(prompt).images[0]\n",
+        "\n",
+        "display(image)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ae94b9b23a52"
+      },
+      "source": [
+        "#### [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) (Text guided image-to-image)\n",
+        "Generate an image based on an initial image and a text prompt."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0acd70f4d08a"
+      },
+      "outputs": [],
+      "source": [
+        "from io import BytesIO\n",
+        "\n",
+        "import requests\n",
+        "import torch\n",
+        "from diffusers import StableDiffusionImg2ImgPipeline\n",
+        "from PIL import Image\n",
+        "\n",
+        "device = \"cuda\"\n",
+        "model_id_or_path = \"runwayml/stable-diffusion-v1-5\"\n",
+        "pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n",
+        "    model_id_or_path, torch_dtype=torch.float16\n",
+        ")\n",
+        "pipe = pipe.to(device)\n",
+        "\n",
+        "url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n",
+        "\n",
+        "response = requests.get(url)\n",
+        "init_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n",
+        "init_image = init_image.resize((768, 512))\n",
+        "\n",
+        "prompt = \"A fantasy landscape, trending on artstation\"\n",
+        "\n",
+        "images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images\n",
+        "display(images[0])"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e76b3fe8d10c"
+      },
+      "source": [
+        "#### [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting) (Image-inpainting)\n",
+        "Generate an image based on an original image and prompt, only editing the areas denoted by a mask image."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8bc3238be4e7"
+      },
+      "outputs": [],
+      "source": [
+        "from io import BytesIO\n",
+        "\n",
+        "import requests\n",
+        "import torch\n",
+        "from diffusers import StableDiffusionInpaintPipeline\n",
+        "from PIL import Image\n",
+        "\n",
+        "image_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\n",
+        "image_response = requests.get(image_url)\n",
+        "init_image = Image.open(BytesIO(image_response.content)).convert(\"RGB\")\n",
+        "display(init_image)\n",
+        "\n",
+        "mask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n",
+        "mask_response = requests.get(mask_url)\n",
+        "mask_image = Image.open(BytesIO(mask_response.content)).convert(\"RGB\")\n",
+        "\n",
+        "pipe = StableDiffusionInpaintPipeline.from_pretrained(\n",
+        "    \"runwayml/stable-diffusion-inpainting\",\n",
+        "    revision=\"fp16\",\n",
+        "    torch_dtype=torch.float16,\n",
+        ")\n",
+        "pipe.to(\"cuda\")\n",
+        "\n",
+        "prompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\n",
+        "images = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images\n",
+        "display(images[0])"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1ade95a9b20e"
+      },
+      "source": [
+        "#### [impira/layoutlm-document-qa](https://huggingface.co/impira/layoutlm-document-qa) (Document question answering)\n",
+        "Answer questions about a given document."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "492d9f1de3f2"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import pipeline\n",
+        "\n",
+        "nlp = pipeline(\n",
+        "    \"document-question-answering\",\n",
+        "    model=\"impira/layoutlm-document-qa\",\n",
+        ")\n",
+        "\n",
+        "print(\n",
+        "    nlp(\n",
+        "        \"https://templates.invoicehome.com/invoice-template-us-neat-750px.png\",\n",
+        "        \"What is the invoice number?\",\n",
+        "    )\n",
+        ")\n",
+        "# [{'score': 0.9943977, 'answer': 'us-001', 'start': 15, 'end': 15}]\n",
+        "\n",
+        "print(\n",
+        "    nlp(\n",
+        "        \"https://miro.medium.com/max/787/1*iECQRIiOGTmEFLdWkVIH2g.jpeg\",\n",
+        "        \"What is the purchase amount?\",\n",
+        "    )\n",
+        ")\n",
+        "# [{'score': 0.9912159, 'answer': '$1,000,000,000', 'start': 97, 'end': 97}]\n",
+        "\n",
+        "print(\n",
+        "    nlp(\n",
+        "        \"https://www.accountingcoach.com/wp-content/uploads/2013/10/income-statement-example@2x.png\",\n",
+        "        \"What are the 2020 net sales?\",\n",
+        "    )\n",
+        ")\n",
+        "# [{'score': 0.978011429309845, 'answer': '$ 3,980', 'start': 15, 'end': 16}]"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "model_garden_huggingface_local_inference.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/community/model_garden/model_garden_pytorch_stable_diffusion.ipynb

@@ -24,7 +24,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "99c1c3fc2ca5"
@@ -55,7 +54,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "3de7470326a2"
@@ -63,8 +61,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This notebook demonstrates running local inference on [Vertex AI Workbench](https://cloud.google.com/vertex-ai-workbench).\n",
-        "This notebook also demonstrates finetuning [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with [Dreambooth](https://huggingface.co/docs/diffusers/training/dreambooth) and deploying it on Vertex AI for online prediction.\n",
+        "This notebook demonstrates running local inference for [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) on either [Colab](https://colab.research.google.com) or [Vertex AI Workbench](https://cloud.google.com/vertex-ai-workbench). This notebook also demonstrates finetuning runwayml/stable-diffusion-v1-5 with [Dreambooth](https://huggingface.co/docs/diffusers/training/dreambooth) and deploying it on Vertex AI for online prediction.\n",
         "\n",
         "### Objective\n",
         "\n",
@@ -85,132 +82,33 @@
       ]
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "e8a42fa49305"
-      },
-      "source": [
-        "## Local inference (Workbench only)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1169c41b76b3"
-      },
-      "source": [
-        "The quickest and easiest way to use this model locally is by using Vertex AI Workbench with a pre-built custom container that has the necessary packages installed.\n",
-        "\n",
-        "\n",
-        "\n",
-        "1.   Follow [this link](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_pytorch_stable_diffusion.ipynb) to deploy the notebook to a Vertex AI Workbench Instance.\n",
-        "2.   Select `Create a new Notebook`.\n",
-        "3.   Click `Advanced Options`.\n",
-        "4.   Under **Environment**, select `Custom Container` for `Environment`. \n",
-        "5.   Set `Docker container image` to `us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/transformers-notebook`.\n",
-        "6.   Under **Machine configuration**, select a GPU and select `Install NVIDIA GPU driver automatically for me`.\n",
-        "7.   Click `Create` to create the Vertex AI Workbench instance. \n",
-        "\n",
-        "Once the notebook is ready, simply execute the code block(s) below in the Workbench instance."
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1d5ebc91c786"
-      },
-      "source": [
-        "### Text-to-image"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "d39ed8c97cc5"
-      },
-      "outputs": [],
-      "source": [
-        "import torch\n",
-        "from diffusers import StableDiffusionPipeline\n",
-        "\n",
-        "model_id = \"runwayml/stable-diffusion-v1-5\"\n",
-        "pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n",
-        "pipe = pipe.to(\"cuda\")\n",
-        "\n",
-        "prompt = \"a photo of an astronaut riding a horse on mars\"\n",
-        "image = pipe(prompt).images[0]\n",
-        "\n",
-        "display(image)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5aed5ed7b6f6"
-      },
-      "source": [
-        "### Text-guided image-to-image"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "faadabb7728f"
-      },
-      "outputs": [],
-      "source": [
-        "from io import BytesIO\n",
-        "\n",
-        "import requests\n",
-        "import torch\n",
-        "from diffusers import StableDiffusionImg2ImgPipeline\n",
-        "from PIL import Image\n",
-        "\n",
-        "device = \"cuda\"\n",
-        "model_id_or_path = \"runwayml/stable-diffusion-v1-5\"\n",
-        "pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n",
-        "    model_id_or_path, torch_dtype=torch.float16\n",
-        ")\n",
-        "pipe = pipe.to(device)\n",
-        "\n",
-        "url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n",
-        "\n",
-        "response = requests.get(url)\n",
-        "init_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n",
-        "init_image = init_image.resize((768, 512))\n",
-        "\n",
-        "prompt = \"A fantasy landscape, trending on artstation\"\n",
-        "\n",
-        "images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images\n",
-        "display(images[0])"
-      ]
-    },
-    {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "264c07757582"
       },
       "source": [
-        "## Setup environment\n",
+        "## Before you begin\n",
         "\n",
         "**NOTE**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
       ]
     },
     {
-      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ioensNKM8ned"
+      },
+      "source": [
+        "### Setup notebook"
+      ]
+    },
+    {
       "cell_type": "markdown",
       "metadata": {
         "id": "d73ffa0c0b83"
       },
       "source": [
-        "### Colab only"
+        "#### Colab only\n",
+        "Run the following commands for Colab and skip this section if you are using Workbench."
       ]
     },
     {
@@ -221,70 +119,48 @@
       },
       "outputs": [],
       "source": [
-        "!pip3 install --upgrade google-cloud-aiplatform"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "b60a4d7100bf"
-      },
-      "outputs": [],
-      "source": [
-        "from google.colab import auth as google_auth\n",
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    ! pip3 install --upgrade google-cloud-aiplatform\n",
+        "    from google.colab import auth as google_auth\n",
         "\n",
-        "google_auth.authenticate_user()"
+        "    google_auth.authenticate_user()\n",
+        "    ! pip3 install --upgrade pip\n",
+        "    ! pip3 install torchvision==0.14.1\n",
+        "    ! pip3 install transformers==4.27.1\n",
+        "    ! pip3 install diffusers==0.15.1\n",
+        "    ! pip3 install datasets==2.9.0\n",
+        "    ! pip3 install accelerate==0.18.0\n",
+        "    ! pip3 install triton==2.0.0.dev20221120\n",
+        "    ! pip3 install xformers==0.0.16\n",
+        "    # Install gdown for downloading example training images.\n",
+        "    ! pip3 install gdown\n",
+        "    # Remove wrong cublas version.\n",
+        "    ! pip3 uninstall nvidia_cublas_cu11 --yes\n",
+        "\n",
+        "    # Restart the notebook kernel after installs.\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "fb671e75ca7b"
       },
       "source": [
-        "### Install dependencies"
+        "#### Workbench only\n",
+        "1.   Follow [this link](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_pytorch_stable_diffusion_inpainting.ipynb) to deploy the notebook to a Vertex AI Workbench Instance.\n",
+        "2.   Select `Create a new Notebook`.\n",
+        "3.   Click `Advanced Options`.\n",
+        "4.   Under **Environment**, select `Custom Container` for `Environment`. \n",
+        "5.   Set `Docker container image` to `us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/transformers-notebook`.\n",
+        "6.   Under **Machine configuration**, select 1 `T4` GPU and select `Install NVIDIA GPU driver automatically for me`.\n",
+        "7.   Click `Create` to create the Vertex AI Workbench instance."
       ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "dc8ee367fb42"
-      },
-      "outputs": [],
-      "source": [
-        "# Install gdown for downloading example training images.\n",
-        "!pip install gdown"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5244aac3d929"
-      },
-      "source": [
-        "Restart the notebook kernel after installs."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "567212ff53a6"
-      },
-      "outputs": [],
-      "source": [
-        "import IPython\n",
-        "\n",
-        "app = IPython.Application.instance()\n",
-        "app.kernel.do_shutdown(True)"
-      ]
-    },
-    {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "bb7adab99e41"
@@ -304,7 +180,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "6c460088b873"
@@ -335,7 +210,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "e828eb320337"
@@ -358,7 +232,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "2cc825514deb"
@@ -383,7 +256,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "0c250872074f"
@@ -407,6 +279,7 @@
         "from io import BytesIO\n",
         "\n",
         "import requests\n",
+        "import torch\n",
         "from google.cloud import aiplatform, storage\n",
         "from PIL import Image\n",
         "\n",
@@ -492,7 +365,82 @@
       ]
     },
     {
-      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e8a42fa49305"
+      },
+      "source": [
+        "## Run inferences locally"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1d5ebc91c786"
+      },
+      "source": [
+        "### Text-to-image"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d39ed8c97cc5"
+      },
+      "outputs": [],
+      "source": [
+        "from diffusers import StableDiffusionPipeline\n",
+        "\n",
+        "model_id = \"runwayml/stable-diffusion-v1-5\"\n",
+        "pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n",
+        "pipe = pipe.to(\"cuda\")\n",
+        "\n",
+        "prompt = \"a photo of an astronaut riding a horse on mars\"\n",
+        "image = pipe(prompt).images[0]\n",
+        "\n",
+        "display(image)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5aed5ed7b6f6"
+      },
+      "source": [
+        "### Text-guided image-to-image"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "faadabb7728f"
+      },
+      "outputs": [],
+      "source": [
+        "from diffusers import StableDiffusionImg2ImgPipeline\n",
+        "\n",
+        "device = \"cuda\"\n",
+        "model_id_or_path = \"runwayml/stable-diffusion-v1-5\"\n",
+        "pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n",
+        "    model_id_or_path, torch_dtype=torch.float16\n",
+        ")\n",
+        "pipe = pipe.to(device)\n",
+        "\n",
+        "url = \"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg\"\n",
+        "\n",
+        "response = requests.get(url)\n",
+        "init_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n",
+        "init_image = init_image.resize((768, 512))\n",
+        "\n",
+        "prompt = \"A fantasy landscape, trending on artstation\"\n",
+        "\n",
+        "images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images\n",
+        "display(images[0])"
+      ]
+    },
+    {
       "cell_type": "markdown",
       "metadata": {
         "id": "e70e3519ff8b"
@@ -502,7 +450,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "0dc65d8f0689"
@@ -532,7 +479,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "969cfeb79317"
@@ -602,7 +548,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "bf7f82732e61"
@@ -612,7 +557,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "1cc26e68d7b0"
@@ -624,7 +568,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "cd7b56421392"
@@ -634,7 +577,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "6d331b1ea337"
@@ -662,7 +604,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "80b3fd2ace09"
@@ -691,7 +632,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "af21a3cff1e0"
@@ -716,7 +656,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "c1e51f764a60"
@@ -726,7 +665,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "fa686a54047c"
@@ -750,10 +688,9 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "id": "80b3fd2ace09"
+        "id": "SsjYFLvNymc0"
       },
       "source": [
         "NOTE: The model weights will be downloaded after the deployment succeeds. Thus additional 5 minutes of waiting time is needed **after** the above model deployment step succeeds and before you run the next step below. Otherwise you might see a `ServiceUnavailable: 503 502:Bad Gateway` error when you send requests to the endpoint."
@@ -783,7 +720,6 @@
       ]
     },
     {
-      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "ed3795d474b9"

notebooks/community/model_garden/model_garden_pytorch_stable_diffusion_inpainting.ipynb

@@ -24,6 +24,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "1e9c07efb6ac"
@@ -48,12 +49,13 @@
         "      <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",
         "    </a>\n",
-        "    (a Python-3 CPU notebook is recommended)\n",
+        "    (a Python-3 GPU notebook with preinstalled HuggingFace/transformer libraries is recommended)\n",
         "  </td>\n",
         "</table>"
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "cd8433ec804a"
@@ -61,13 +63,14 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This notebook demonstrates finetuning [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting) with [Dreambooth](https://huggingface.co/docs/diffusers/training/dreambooth) and deploying it on Vertex-AI for online prediction.\n",
+        "This notebook demonstrates running local inference for [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting) on either [Colab](https://colab.research.google.com) or [Vertex AI Workbench](https://cloud.google.com/vertex-ai-workbench). This notebook also demonstrates finetuning runwayml/stable-diffusion-inpainting with [Dreambooth](https://huggingface.co/docs/diffusers/training/dreambooth) and deploying it on Vertex-AI for online prediction.\n",
         "\n",
         "### Objective\n",
         "\n",
+        "- Run local predictions for image-inpainting with serving dockers.\n",
         "- Finetune the stable-diffusion-inpainting model with [Dreambooth](https://huggingface.co/docs/diffusers/training/dreambooth).\n",
-        "- Upload the model to [Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).\n",
-        "- Deploy the model on [Endpoint](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints).\n",
+        "- Upload the model to [Vertex AI Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).\n",
+        "- Deploy the model to a [Vertex AI Endpoint resource](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints).\n",
         "- Run online predictions for image-inpainting.\n",
         "\n",
         "### Costs\n",
@@ -81,56 +84,36 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "id": "264c07757582"
+        "id": "Gl3bjJsV3k4J"
       },
       "source": [
-        "## Setup environment\n",
+        "## Before you begin\n",
         "\n",
         "**NOTE**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "id": "d73ffa0c0b83"
+        "id": "CaCslNQE37P_"
       },
       "source": [
-        "### Colab only"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2707b02ef5df"
-      },
-      "outputs": [],
-      "source": [
-        "!pip3 install --upgrade google-cloud-aiplatform"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "b60a4d7100bf"
-      },
-      "outputs": [],
-      "source": [
-        "from google.colab import auth as google_auth\n",
-        "\n",
-        "google_auth.authenticate_user()"
+        "### Setup notebook"
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "fb671e75ca7b"
       },
       "source": [
-        "### Install dependencies"
+        "#### Colab only\n",
+        "Run the following commands for Colab and skip this section if you are using Workbench."
       ]
     },
     {
@@ -141,34 +124,50 @@
       },
       "outputs": [],
       "source": [
-        "# Install gdown for downloading example training images.\n",
-        "!pip install gdown"
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    ! pip3 install --upgrade google-cloud-aiplatform\n",
+        "    from google.colab import auth as google_auth\n",
+        "\n",
+        "    google_auth.authenticate_user()\n",
+        "    ! pip3 install --upgrade pip\n",
+        "    ! pip3 install torchvision==0.14.1\n",
+        "    ! pip3 install transformers==4.27.1\n",
+        "    ! pip3 install diffusers==0.15.1\n",
+        "    ! pip3 install datasets==2.9.0\n",
+        "    ! pip3 install accelerate==0.18.0\n",
+        "    ! pip3 install triton==2.0.0.dev20221120\n",
+        "    ! pip3 install xformers==0.0.16\n",
+        "    # Install gdown for downloading example training images.\n",
+        "    ! pip3 install gdown\n",
+        "    # Remove wrong cublas version.\n",
+        "    ! pip3 uninstall nvidia_cublas_cu11 --yes\n",
+        "\n",
+        "    # Restart the notebook kernel after installs.\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
-        "id": "5244aac3d929"
+        "id": "JaY__7zz3uWk"
       },
       "source": [
-        "Restart the notebook kernel after installs."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "567212ff53a6"
-      },
-      "outputs": [],
-      "source": [
-        "import IPython\n",
-        "\n",
-        "app = IPython.Application.instance()\n",
-        "app.kernel.do_shutdown(True)"
+        "#### Workbench only\n",
+        "1.   Follow [this link](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_pytorch_stable_diffusion_inpainting.ipynb) to deploy the notebook to a Vertex AI Workbench Instance.\n",
+        "2.   Select `Create a new Notebook`.\n",
+        "3.   Click `Advanced Options`.\n",
+        "4.   Under **Environment**, select `Custom Container` for `Environment`. \n",
+        "5.   Set `Docker container image` to `us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/transformers-notebook`.\n",
+        "6.   Under **Machine configuration**, select 1 `T4` GPU and select `Install NVIDIA GPU driver automatically for me`.\n",
+        "7.   Click `Create` to create the Vertex AI Workbench instance."
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "bb7adab99e41"
@@ -188,6 +187,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "6c460088b873"
@@ -221,6 +221,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "e828eb320337"
@@ -243,6 +244,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "2cc825514deb"
@@ -267,6 +269,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "0c250872074f"
@@ -375,6 +378,59 @@
       ]
     },
     {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Wx18bDXC2XPR"
+      },
+      "source": [
+        "## Run inferences locally"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OSYVyAvu2mfN"
+      },
+      "source": [
+        "### Image-inpainting"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CZ2RkKT32Zj2"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "from diffusers import StableDiffusionInpaintPipeline\n",
+        "\n",
+        "image_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png\"\n",
+        "image_response = requests.get(image_url)\n",
+        "init_image = Image.open(BytesIO(image_response.content)).convert(\"RGB\")\n",
+        "display(init_image)\n",
+        "\n",
+        "mask_url = \"https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png\"\n",
+        "mask_response = requests.get(mask_url)\n",
+        "mask_image = Image.open(BytesIO(mask_response.content)).convert(\"RGB\")\n",
+        "\n",
+        "pipe = StableDiffusionInpaintPipeline.from_pretrained(\n",
+        "    \"runwayml/stable-diffusion-inpainting\",\n",
+        "    revision=\"fp16\",\n",
+        "    torch_dtype=torch.float16,\n",
+        ")\n",
+        "pipe.to(\"cuda\")\n",
+        "\n",
+        "prompt = \"Face of a yellow cat, high resolution, sitting on a park bench\"\n",
+        "images = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images\n",
+        "display(images[0])"
+      ]
+    },
+    {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "e70e3519ff8b"
@@ -384,6 +440,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "f560edbf96c6"
@@ -473,6 +530,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "90d3c379090e"
@@ -482,6 +540,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "1cc26e68d7b0"
@@ -493,6 +552,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "b8bb7d198315"
@@ -502,6 +562,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "79b66382f849"
@@ -529,6 +590,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "80b3fd2ace09"
@@ -565,6 +627,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "f12f8d9c2786"

notebooks/curated.csv

@@ -1,34 +0,0 @@
-tag,notebook,doc
-"AutoML, Text data",official/automl/automl-text-classification.ipynb,vertex-ai/docs/text-data/classification/train-model
-"AutoML, Tabular data",official/automl/sdk_automl_tabular_forecasting_batch.ipynb,vertex-ai/docs/tabular-data/forecasting/tutorials-samples
-"BigQuery, Vertex AI Workbench",official/workbench/exploratory_data_analysis/explore_data_in_bigquery_with_workbench.ipynb,
-"BigQuery ML, Vertex AI Model Registry, Batch prediction",official/model-registry/bqml-vertexai-model-registry.ipynb,
-"BigQuery ML, Vertex AI Model Registry, Online prediction",official/bigquery_ml/bqml-online-prediction.ipynb,
-Custom Training,official/custom/sdk-custom-image-classification-batch.ipynb,
-Custom Training,official/custom/sdk-custom-image-classification-online.ipynb,
-Tabular Data,official/automl/automl_forecasting_bqml_arima_plus_comparison.ipynb,vertex-ai/docs/tabular-data/forecasting-arima/overview
-"AutoML, Tabular Data",official/automl/automl_tabular_on_vertex_pipelines.ipynb,vertex-ai/docs/tabular-data/tabular-workflows/e2e-automl
-Vertex AI Experiments,official/experiments/comparing_pipeline_runs.ipynb,
-Vertex AI Experiments,official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb,
-Vertex AI Experiments,official/experiments/comparing_local_trained_models.ipynb,
-Vertex AI Feature Store,official/feature_store/sdk-feature-store.ipynb,
-Matching Engine,official/matching_engine/sdk_matching_engine_for_indexing.ipynb,
-Model Monitoring,official/model_monitoring/model_monitoring.ipynb,
-Vertex AI Pipelines,official/pipelines/lightweight_functions_component_io_kfp.ipynb,
-"Vertex AI Pipelines Image data",official/pipelines/google_cloud_pipeline_components_automl_images.ipynb,
-"Vertex AI Pipelines, Tabular data",official/pipelines/automl_tabular_classification_beans.ipynb,
-"Vertex AI Pipelines, Tabular data",official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb,
-"Vertex AI Pipelines, Text data",official/pipelines/google_cloud_pipeline_components_automl_text.ipynb,
-Vertex AI Pipelines,official/pipelines/custom_model_training_and_batch_prediction.ipynb,
-Vertex AI Pipelines,official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb,
-Vertex AI Pipelines,official/pipelines/control_flow_kfp.ipynb,
-Vertex AI Pipelines,official/pipelines/metrics_viz_run_compare_kfp.ipynb,
-Vertex AI Pipelines,official/pipelines/pipelines_intro_kfp.ipynb,
-Vertex AI Vizier,official/vizier/gapic-vizier-multi-objective-optimization.ipynb,vertex-ai/docs/vizier/using-vizier
-"Vertex Explainable AI, Tabular data",official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb,vertex-ai/docs/explainable-ai/overview
-"Vertex Explainable AI, Tabular data",official/explainable_ai/sdk_automl_tabular_classification_online_explain.ipynb,vertex-ai/docs/explainable-ai/overview
-"Vertex Explainable AI, Image data",official/explainable_ai/sdk_custom_image_classification_batch_explain.ipynb,vertex-ai/docs/explainable-ai/overview
-"Vertex Explainable AI, Image data",official/explainable_ai/sdk_custom_image_classification_online_explain.ipynb,vertex-ai/docs/explainable-ai/overview
-"Vertex Explainable AI, Tabular data",official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb,vertex-ai/docs/explainable-ai/overview
-"Vertex Explainable AI, Tabular data",official/explainable_ai/sdk_custom_tabular_regression_online_explain.ipynb,vertex-ai/docs/explainable-ai/overview
-Vertex ML Metadata,official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb,

notebooks/official/bigquery_ml/bqml-online-prediction.ipynb

@@ -437,7 +437,7 @@
       },
       "outputs": [],
       "source": [
-        "bq_client = bigquery.Client(project=PROJECT_ID)"
+        "bq_client = bigquery.Client(project=PROJECT_ID, location=REGION)"
       ]
     },
     {

notebooks/official/migration/sdk-automl-object-tracking-batch-prediction.ipynb

@@ -34,18 +34,18 @@
         "<table align=\"left\">\n",
         "\n",
         "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ15 Vertex SDK AutoML Object Tracking.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-object-tracking-batch-prediction.ipynb\">\n",
         "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ15 Vertex SDK AutoML Object Tracking.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-object-tracking-batch-prediction.ipynb\">\n",
         "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ15 Vertex SDK AutoML Object Tracking.ipynb\" target='_blank'>\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-automl-object-tracking-batch-prediction.ipynb\" target='_blank'>\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",
         "    </a>\n",

notebooks/official/migration/sdk-automl-text-sentiment-analysis-batch-prediction.ipynb

@@ -34,18 +34,18 @@
         "<table align=\"left\">\n",
         "\n",
         "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ8 Vertex SDK AutoML Text Sentiment Analysis.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-text-sentiment-analysis-batch-prediction.ipynb\">\n",
         "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ8 Vertex SDK AutoML Text Sentiment Analysis.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-text-sentiment-analysis-batch-prediction.ipynb\">\n",
         "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
         "      View on GitHub\n",
         "    </a>\n",
         "  </td>\n",
         "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ8 Vertex SDK AutoML Text Sentiment Analysis.ipynb\" target='_blank'>\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/sdk-automl-text-sentiment-analysis-batch-prediction.ipynb\" target='_blank'>\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",
         "    </a>\n",

notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb

@@ -63,7 +63,7 @@
         "\n",
         "This notebook demonstrates how to track metrics and parameters for Vertex AI custom training jobs, and how to perform detailed analysis using this data.\n",
         "\n",
-        "Learn more about [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata), [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training), and [Vertex AI Experiments](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments)."
       ]
     },
     {
@@ -81,6 +81,7 @@
         "- Vertex AI Model\n",
         "- Vertex AI Endpoint\n",
         "- Vertex AI Custom Training Job\n",
+        "- Vertex AI Experiments\n",
         "\n",
         "The steps performed include:\n",
         "- Track training parameters and prediction metrics for a custom training job.\n",

notebooks/official/workbench/spark/spark_bigquery.ipynb

@@ -54,6 +54,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "tvgnzT1CKxrO"
@@ -65,7 +66,7 @@
         "\n",
         "To run this notebook, click the link `Open in Vertex AI Workbench` above.\n",
         "\n",
-        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [Dataproc](https://cloud.google.com/vertex-ai/docs/pipelines/dataproc-component)."
+        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [Dataproc Serverless for Spark](https://cloud.google.com/dataproc-serverless/docs/guides/bigquery-connector-spark-example)."
       ]
     },
     {