Update automl_video_classification_model_evaluation.ipynb

Merge pull request #1562 from GoogleCloudPlatform/reznitskii-patch-8
Updated link
2026-09-26 14:42:04 +00:00 · 2023-03-02 15:11:07 -05:00 · 2023-03-02 08:25:51 +00:00 · 2023-03-02 08:25:22 +00:00 · 2023-03-02 01:34:46 +00:00 · 2023-03-02 01:26:52 +00:00
64 changed files with 3929 additions and 204 deletions
@@ -2,9 +2,9 @@ git+https://github.com/tensorflow/docs
 ipython
 jupyter
 nbconvert
-black==22.10.0
+black==22.12.0
 pyupgrade==2.38.4
-isort==5.10.1
+isort==5.12.0
 flake8==6.0.0
 nbqa==1.5.3

@@ -1,3 +1,3 @@
-torch==1.8.1
+torch==1.13.1
 torchvision==0.9.1
 tensorboard==2.5.0
@@ -31,17 +31,7 @@
      "source": [
        "# Deploying a PyTorch Text Classification Model on [Vertex AI](https://cloud.google.com/vertex-ai)\n",
        "\n",
-        "**This is an Experimental release**, covered by the Pre-GA Offerings Terms of your Google Cloud Platform [Terms of Service](https://cloud.google.com/terms).\n",
-        "\n",
-        "Experiments are focused on validating a prototype and are not guaranteed to be released. They are not intended for production use or covered by any SLA, support obligation, or deprecation policy and might be subject to backward-incompatible changes.\n",
-        "\n",
-        "**Kindly drop us a note before you run any scale tests.**\n",
-        "\n",
-        "**Do not hesitate to contact vertexai-prediction-preview-feedback@google.com if you have any questions or run into any issues.**\n",
-        "\n",
-        "The usage of the product is free during the Experimental release period: you will still incur charges for other GCP products usage, such as storage.\n",
-        "\n",
-        "The projects need to be added to the allowlist in order to deploy PyTorch models using Vertex AI Prediction pre-built PyTorch images. If you are interested in the feature, please send an email to vertexai-prediction-preview-feedback@google.com to provide your project numbers OR project ids."
+        "**Kindly reach out to Vertex AI before you run any scale tests or you have any questions.**\n"
      ]
    },
    {
@@ -1333,7 +1333,6 @@
        "Next, you compile the pipeline and then exeute it. The pipeline takes the following parameters, which are passed as the dictionary `parameter_values`:\n",
        "\n",
        "- `display_name`: A human readable name for the pipeline job.\n",
-        "- `import_file`: The Cloud Storage location to the dataset.\n",
        "- `worker_pool_specs`: The the machine and container, and auto-scaling requirements, as well as command line arguments.\n",
        "- `study_spec_metrics`: The metrics to optimize in the study trials.\n",
        "- `study_spec_parameters`: The parameters to tune."
@@ -33,6 +33,7 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "JAPoU8Sm5E6e"
@@ -41,18 +42,18 @@
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/vertex_endpoints/torchserve/dreambooth_stable_diffusion.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/vertex_endpoints/torchserve/dreambooth_stablediffusion.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/vertex_endpoints/torchserve/dreambooth_stable_diffusion.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/vertex_endpoints/torchserve/dreambooth_stablediffusion.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/blob/main/notebooks/community/vertex_endpoints/torchserve/dreambooth_stable_diffusion.ipynb\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/vertex_endpoints/torchserve/dreambooth_stablediffusion.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",
@@ -90,7 +91,7 @@
        "\n",
        "+ Create a `torchserve` handler for responding to prediction requests.\n",
        "+ Upload a Stable Diffusion 1.5 model on a prebuilt PyTorch container in Vertex AI.\n",
-        "+ Deploy a custom model to a Vertex AI Endpoint.\n",
+        "+ Deploy a model to a Vertex AI Endpoint.\n",
        "+ Send requests to the endpoint and parse the responses using Vertex AI Prediction service."
      ]
    },
@@ -100,7 +101,7 @@
        "id": "c6deba5a8557"
      },
      "source": [
-        "### Dataset\n",
+        "### Model\n",
        "\n",
        "This notebook uses a collection of model artifacts fine-tuned to generate images of a small dog. These are the same images used in the original [DreamBooth paper](https://dreambooth.github.io/)."
      ]
@@ -118,7 +119,7 @@
        "* Vertex AI models\n",
        "* Vertex AI endpoints\n",
        "* Vertex AI prediction\n",
-        "* Google Cloud Storage\n",
+        "* Cloud Storage\n",
        "* (Optionally) Vertex AI Workbench\n",
        "\n",
        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
@@ -420,7 +421,6 @@
      "outputs": [],
      "source": [
        "import base64\n",
-        "import gc\n",
        "import math\n",
        "\n",
        "import torch\n",
@@ -437,9 +437,9 @@
        "id": "697566b5f660"
      },
      "source": [
-        "## View model inferences\n",
+        "## Optional: View model inferences\n",
        "\n",
-        "Before uploading the model to Vertex AI, you can review the expected output from the model. The model used in this notebook is available for your use and can be downloaded from Google Cloud Storage. This download may take a few minutes to complete."
+        "Before uploading the model to Vertex AI, you can review the expected output from the model. The model used in this notebook is available for your use and can be downloaded from Cloud Storage. This download may take a few minutes to complete."
      ]
    },
    {
@@ -574,31 +574,6 @@
        "grid"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cf8d545a135b"
-      },
-      "source": [
-        "### Re-training the model\n",
-        "\n",
-        "If you aren't satisfied with the inference outputs from the model, you can retrain it. Before you can do that, you might need to release the GPU memory."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "5621b7a927f8"
-      },
-      "outputs": [],
-      "source": [
-        "# Run this cell _only_ if you want to release the GPU memory.\n",
-        "pipe = None\n",
-        "gc.collect()\n",
-        "torch.cuda.empty_cache()"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -607,10 +582,10 @@
      "source": [
        "## Deploy the model to Vertex AI\n",
        "\n",
-        "You can host your Stable Diffusion 2.0 model on a Vertex AI endpoint where you can get inferences from it online. Uploading your model is a four step process: \n",
+        "You can host your Stable Diffusion 1.5 model on a Vertex AI endpoint where you can get inferences from it online. Uploading your model is a four step process: \n",
        "\n",
        "1. Create a custom TorchServe handler.\n",
-        "1. Upload the model artifacts onto Google Cloud Storage.\n",
+        "1. Upload the model artifacts onto Cloud Storage.\n",
        "2. Create a Vertex AI model with the model artifacts and a prebuilt PyTorch container image.\n",
        "3. Deploy the Vertex AI model onto an endpoint."
      ]
@@ -740,9 +715,9 @@
        "id": "ffab030f4bc8"
      },
      "source": [
-        "### Upload the model artifacts to Google Cloud Storage\n",
+        "### Upload the model artifacts to Cloud Storage\n",
        "\n",
-        "Create a new folder in your Google Cloud Storage bucket to hold the model artifacts"
+        "Create a new folder in your Cloud Storage bucket to hold the model artifacts"
      ]
    },
    {
@@ -838,6 +813,16 @@
        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_NAME)"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FAPoU8Sm5E6e"
+      },
+      "source": [
+        "<div style=\"background:#e3effe; padding:5px; color:#0000aa\">\n",
+        "<strong>Note:</strong> The next cell fails if you haven't <a href=\"https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com\"><u>enabled the Vertex API</u></a>.</div>"
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -907,7 +892,7 @@
        "id": "c9fc560df0a9"
      },
      "source": [
-        "The previous cell, which deploys your model to the nedpoint, can take a while to complete. If the previous cell times out before returning, your endpoint might still be successfully deployed ot an end point. Check the [Cloud Console](https://console.cloud.google.com/vertex-ai/endpoints) to verify the results.\n",
+        "The previous cell, which deploys your model to the endpoint, can take a while to complete. If the previous cell times out before returning, your endpoint might still be successfully deployed to an endpoint. Check the [Cloud Console](https://console.cloud.google.com/vertex-ai/endpoints) to verify the results.\n",
        "\n",
        "You can also extend the time to wait for deployment by changing the `deploy_request_timeout` argument passed to `model.deploy()`."
      ]
@@ -920,7 +905,7 @@
      "source": [
        "## Get online predictions\n",
        "\n",
-        "Finally, with your Stable Diffusion 2.0 model deployed to a Vertex AI endpoint, you can now get online predictions from it. Using the Vertex AI SDK, you only need a few lines of code to get an inference."
+        "Finally, with your Stable Diffusion 1.5 model deployed to a Vertex AI endpoint, you can now get online predictions from it. Using the Vertex AI SDK, you only need a few lines of code to get an inference."
      ]
    },
    {
@@ -74,7 +74,9 @@
      "source": [
        "## Overview\n",
        "\n",
-        "{TODO: Include a paragraph or two explaining what this example demonstrates, who should be interested in it, and what you need to know before you get started.}"
+        "{TODO: Include a paragraph or two explaining what this example demonstrates, who should be interested in it, and what you need to know before you get started.}\n",
+        "\n",
+        "Learn more about [web-doc-title](linkback-to-webdoc-page). {TODO: if more than one primary feature, add tag/linkback for each one}"
      ]
    },
    {
@@ -1140,16 +1140,25 @@ def add_index(path: str,
            print(f'            {tag.strip()}<br/>\n')
        print('        </td>')
        print('        <td>')
-        print(f'            <b>{title}</b>.\n')
+        print(f'            <b>{title}</b>. ')
        if args.desc:
            desc = replace_cl(desc.replace('`', ''))
            print('<br/>')
-            print(f'            {desc}<br/>\n')
+            print(f'            {desc}\n')
            
+            
+        if args.linkback and linkbacks:
+            num = len(tags)
+            for _ in range(num):
+                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')
+                    
        if args.steps:
            print("<devsite-expandable>\n")
-            print('  <h6 class="showalways">Notebook steps</h6>\n')
-            print('  <ul style="font-size: .75em">\n')
+            print('  <p class="showalways">Tutorial steps</p>\n')
+            print('  <ul>\n')
            
            if ":" in steps:
                steps = steps.split(':')[1].replace('*', '').replace('-', '').strip().split('\n')
@@ -1158,22 +1167,8 @@ def add_index(path: str,
              
            for step in steps:
                print(f'    <li>{replace_cl(step)}</li>\n')
-            #steps = replace_cl(steps.replace('\n', '<br/>').replace('-', '&nbsp;&nbsp;-').replace('**', '').replace('*', '&nbsp;&nbsp;-').replace('`', ''))
-            #print('<br/>' + steps +  '<br/>')
            print('  </ul>\n')
            print("</devsite-expandable>\n")
-            
-        if args.linkback and linkbacks:
-            num = len(tags)
-            for _ in range(num):
-                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')
-                    
-        if args.steps:
-            steps = replace_cl(steps.replace('\n', '<br/>').replace('-', '&nbsp;&nbsp;-').replace('**', '').replace('*', '&nbsp;&nbsp;-').replace('`', ''))
-            print('<br/><br/>' + steps +  '<br/>')
                    
        print('        </td>')
        print('        <td>')
@@ -1245,6 +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}}',
@@ -1270,6 +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}}',
    }
@@ -1325,7 +1324,7 @@ if args.web:
    print('            <th width="80px">Open in</th>')
    print('        </tr>')
    print('    </thead>')
-    print('    <tbody>')
+    print('    <tbody class="list">')

 if args.notebook_dir:
    if not os.path.isdir(args.notebook_dir):
@@ -23,7 +23,62 @@
    - Incorrect examples: "Let's update the field", "We'll update the field", "The user should update the field"
 - **Googlers**: Please follow our [branding guidelines](http://goto/cloud-branding).

-### Code
+
+## Authoring guidelines
+
+### Focus
+
+Notebooks for official are expected to be narrow focused, which highlight a subset of features of a Vertex AI product/service.
+The product/feature is to be highlighted in the Overview section. For example:
+
+```
+This tutorial demonstrates using Vertex AI Training to train an XGBoost model using a XGBoost pre-built training container.
+```
+
+In the above example, the Vertex AI product/service is `Vertex AI Training` and the feature is `XGBoost pre-built training container`.
+
+### Scope
+
+Notebooks for official are expected to be narrow in scope, without extra extraneous steps. For example, if the notebook is about training, we discourage ending the notebook with deploying the model and doing an online/batch prediction. On the later, we recommend a separate notebook about prediction that uses a pretrained model.
+
+#### Training
+
+Notebooks for training should be constructed as follows:
+
+1. If the training script(s) are small, embed them in the notebook and use %writefile to store them locally.
+2. If the training script(s) are large, store them in our public bucket: gs://cloud-samples-data/vertex-ai/dataset-management/script, and use !wgets to retrieve and store the script locally.
+3. Train the model using the Vertex AI SDK methods for custom training.
+4. Preferrable have the service upload the trained model to the Vertex AI Model Registry.
+4. Have the script do an evaluation.
+5. Retrieve the evaluation metrics and attach them as an artifact to the corresponding entry in the Model Registry.
+6. Optionally, download the model artifacts and test locally -- i.e., make a local prediction request.
+
+#### Evaluation
+
+Notebooks for evaluation should be constructed as follows:
+
+1. Use a pretrained model from a public repository.
+2. Upload the pretrained model to the Vertex AI Model Registry.
+3. Perform a model evaluation.
+4. Review the model evaluation.
+4. Attach the model evaluation to the corresponding entry in the Model Registry.
+
+#### Prediction
+
+Notebooks for prediction should be constructed as follows:
+
+1. Use a pretrained model from a public repository.
+2. If relevant, attach a serving function to the model artifacts.
+3. Upload the pretrained model to the Vertex AI Model Registry.
+4. For online:<br/>
+    A. Deploy the model.<br/>
+    B. Perform an online prediction.</br>
+    C. Review the result.
+5. For batch:<br/>
+    A. Perform a batch prediction.</br/>
+    B. Review the result.
+    
+## Code

 - Put all your installs and imports in a setup section.
 - Save the notebook with the Table of Contents open.
@@ -31,7 +86,7 @@
 - Follow the [Google Python Style guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md) and write readable code.
 - Keep cells small (max ~20 lines).

-## TensorFlow code style
+### TensorFlow code style

 Use the highest level API that gets the job done (unless the goal is to demonstrate the low level API). For example, when using Tensorflow:

@@ -39,6 +39,4 @@
 /tabular_workflows/prophet_on_vertex_pipelines.ipynb @TheMichaelHu
 /model_evaluation/custom_tabular_classification_model_evaluation.ipynb @soheilazangeneh
 /sdk/SDK_FBProphet_Forecasting_Online.ipynb @brianchunkang
-/automl/sdk_automl_forecasting_hierarchical_batch.ipynb @ivanmkc
-/prediction/custom_batch_prediction_feature_filter.ipynb @soheilazangeneh
-/feature_store/feature_store_streaming_ingestion_sdk.ipynb @soheilazangeneh
+/pipelines/Train_tabular_models_with_many_frameworks_and_import_to_Vertex_AI_using_Pipelines @Ark-kun
@@ -170,7 +170,7 @@
      },
      "outputs": [],
      "source": [
-        "!pip install -U google-cloud-pipeline-components -q"
+        "!pip install -U google-cloud-pipeline-components==1.0.25 -q"
      ]
    },
    {
@@ -333,7 +333,10 @@
      },
      "outputs": [],
      "source": [
-        "REGION = \"[your-region]\"  # @param {type: \"string\"}"
+        "REGION = \"us-central1\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
      ]
    },
    {
@@ -379,8 +382,11 @@
        "import os\n",
        "import sys\n",
        "\n",
-        "# If on Google Cloud Notebook, then don't execute this code\n",
-        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
+        "# If on Vertex AI Workbench, then don't execute this code\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
+        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
+        "    \"DL_ANACONDA_HOME\"\n",
+        "):\n",
        "    if \"google.colab\" in sys.modules:\n",
        "        from google.colab import auth as google_auth\n",
        "\n",
@@ -388,10 +394,9 @@
        "\n",
        "    # If you are running this notebook locally, replace the string below with the\n",
        "    # path to your service account key and run this cell to authenticate your GCP\n",
-        "    # account. Alternatively, you may edit this notebook to authenticate using\n",
-        "    # gcloud.\n",
+        "    # account.\n",
        "    elif not os.getenv(\"IS_TESTING\"):\n",
-        "        %env GOOGLE_APPLICATION_CREDENTIALS ''"
+        "        %env GOOGLE_APPLICATION_CREDENTIALS '[your-service-account-key-path]'"
      ]
    },
    {
@@ -478,6 +483,77 @@
        "! gsutil ls -al $BUCKET_URI"
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "44accda192d5"
+      },
+      "source": [
+        "#### Service Account\n",
+        "\n",
+        "You use a service account to create Vertex AI Pipeline jobs. If you do not want to use your project's Compute Engine service account, set `SERVICE_ACCOUNT` to another service account ID."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e0c9c4f84849"
+      },
+      "outputs": [],
+      "source": [
+        "SERVICE_ACCOUNT = \"[your-service-account]\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "604ae09ab6d3"
+      },
+      "outputs": [],
+      "source": [
+        "if (\n",
+        "    SERVICE_ACCOUNT == \"\"\n",
+        "    or SERVICE_ACCOUNT is None\n",
+        "    or SERVICE_ACCOUNT == \"[your-service-account]\"\n",
+        "):\n",
+        "    # Get your service account from gcloud\n",
+        "    if not IS_COLAB:\n",
+        "        shell_output = !gcloud auth list 2>/dev/null\n",
+        "        SERVICE_ACCOUNT = shell_output[2].replace(\"*\", \"\").strip()\n",
+        "\n",
+        "    else:  # IS_COLAB:\n",
+        "        shell_output = ! gcloud projects describe  $PROJECT_ID\n",
+        "        project_number = shell_output[-1].split(\":\")[1].strip().replace(\"'\", \"\")\n",
+        "        SERVICE_ACCOUNT = f\"{project_number}-compute@developer.gserviceaccount.com\"\n",
+        "\n",
+        "    print(\"Service Account:\", SERVICE_ACCOUNT)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d1ecb60964d5"
+      },
+      "source": [
+        "#### Set service account access for Vertex AI Pipelines\n",
+        "Run the following commands to grant your service account access to read and write pipeline artifacts in the bucket that you created in the previous step. You only need to run this step once per service account."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "a592f0a380c2"
+      },
+      "outputs": [],
+      "source": [
+        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectCreator $BUCKET_URI\n",
+        "\n",
+        "! gsutil iam ch serviceAccount:{SERVICE_ACCOUNT}:roles/storage.objectViewer $BUCKET_URI"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -836,6 +912,7 @@
        "\n",
        "job.run()\n",
        "\n",
+        "\n",
        "pipeline_task_details = job.gca_resource.job_detail.task_details\n",
        "\n",
        "if export_additional_model_without_custom_ops:\n",
@@ -877,6 +954,7 @@
        "stage_1_tuner_task = get_task_detail(\n",
        "    pipeline_task_details, \"automl-tabular-stage-1-tuner\"\n",
        ")\n",
+        "\n",
        "stage_1_tuning_result_artifact_uri = (\n",
        "    stage_1_tuner_task.outputs[\"tuning_result_output\"].artifacts[0].uri\n",
        ")"
@@ -64,7 +64,7 @@
        "\n",
        "This tutorial demonstrates how to use AutoML in production. This tutorial covers get started with AutoML training.\n",
        "\n",
-        "Learn more about [Vertex AI for AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users)."
+        "Learn more about [AutoML training](https://cloud.google.com/vertex-ai/docs/training-overview)."
      ]
    },
    {
@@ -872,6 +872,18 @@
        "The execution of the training pipeline will take upto > 30 minutes."
      ]
    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3eaba926cdfa"
+      },
+      "outputs": [],
+      "source": [
+        "if os.getenv(\"IS_TESTING\"):\n",
+        "    sys.exit(0)"
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -60,7 +60,7 @@
        "\n",
        "This tutorial walks you through building a custom container to serve a scikit-learn model on Vertex AI. You use the FastAPI Python web server framework to create a prediction and health endpoint. You also incorporate a pre-processor from training pipeline into your online serving application.\n",
        "\n",
-        "Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)  and [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions)."
+        "Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training) and [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions)."
      ]
    },
    {
@@ -64,7 +64,7 @@
        "\n",
        "Learn more about serving an FBProphet model from this [article on testdriven.io: Deploying and Hosting a Machine Learning Model with FastAPI and Heroku](https://testdriven.io/blog/fastapi-machine-learning/).\n",
        "\n",
-        "Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training) and [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions).\n"
+        "Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training) and [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions).\n"
      ]
    },
    {
@@ -64,7 +64,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to train and deploy a custom image classification model for batch prediction.\n",
        "\n",
-        "Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)  and [Vertex AI Batch Prediction](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions)."
+        "Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training) and [Vertex AI Batch Prediction](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions)."
      ]
    },
    {
@@ -64,7 +64,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to train and deploy a custom image classification model for online prediction.\n",
        "\n",
-        "Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)  and [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions)."
+        "Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training) and [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions)."
      ]
    },
    {
@@ -73,7 +73,7 @@
        "\n",
        "This tutorial demonstrates how to use Vertex AI in production. This tutorial covers get started with Vertex AI Experiments.\n",
        "\n",
-        "Learn more about [Vertex AI Experiments](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments), [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata) and [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Vertex AI Experiments](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments), [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)."
      ]
    },
    {
@@ -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)."
      ]
    },
    {
@@ -292,8 +292,7 @@
      },
      "outputs": [],
      "source": [
-        "# VPC_NETWORK = \"[your-vpc-network-name]\"  # @param {type:\"string\"}\n",
-        "VPC_NETWORK = \"matching-engine-test\"  # @param {type:\"string\"}\n",
+        "VPC_NETWORK = \"[your-vpc-network-name]\"  # @param {type:\"string\"}\n",
        "\n",
        "PEERING_RANGE_NAME = \"ann-haystack-range\""
      ]
@@ -544,7 +543,7 @@
        "        json.dumps(\n",
        "            {\n",
        "                \"id\": str(index),\n",
-        "                \"embedding\": [str(value) for value in train[index]],\n",
+        "                \"embedding\": [str(value) for value in embedding],\n",
        "                \"restricts\": [\n",
        "                    {\n",
        "                        \"namespace\": \"class\",\n",
@@ -646,7 +645,7 @@
      "outputs": [],
      "source": [
        "tree_ah_index = aiplatform.MatchingEngineIndex.create_tree_ah_index(\n",
-        "    display_name=DISPLAY_NAME_BRUTE_FORCE,\n",
+        "    display_name=DISPLAY_NAME,\n",
        "    contents_delta_uri=EMBEDDINGS_INITIAL_URI,\n",
        "    dimensions=DIMENSIONS,\n",
        "    approximate_neighbors_count=150,\n",
@@ -712,7 +711,7 @@
      "outputs": [],
      "source": [
        "brute_force_index = aiplatform.MatchingEngineIndex.create_brute_force_index(\n",
-        "    display_name=DISPLAY_NAME,\n",
+        "    display_name=DISPLAY_NAME_BRUTE_FORCE,\n",
        "    contents_delta_uri=EMBEDDINGS_INITIAL_URI,\n",
        "    dimensions=DIMENSIONS,\n",
        "    distance_measure_type=\"DOT_PRODUCT_DISTANCE\",\n",
@@ -64,7 +64,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to train and deploy an AutoML image classification model.\n",
        "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [AutoML Image](https://cloud.google.com/vertex-ai/docs/tutorials/image-recognition-automl/training)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Classification for image data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_images)."
      ]
    },
    {
@@ -64,7 +64,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to train and deploy a custom tabular classification scikit-learn model for batch prediction.\n",
        "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
@@ -65,7 +65,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to hyperparamer tune a custom tabular classification TemsorFlow model.\n",
        "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview) and [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
@@ -568,7 +568,7 @@
      },
      "outputs": [],
      "source": [
-        "IMPORT_FILE = \"gs://automl-video-demo-data/hmdb_split1_5classes_train_inf.csv\""
+        "IMPORT_FILE = \"gs://automl-video-demo-data/hmdb_split1_train_40_mp4_gs.csv\""
      ]
    },
    {
@@ -604,6 +604,19 @@
        "! gsutil cat $FILE | head"
      ]
    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f483d8d80f64"
+      },
+      "outputs": [],
+      "source": [
+        "import datetime\n",
+        "\n",
+        "start = datetime.datetime.now()"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -757,7 +770,19 @@
        "\n",
        "The `run` method when completed returns the `Model` resource.\n",
        "\n",
-        "The execution of the training pipeline will take upto 20 minutes."
+        "The execution of the training pipeline may take over 24 hrs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8f135100f5d9"
+      },
+      "outputs": [],
+      "source": [
+        "if os.getenv(\"IS_TESTING\"):\n",
+        "    sys.exit(0)"
      ]
    },
    {
@@ -806,6 +831,18 @@
        "    INFO:google.cloud.aiplatform.training_jobs:Model available at projects/759209241365/locations/us-central1/models/1899701006099283968"
      ]
    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "48ca7f0980e0"
+      },
+      "outputs": [],
+      "source": [
+        "end = datetime.datetime.now()\n",
+        "end - start"
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -64,7 +64,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to train a AutoML video object tracking 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/video-intelligence/automl/object-tracking/docs/index-object-tracking)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Object tracking for video data](https://cloud.google.com/vertex-ai/docs/training-overview#object_tracking_for_videos)."
      ]
    },
    {
@@ -64,7 +64,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to train using a pre-built container and deploy a custom image classification model for online and batch prediction.\n",
        "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
@@ -63,7 +63,7 @@
        "\n",
        "This notebook demonstrates training a custom image classification model using Tensorflow and Vertex AI SDK by creating a custom training container. Additionally, the notebooks also deploys the trained model to Vertex AI and predictions are generated from it.\n",
        "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
@@ -65,7 +65,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to train and deploy an AutoML object detection model.\n",
        "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [AutoML Image](https://cloud.google.com/vertex-ai/docs/tutorials/image-recognition-automl/training)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Object detection for image data](https://cloud.google.com/vertex-ai/docs/training-overview#object_detection_for_images)."
      ]
    },
    {
@@ -61,11 +61,11 @@
      "source": [
        "## Overview\n",
        "\n",
-        "This notebook demonstrates how to create an AutoML Video Classification Model, with a Vertex AI video dataset, and how to serve the model for batch prediction. It requires you provide a bucket where the dataset will be stored.\n",
+        "This notebook demonstrates how to create an AutoML Text Classification Model, with a Vertex AI text dataset, and how to serve the model for batch prediction. It requires you provide a bucket where the dataset will be stored.\n",
        "\n",
        "Note: you may incur charges for training, prediction, storage or usage of other GCP products in connection with testing this SDK.\n",
        "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/classification/prepare-data)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Classification for text data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_text)."
      ]
    },
    {
@@ -76,7 +76,7 @@
      "source": [
        "### Objective\n",
        "\n",
-        "The objective of this notebook is to build a AutoML Video Classification Model. The following steps have been followed:\n",
+        "The objective of this notebook is to build a AutoML Text Classification Model. The following steps have been followed:\n",
        "This tutorial uses the following Google Cloud ML services :\n",
        "\n",
        "* Vertex AI Dataset resource\n",
@@ -87,11 +87,11 @@
        "The steps performed include the following:\n",
        "\n",
        "* Set your task name, and GCS prefix\n",
-        "* Copy AutoML video demo train data for creating managed dataset\n",
+        "* Copy AutoML text demo train data for creating managed dataset\n",
        "* Create a dataset on Vertex AI.\n",
        "* Configure a training job\n",
        "* Launch a training job and create a model on Vertex AI\n",
-        "* Copy AutoML Video Demo Prediction Data for creating batch prediction job\n",
+        "* Copy AutoML Text Demo Prediction Data for creating batch prediction job\n",
        "* Perform batch prediction job on the model"
      ]
    },
@@ -65,7 +65,7 @@
        "\n",
        "Note: you may incur charges for training, prediction, storage or usage of other Google Cloud products in connection with testing this SDK.\n",
        "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/entity-extraction/prepare-data)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Entity extraction for text data](https://cloud.google.com/vertex-ai/docs/training-overview#entity_extraction_for_text).\n"
      ]
    },
    {
@@ -76,7 +76,7 @@
      "source": [
        "### Objective\n",
        "\n",
-        "The objective of this notebook is to build a AutoML Text Entity Extraction Model. The following steps have been followed:\n",
+        "The objective of this notebook is to build a AutoML Text Entity Extraction model. The following steps have been followed:\n",
        "This tutorial uses the following Google Cloud ML services :\n",
        "\n",
        "* Vertex AI Dataset resource\n",
@@ -66,7 +66,7 @@
        "\n",
        "Note: you may incur charges for training, prediction, storage or usage of other Google Cloud products in connection with testing this SDK.\n",
        "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/sentiment-analysis/prepare-data)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Sentiment analysis for text data](https://cloud.google.com/vertex-ai/docs/training-overview#sentiment_analysis_for_text)."
      ]
    },
    {
@@ -64,7 +64,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to train and deploy a custom tabular classification XGBoost model for batch prediction.\n",
        "\n",
-        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
@@ -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 [Vertex AI 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) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
@@ -92,7 +92,7 @@
        "- Run the `AutoMLTabularTrainingJob` which returns a model\n",
        "- Import a pre-trained `AutoML model resource` into the pipeline\n",
        "- Run a `batch prediction` job in the pipeline\n",
-        "- Evaulate the AutoML model using the `regression evaluation component`\n",
+        "- Evaluate the AutoML model using the `regression evaluation component`\n",
        "- Import the Regression Metrics to the AutoML model resource"
      ]
    },
@@ -63,7 +63,7 @@
        "\n",
        "This notebook demonstrates how to use the Vertex AI classification model evaluation component to evaluate an AutoML video classification model. Model evaluation helps you determine your model performance based on the evaluation metrics and improve the model if necessary. \n",
        "\n",
-        "Learn more about [Vertex AI Model Evaluation](https://cloud.google.com/vertex-ai/docs/evaluation/introduction) and [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/classification/prepare-data)."
+        "Learn more about [Vertex AI Model Evaluation](https://cloud.google.com/vertex-ai/docs/evaluation/introduction) and [Classification for video data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_videos)."
      ]
    },
    {
@@ -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."
      ]
    },
@@ -792,7 +792,19 @@
        "\n",
        "The `run` method when completed returns the `Model` resource.\n",
        "\n",
-        "The execution of the training pipeline can take over 3 hours to complete."
+        "The execution of the training pipeline can take over 24 hours to complete."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3eaba926cdfa"
+      },
+      "outputs": [],
+      "source": [
+        "if os.getenv(\"IS_TESTING\"):\n",
+        "    sys.exit(0)"
      ]
    },
    {
@@ -63,7 +63,7 @@
        "\n",
        "This notebook demonstrates how to use the Vertex AI regression model evaluation component to evaluate a custom regression model. Model evaluation helps you determine your model performance based on the evaluation metrics and improve the model if necessary. \n",
        "\n",
-        "Learn more about [Vertex AI Model Evaluation](https://cloud.google.com/vertex-ai/docs/evaluation/introduction) and [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Vertex AI Model Evaluation](https://cloud.google.com/vertex-ai/docs/evaluation/introduction) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
@@ -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."
      ]
    },
@@ -0,0 +1,865 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ur8xi4C7S06n"
+      },
+      "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."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JAPoU8Sm5E6e"
+      },
+      "source": [
+        "# Get started with importing a custom model evaluation to the Vertex AI Model Registry\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "\n",
+        "  <td>\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/get_started_with_custom_model_evaluation_import.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/model_evaluation/get_started_with_custom_model_evaluation_import.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/model_evaluation/get_started_with_custom_model_evaluation_import.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",
+        "  </td>                                                                                               \n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "24743cf4a1e1"
+      },
+      "source": [
+        "**_NOTE_**: This notebook has been tested in the following environment:\n",
+        "\n",
+        "* Python version = 3.9"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tvgnzT1CKxrO"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This tutorial shows how to use Vertex AI Model Evaluation to import a custom model evaluation to an existing Vertex AI Model Registry entry."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d975e698c9a4"
+      },
+      "source": [
+        "### Objective\n",
+        "\n",
+        "In this tutorial, you learn how to construct and upload a custom model evaluation, and upload the custom model evaluation to a Model resource entry in Vertex AI Model Registry.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "\n",
+        "- Vertex AI Model Evaluation\n",
+        "- Vertex AI Model Registry\n",
+        "\n",
+        "The steps performed include:\n",
+        "\n",
+        "- Import a pretrained (blessed) model to the Vertex AI Model Registry.\n",
+        "- Construct a custom model evaluation.\n",
+        "- Import the model evaluation metrics to the corresponding model in the Vertex AI Model Registry.\n",
+        "- List the model evaluation for the corresponding model in the Vertex AI Model Registry.\n",
+        "- Construct a second custom model evaluation.\n",
+        "- Import the second model evaluation metrics to the corresponding model in the Vertex AI Model Registry.\n",
+        "- List the second model evaluation for the corresponding model in the Vertex AI Model Registry.\n",
+        "\n",
+        "Learn more about [Model Evaluation in Vertex AI](https://cloud.google.com/vertex-ai/docs/evaluation/introduction)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "08d289fa873f"
+      },
+      "source": [
+        "### Model\n",
+        "\n",
+        "This tutorial uses a pre-trained image classification model from TensorFlow Hub, which is trained on ImageNet dataset.\n",
+        "\n",
+        "Learn more about [ResNet V2 pretained model](https://tfhub.dev/google/imagenet/resnet_v2_101/classification/5). "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aed92deeb4a0"
+      },
+      "source": [
+        "### Costs \n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "* Cloud Storage\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing),\n",
+        "and [Cloud Storage pricing](https://cloud.google.com/storage/pricing), \n",
+        "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n",
+        "to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i7EUnXsZhAGF"
+      },
+      "source": [
+        "## Installation\n",
+        "\n",
+        "Install the following packages required to execute this notebook. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2b4ef9b72d43"
+      },
+      "outputs": [],
+      "source": [
+        "# Install the packages\n",
+        "USER=''\n",
+        "! pip3 install {USER} --upgrade google-cloud-aiplatform \\\n",
+        "                                tensorflow==2.5 \\\n",
+        "                                tensorflow-hub"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "58707a750154"
+      },
+      "source": [
+        "### Colab only: Uncomment the following cell to restart the kernel."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f200f10a1da3"
+      },
+      "outputs": [],
+      "source": [
+        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
+        "# import IPython\n",
+        "\n",
+        "# app = IPython.Application.instance()\n",
+        "# app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BF1j6f9HApxa"
+      },
+      "source": [
+        "## Before you begin\n",
+        "\n",
+        "### Set up your Google Cloud project\n",
+        "\n",
+        "**The following steps are required, regardless of your notebook environment.**\n",
+        "\n",
+        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
+        "\n",
+        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
+        "\n",
+        "3. [Enable the Vertex AI API]\n",
+        "\n",
+        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WReHDGG5g0XY"
+      },
+      "source": [
+        "#### Set your project ID\n",
+        "\n",
+        "**If you don't know your project ID**, try the following:\n",
+        "* Run `gcloud config list`.\n",
+        "* Run `gcloud projects list`.\n",
+        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oM1iC_MfAts1"
+      },
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# Set the project id\n",
+        "! gcloud config set project {PROJECT_ID}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "region"
+      },
+      "source": [
+        "#### Region\n",
+        "\n",
+        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "region"
+      },
+      "outputs": [],
+      "source": [
+        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sBCra4QMA2wR"
+      },
+      "source": [
+        "### Authenticate your Google Cloud account\n",
+        "\n",
+        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "74ccc9e52986"
+      },
+      "source": [
+        "**1. Vertex AI Workbench**\n",
+        "* Do nothing as you are already authenticated."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "de775a3773ba"
+      },
+      "source": [
+        "**2. Local JupyterLab instance, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "254614fa0c46"
+      },
+      "outputs": [],
+      "source": [
+        "# ! gcloud auth login"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ef21552ccea8"
+      },
+      "source": [
+        "**3. Colab, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "603adbbf0532"
+      },
+      "outputs": [],
+      "source": [
+        "# from google.colab import auth\n",
+        "# auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f6b2ccc891ed"
+      },
+      "source": [
+        "**4. Service account or other**\n",
+        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zgPO1eR3CYjk"
+      },
+      "source": [
+        "### Create a Cloud Storage bucket\n",
+        "\n",
+        "Create a storage bucket to store intermediate artifacts such as datasets.\n",
+        "\n",
+        "- *{Note to notebook author: For any user-provided strings that need to be unique (like bucket names or model ID's), append \"-unique\" to the end so proper testing can occur}*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MzGDU7TWdts_"
+      },
+      "outputs": [],
+      "source": [
+        "BUCKET_URI = f\"gs://your-bucket-name-unique-{PROJECT_ID}\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-EcIXiGsCePi"
+      },
+      "source": [
+        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NIq7R4HZCfIc"
+      },
+      "outputs": [],
+      "source": [
+        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "960505627ddf"
+      },
+      "source": [
+        "### Import libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PyQmSRbKA8r-"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "import tensorflow_hub as hub\n",
+        "from google.cloud import aiplatform\n",
+        "from google.cloud.aiplatform import gapic"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "init_aip:mbsdk,all"
+      },
+      "source": [
+        "### Initialize Vertex AI SDK for Python\n",
+        "\n",
+        "Initialize the Vertex AI SDK for Python for your project."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "init_aip:mbsdk,all"
+      },
+      "outputs": [],
+      "source": [
+        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "accelerators:training,cpu,prediction,cpu,mbsdk"
+      },
+      "source": [
+        "#### Set hardware accelerators\n",
+        "\n",
+        "You can set hardware accelerators for prediction.\n",
+        "\n",
+        "Set the variables `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n",
+        "\n",
+        "    (aip.gapic.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
+        "\n",
+        "\n",
+        "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
+        "\n",
+        "Learn more about [hardware accelerator support for your region](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators), and [GPU pricing](https://cloud.google.com/compute/gpus-pricing)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "accelerators:training,cpu,prediction,cpu,mbsdk"
+      },
+      "outputs": [],
+      "source": [
+        "DEPLOY_GPU, DEPLOY_NGPU = (None, None)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "container:training,prediction"
+      },
+      "source": [
+        "#### Set pre-built containers\n",
+        "\n",
+        "Set the pre-built Docker container image for prediction.\n",
+        "\n",
+        "For the latest list, see [Pre-built containers for prediction](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "container:training,prediction"
+      },
+      "outputs": [],
+      "source": [
+        "TF = \"2.5\".replace(\".\", \"-\")\n",
+        "\n",
+        "if DEPLOY_GPU:\n",
+        "    DEPLOY_VERSION = \"tf2-gpu.{}\".format(TF)\n",
+        "else:\n",
+        "    DEPLOY_VERSION = \"tf2-cpu.{}\".format(TF)\n",
+        "\n",
+        "DEPLOY_IMAGE = \"{}-docker.pkg.dev/vertex-ai/prediction/{}:latest\".format(\n",
+        "    REGION.split(\"-\")[0], DEPLOY_VERSION\n",
+        ")\n",
+        "\n",
+        "print(\"Deployment:\", DEPLOY_IMAGE, DEPLOY_GPU, DEPLOY_NGPU)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d8128b8ff025"
+      },
+      "source": [
+        "## Get pretrained model from TensorFlow Hub\n",
+        "\n",
+        "For demonstration purposes, this tutorial uses a pretrained model from TensorFlow Hub (TFHub), which is then uploaded to a `Vertex AI Model` resource. Once you have a `Vertex AI Model` resource, the model can be deployed to a `Vertex AI Endpoint` resource.\n",
+        "\n",
+        "### Download the pretrained model\n",
+        "\n",
+        "First, you download the pretrained model from TensorFlow Hub. The model gets downloaded as a TF.Keras layer. To finalize the model, in this example, you create a `Sequential()` model with the downloaded TFHub model as a layer, and specify the input shape to the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "c55fa4c826f7"
+      },
+      "outputs": [],
+      "source": [
+        "tfhub_model = tf.keras.Sequential(\n",
+        "    [hub.KerasLayer(\"https://tfhub.dev/google/imagenet/resnet_v2_101/classification/5\")]\n",
+        ")\n",
+        "\n",
+        "tfhub_model.build([None, 32, 32, 3])\n",
+        "\n",
+        "tfhub_model.summary()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "64618c713db9"
+      },
+      "outputs": [],
+      "source": [
+        "MODEL_DIR = BUCKET_URI + \"/model\"\n",
+        "tfhub_model.save(MODEL_DIR)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e8ce91147c93"
+      },
+      "source": [
+        "### Upload the TensorFlow Hub model to a `Vertex AI Model` resource\n",
+        "\n",
+        "Finally, you upload the model artifacts from the TFHub model into a `Vertex AI Model` resource using the method `upload()`, with the following parameters:\n",
+        "\n",
+        "- `display_name`: A human readable name for the `Model` resource.\n",
+        "- `artifact_uri`: The Cloud Storage location of the model package.\n",
+        "- `serving_container_image_uri`: The serving container image.\n",
+        "\n",
+        "Uploading a model into a Vertex AI Model resource returns a long running operation, since it may take a few moments. \n",
+        "\n",
+        "*Note:* When you upload the model artifacts to a `Vertex AI Model` resource, you specify the corresponding deployment container image."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ad61e1429512"
+      },
+      "outputs": [],
+      "source": [
+        "model = aiplatform.Model.upload(\n",
+        "    display_name=\"resnet\",\n",
+        "    artifact_uri=MODEL_DIR,\n",
+        "    serving_container_image_uri=DEPLOY_IMAGE,\n",
+        "    is_default_version=True,\n",
+        "    version_aliases=[\"v1\"],\n",
+        ")\n",
+        "\n",
+        "print(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c11e98ef5391"
+      },
+      "source": [
+        "## Introduction to custom model evaluations\n",
+        "\n",
+        "When training a custom model, one generally performs some form of an evaluation of the trained model. Your custom model evaluation can then be imported to the corresponding model in the Vertex AI Model Registry using the `import_model_evaluation()` method. Once imported, the custom model evaluation can be subsequently retreived with the `list_model_evaluations()` method. \n",
+        "\n",
+        "The Vertex AI Model Registry supports importing multiple model evaluations for a model where each evaluation is distinquished by a unique `display_name`.\n",
+        "\n",
+        "\n",
+        "### Create a model evaluation\n",
+        "\n",
+        "First, you create a model evaluation in a format that corresponds to one of the predefined schemas for model evaluations. In this example, you use the schema for a classification metric, and specify the following subset of evaluation metrics as a dictionary:\n",
+        "\n",
+        "- `logLoss`: The log loss.\n",
+        "- `auPrc`: The accuracy.\n",
+        "\n",
+        "You then construct the `ModelEvaluation` object with the following parameters:\n",
+        "\n",
+        "- `display_name`: The human readable name for the evaluation metric.\n",
+        "- `metrics_schema_uri`: The schema for the specific type of evaluation metrics.\n",
+        "- `metrics`: The dictionary with the evaluation metrics.\n",
+        "\n",
+        "Learn more about [Schemas for evaluation metrics](https://cloud.google.com/vertex-ai/docs/evaluation/introduction#features)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e9af222db292"
+      },
+      "outputs": [],
+      "source": [
+        "metrics = {\"logLoss\": 1.4, \"auPrc\": 0.85}\n",
+        "print(metrics)\n",
+        "\n",
+        "model_eval = gapic.ModelEvaluation(\n",
+        "    display_name=\"eval\",\n",
+        "    metrics_schema_uri=\"gs://google-cloud-aiplatform/schema/modelevaluation/classification_metrics_1.0.0.yaml\",\n",
+        "    metrics=metrics,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "68870bd8194d"
+      },
+      "source": [
+        "### Upload the evaluation metrics to the Model Registry\n",
+        "\n",
+        "Next, upload the model's evaluation from the custom training job to the corresponding entry in the Vertex AI Model Registry.\n",
+        "\n",
+        "Currently, there is not yet support for this method in the SDK. Instead, you use the lower level GAPIC API interface."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3044788848af"
+      },
+      "outputs": [],
+      "source": [
+        "API_ENDPOINT = f\"{REGION}-aiplatform.googleapis.com\"\n",
+        "client = gapic.ModelServiceClient(client_options={\"api_endpoint\": API_ENDPOINT})\n",
+        "\n",
+        "client.import_model_evaluation(parent=model.resource_name, model_evaluation=model_eval)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "337319a19efe"
+      },
+      "source": [
+        "### List the custom model evaluation\n",
+        "\n",
+        "Now that your custom metric has been uploaded to the corresponding model in the Vertex AI Model Registry, you can retriv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1b3374e988cf"
+      },
+      "outputs": [],
+      "source": [
+        "evaluation = model.list_model_evaluations()[0]\n",
+        "print(evaluation.gca_resource)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "68870bd8194d"
+      },
+      "source": [
+        "### Upload a second evaluation metrics to the Model Registry\n",
+        "\n",
+        "Next, upload a second model evaluation to the corresponding entry in the Vertex AI Model Registry. In this example, we refer to first evaluation metric as `eval` (from training) and the second as `prod` (from production data)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9b864c0ba07f"
+      },
+      "outputs": [],
+      "source": [
+        "metrics = {\"logLoss\": 1.2, \"auPrc\": 0.87}\n",
+        "print(metrics)\n",
+        "\n",
+        "model_prod = gapic.ModelEvaluation(\n",
+        "    display_name=\"prod\",\n",
+        "    metrics_schema_uri=\"gs://google-cloud-aiplatform/schema/modelevaluation/classification_metrics_1.0.0.yaml\",\n",
+        "    metrics=metrics,\n",
+        ")\n",
+        "\n",
+        "client.import_model_evaluation(parent=model.resource_name, model_evaluation=model_prod)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "337319a19efe"
+      },
+      "source": [
+        "### List a specific custom model evaluation\n",
+        "\n",
+        "Now that your custom second metric has been uploaded to the corresponding model in the Vertex AI Model Registry, you can retrieve this specific evaluation by filtering using the `display_name`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1b3374e988cf"
+      },
+      "outputs": [],
+      "source": [
+        "evaluations = model.list_model_evaluations()\n",
+        "for evaluation in evaluations:\n",
+        "    if evaluation.display_name == \"prod\":\n",
+        "        print(evaluation.gca_resource)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b3a3993dc813"
+      },
+      "source": [
+        "### Upload version 2 of the TFHub model to the `Vertex AI Model Registry`\n",
+        "\n",
+        "Next, you upload the second version of the TFHub model as a `Model` resource in the `Vertex AI Model Registry`, with the additional following parameters:\n",
+        "\n",
+        "- `parent_model`: The existing `Model` resource for which to add this model as the next model version.\n",
+        "- `is_default_version`: Whether this will be the default version for the `Model` resource. In this example, you change from the default from the first version to the second version of the model.\n",
+        "- `version_ailiases`: User defined list of alternative alias names for the model version, such as `production`.\n",
+        "- `version_description`: User description of the model version.\n",
+        "\n",
+        "When a subsequent model version is created in the `Vertex AI Model Registry`, the property `version_id` will automatically be incremented. In this example, it will be set to 2 (2nd version)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "852be5e5f964"
+      },
+      "outputs": [],
+      "source": [
+        "model_v2 = aiplatform.Model.upload(\n",
+        "    display_name=\"resnet\",\n",
+        "    artifact_uri=MODEL_DIR,\n",
+        "    serving_container_image_uri=DEPLOY_IMAGE,\n",
+        "    parent_model=model.resource_name,\n",
+        "    is_default_version=True,\n",
+        "    version_aliases=[\"v2\"],\n",
+        "    version_description=\"This is the second version of the model\",\n",
+        ")\n",
+        "\n",
+        "print(model_v2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "68870bd8194d"
+      },
+      "source": [
+        "### Upload an evaluation metrics for version 2 of the model to the Model Registry\n",
+        "\n",
+        "Next, upload a model evaluation to the corresponding model version in the Vertex AI Model Registry. Note, you referenced `model_v2.resource_name` to refer to version 2 of this model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9b864c0ba07f"
+      },
+      "outputs": [],
+      "source": [
+        "metrics = {\"logLoss\": 1.0, \"auPrc\": 0.91}\n",
+        "print(metrics)\n",
+        "\n",
+        "model_eval = gapic.ModelEvaluation(\n",
+        "    display_name=\"eval\",\n",
+        "    metrics_schema_uri=\"gs://google-cloud-aiplatform/schema/modelevaluation/classification_metrics_1.0.0.yaml\",\n",
+        "    metrics=metrics,\n",
+        ")\n",
+        "\n",
+        "client.import_model_evaluation(\n",
+        "    parent=model_v2.resource_name, model_evaluation=model_eval\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d4e72b21b06b"
+      },
+      "source": [
+        "### List the evaluations for both versions of the model\n",
+        "\n",
+        "Finally, list the number of evaluations for both versions of the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "369a23d392a4"
+      },
+      "outputs": [],
+      "source": [
+        "evaluations = model.list_model_evaluations()\n",
+        "print(\"Model v1 no. of evaluations\", len(evaluations))\n",
+        "evaluations = model_v2.list_model_evaluations()\n",
+        "print(\"Model v2 no. of evaluations\", len(evaluations))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TpV-iwP9qw9c"
+      },
+      "source": [
+        "## Cleaning up\n",
+        "\n",
+        "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"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sx_vKniMq9ZX"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "# Delete model resource\n",
+        "model.delete()\n",
+        "\n",
+        "# Delete Cloud Storage objects that were created\n",
+        "delete_bucket = False\n",
+        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "    ! gsutil -m rm -r $BUCKET_URI"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "get_started_with_custom_model_evaluation_import.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
@@ -55,7 +55,11 @@
        "</table>\n",
        "<br/><br/><br/>\n",
        "\n",
-        "**This feature is in private preview**. You can request access to model monitoring for AutoML image models with this [Form](https://docs.google.com/forms/d/1aTitFrlNRlUAF_gvbMFQWDY0Oq8T_fUrN1m79atF8xQ/edit?resourcekey=0-psKsTGtVUFFUBDL2timIQA)."
+        "**This feature is in experimental**. You can request access to model monitoring for AutoML image models with this [Form](https://docs.google.com/forms/d/1aTitFrlNRlUAF_gvbMFQWDY0Oq8T_fUrN1m79atF8xQ/edit?resourcekey=0-psKsTGtVUFFUBDL2timIQA).\n",
+        "\n",
+        "**LEGAL NOTICE**\n",
+        "\n",
+        "This is an Experimental release. Experiments are focused on validating a prototype and are not guaranteed to be released. Experiments are covered by the [Pre-GA Offerings Terms](https://cloud.google.com/terms/service-terms) of the Google Cloud Platform Terms of Service. They are not intended for production use or covered by any SLA,  support obligation, or deprecation policy and might be subject to backward-incompatible changes.\n"
      ]
    },
    {
@@ -188,7 +192,8 @@
        "\n",
        "! pip3 install {USER_FLAG} -q -U google-cloud-aiplatform \"shapely<2\" \\\n",
        "                                 tensorflow==2.7 \\\n",
-        "                                 google-api-core==2.10"
+        "                                 google-api-core==2.10 \\\n",
+        "                                 protobuf==3.20.3"
      ]
    },
    {
@@ -55,9 +55,13 @@
        "</table>\n",
        "<br/><br/><br/>\n",
        "\n",
-        "**This feature is in private preview**. You can request access to model monitoring for AutoML image models with this [Form](https://docs.google.com/forms/d/1aTitFrlNRlUAF_gvbMFQWDY0Oq8T_fUrN1m79atF8xQ/edit?resourcekey=0-psKsTGtVUFFUBDL2timIQA).\n",
+        "**This feature is in experimental**. You can request access to model monitoring for AutoML image models with this [Form](https://docs.google.com/forms/d/1aTitFrlNRlUAF_gvbMFQWDY0Oq8T_fUrN1m79atF8xQ/edit?resourcekey=0-psKsTGtVUFFUBDL2timIQA).\n",
        "\n",
-        "If after access is granted and your monitoring job fails to start for this reason `Training Datasets for AutoML Deployed Models no longer available, please explicitly configure Analysis Instance Schema`, you need to request that your project be added to the group `VISION_MM_EXP`."
+        "If after access is granted and your monitoring job fails to start for this reason `Training Datasets for AutoML Deployed Models no longer available, please explicitly configure Analysis Instance Schema`, you need to request that your project be added to the group `VISION_MM_EXP`.\n",
+        "\n",
+        "## LEGAL NOTICE\n",
+        "\n",
+        "This is an Experimental release. Experiments are focused on validating a prototype and are not guaranteed to be released. Experiments are covered by the [Pre-GA Offerings Terms](https://cloud.google.com/terms/service-terms) of the Google Cloud Platform Terms of Service. They are not intended for production use or covered by any SLA,  support obligation, or deprecation policy and might be subject to backward-incompatible changes."
      ]
    },
    {
@@ -189,14 +193,15 @@
        "    USER_FLAG = \"--user\"\n",
        "\n",
        "# Install the packages\n",
+        "# Install the packages\n",
        "! pip3 install {USER_FLAG} -q --upgrade google-cloud-aiplatform \\\n",
        "                                        google-cloud-storage \\\n",
-        "                                        pandas-gbq \n",
+        "                                        pandas-gbq\n",
        "\n",
-        "! pip3 install {USER_FLAG} -q -U google-api-core==2.10 \n",
-        "\n",
-        "! pip3 install {USER_FLAG} -q --upgrade protobuf \\\n",
-        "                                        tensorflow==2.7"
+        "! pip3 install {USER_FLAG} -q -U google-cloud-aiplatform \"shapely<2\" \\\n",
+        "                                 tensorflow==2.7 \\\n",
+        "                                 google-api-core==2.10 \\\n",
+        "                                 protobuf==3.20.3"
      ]
    },
    {
@@ -304,5 +304,29 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component).

-&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML components](https://cloud.google.com/vertex-ai/docs/pipelines/bigqueryml-component).
+[Train custom tabular ML models with many frameworks and import to Vertex AI using Vertex Pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/pipelines/Train_tabular_models_with_many_frameworks_and_import_to_Vertex_AI_using_Pipelines)

+Learn how to build a pipeline that does the following:
+
+*   Ingest data
+*   Transform data
+*   Clean up data
+*   Split data into train/test subsets
+*   Configure model
+*   Train model using multiple ML frameworks
+*   Import model into Vertex Model Registry
+*   [Optional] Deploy model to Vertex Endpoints for serving
+
+Included pipelines:
+
+*   Train ML model
+*   *   Tabular classification
+*   *   *   TensorFlow
+*   *   *   PyTorch
+*   *   *   XGBoost
+*   *   *   Scikit-learn
+*   *   Tabular regression
+*   *   *   TensorFlow
+*   *   *   PyTorch
+*   *   *   XGBoost
+*   *   *   Scikit-learn
@@ -0,0 +1,73 @@
+# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
+from kfp import components
+
+# %% Loading components
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
+train_logistic_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_logistic_regression_model/from_CSV/component.yaml")
+upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")
+
+# %% Pipeline definition
+def train_tabular_classification_logistic_regression_model_using_Scikit_learn_pipeline():
+    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
+    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
+    label_column = "tips"
+    # Deploying the model might incur additional costs over time
+    deploy_model = False
+
+    classification_label_column = "class"
+    all_columns = [label_column] + feature_columns
+
+    training_data = download_from_gcs_op(
+        gcs_path=dataset_gcs_uri
+    ).outputs["Data"]
+
+    training_data = select_columns_using_Pandas_on_CSV_data_op(
+        table=training_data,
+        column_names=all_columns,
+    ).outputs["transformed_table"]
+
+    # Cleaning the NaN values.
+    training_data = fill_all_missing_values_using_Pandas_on_CSV_data_op(
+        table=training_data,
+        replacement_value="0",
+        #replacement_type_name="float",
+    ).outputs["transformed_table"]
+
+    classification_training_data = binarize_column_using_Pandas_on_CSV_data_op(
+        table=training_data,
+        column_name=label_column,
+        predicate="> 0",
+        new_column_name=classification_label_column,
+    ).outputs["transformed_table"]
+
+    model = train_logistic_regression_model_using_scikit_learn_from_CSV_op(
+        dataset=classification_training_data,
+        label_column_name=classification_label_column,
+        # Optional:
+        #penalty="l2",
+        #solver="lbfgs",
+        #max_iterations=100,
+        #multi_class_mode="auto",
+        #random_seed=0,
+    ).outputs["model"]
+
+    vertex_model_name = upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op(
+        model=model,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        sklearn_vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=vertex_model_name,
+        ).outputs["endpoint_name"]
+
+pipeline_func = train_tabular_classification_logistic_regression_model_using_Scikit_learn_pipeline
+
+# %% Pipeline submission
+if __name__ == '__main__':
+    from google.cloud import aiplatform
+    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()
@@ -0,0 +1,95 @@
+# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
+from kfp import components
+
+# %% Loading components
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
+create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_fully_connected_network/component.yaml")
+train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
+create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
+upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")
+
+# %% Pipeline definition
+def train_tabular_classification_model_using_PyTorch_pipeline():
+    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
+    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
+    label_column = "tips"
+    # Deploying the model might incur additional costs over time
+    deploy_model = False
+
+    classification_label_column = "class"
+    all_columns = [label_column] + feature_columns
+
+    training_data = download_from_gcs_op(
+        gcs_path=dataset_gcs_uri
+    ).outputs["Data"]
+
+    training_data = select_columns_using_Pandas_on_CSV_data_op(
+        table=training_data,
+        column_names=all_columns,
+    ).outputs["transformed_table"]
+
+    # Cleaning the NaN values.
+    training_data = fill_all_missing_values_using_Pandas_on_CSV_data_op(
+        table=training_data,
+        replacement_value="0",
+        #replacement_type_name="float",
+    ).outputs["transformed_table"]
+
+    classification_training_data = binarize_column_using_Pandas_on_CSV_data_op(
+        table=training_data,
+        column_name=label_column,
+        predicate=" > 0",
+        new_column_name=classification_label_column,
+    ).outputs["transformed_table"]
+
+    network = create_fully_connected_pytorch_network_op(
+        input_size=len(feature_columns),
+        # Optional:
+        hidden_layer_sizes=[10],
+        activation_name="elu",
+        output_activation_name="sigmoid",
+        # output_size=1,
+    ).outputs["model"]
+
+    model = train_pytorch_model_from_csv_op(
+        model=network,
+        training_data=classification_training_data,
+        label_column_name=classification_label_column,
+        loss_function_name="binary_cross_entropy",
+        # Optional:
+        #number_of_epochs=1,
+        #learning_rate=0.1,
+        #optimizer_name="Adadelta",
+        #optimizer_parameters={},
+        #batch_size=32,
+        #batch_log_interval=100,
+        #random_seed=0,
+    ).outputs["trained_model"]
+
+    model_archive = create_pytorch_model_archive_with_base_handler_op(
+        model=model,
+        # Optional:
+        # model_name="model",
+        # model_version="1.0",
+    ).outputs["Model archive"]
+
+    vertex_model_name = upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op(
+        model_archive=model_archive,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=vertex_model_name,
+        ).outputs["endpoint_name"]
+
+pipeline_func=train_tabular_classification_model_using_PyTorch_pipeline
+
+# %% Pipeline submission
+if __name__ == '__main__':
+    from google.cloud import aiplatform
+    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()
@@ -0,0 +1,106 @@
+# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
+from kfp import components
+
+# %% Loading components
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
+split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
+create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Create_fully_connected_network/component.yaml")
+train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
+predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Predict/on_CSV/component.yaml")
+upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")
+
+# %% Pipeline definition
+def train_tabular_classification_model_using_TensorFlow_pipeline():
+    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
+    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
+    label_column = "tips"
+    training_set_fraction = 0.8
+    # Deploying the model might incur additional costs over time
+    deploy_model = False
+
+    classification_label_column = "class"
+    all_columns = [label_column] + feature_columns
+
+    dataset = download_from_gcs_op(
+        gcs_path=dataset_gcs_uri
+    ).outputs["Data"]
+
+    dataset = select_columns_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        column_names=all_columns,
+    ).outputs["transformed_table"]
+
+    dataset = fill_all_missing_values_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        replacement_value="0",
+        # # Optional:
+        # column_names=None,  # =[...]
+    ).outputs["transformed_table"]
+
+    classification_dataset = binarize_column_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        column_name=label_column,
+        predicate=" > 0",
+        new_column_name=classification_label_column,
+    ).outputs["transformed_table"]
+
+    split_task = split_rows_into_subsets_op(
+        table=classification_dataset,
+        fraction_1=training_set_fraction,
+    )
+    classification_training_data = split_task.outputs["split_1"]
+    classification_testing_data = split_task.outputs["split_2"]
+
+    network = create_fully_connected_tensorflow_network_op(
+        input_size=len(feature_columns),
+        # Optional:
+        hidden_layer_sizes=[10],
+        activation_name="elu",
+        output_activation_name="sigmoid",
+        # output_size=1,
+    ).outputs["model"]
+
+    model = train_model_using_Keras_on_CSV_op(
+        training_data=classification_training_data,
+        model=network,
+        label_column_name=classification_label_column,
+        # Optional:
+        loss_function_name="binary_crossentropy",
+        number_of_epochs=10,
+        #learning_rate=0.1,
+        #optimizer_name="Adadelta",
+        #optimizer_parameters={},
+        #batch_size=32,
+        #metric_names=["mean_absolute_error"],
+        #random_seed=0,
+    ).outputs["trained_model"]
+
+    predictions = predict_with_TensorFlow_model_on_CSV_data_op(
+        dataset=classification_testing_data,
+        model=model,
+        # label_column_name needs to be set when doing prediction on a dataset that has labels
+        label_column_name=classification_label_column,
+        # Optional:
+        # batch_size=1000,
+    ).outputs["predictions"]
+
+    vertex_model_name = upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op(
+        model=model,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=vertex_model_name,
+        ).outputs["endpoint_name"]
+
+pipeline_func = train_tabular_classification_model_using_TensorFlow_pipeline
+
+# %% Pipeline submission
+if __name__ == '__main__':
+    from google.cloud import aiplatform
+    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()
@@ -0,0 +1,94 @@
+# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
+from kfp import components
+
+# %% Loading components
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
+split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
+train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
+xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
+upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")
+
+# %% Pipeline definition
+def train_tabular_classification_model_using_XGBoost_pipeline():
+    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
+    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
+    label_column = "tips"
+    training_set_fraction = 0.8
+    # Deploying the model might incur additional costs over time
+    deploy_model = False
+
+    classification_label_column = "class"
+    all_columns = [label_column] + feature_columns
+
+    dataset = download_from_gcs_op(
+        gcs_path=dataset_gcs_uri
+    ).outputs["Data"]
+
+    dataset = select_columns_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        column_names=all_columns,
+    ).outputs["transformed_table"]
+
+    dataset = fill_all_missing_values_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        replacement_value="0",
+        # # Optional:
+        # column_names=None,  # =[...]
+    ).outputs["transformed_table"]
+
+    classification_dataset = binarize_column_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        column_name=label_column,
+        predicate="> 0",
+        new_column_name=classification_label_column,
+    ).outputs["transformed_table"]
+
+    split_task = split_rows_into_subsets_op(
+        table=classification_dataset,
+        fraction_1=training_set_fraction,
+    )
+    classification_training_data = split_task.outputs["split_1"]
+    classification_testing_data = split_task.outputs["split_2"]
+
+    model = train_XGBoost_model_on_CSV_op(
+        training_data=classification_training_data,
+        label_column_name=classification_label_column,
+        objective="binary:logistic",
+        # Optional:
+        #starting_model=None,
+        #num_iterations=10,
+        #booster_params={},
+        #booster="gbtree",
+        #learning_rate=0.3,
+        #min_split_loss=0,
+        #max_depth=6,
+    ).outputs["model"]
+
+    # Predicting on the testing data
+    predictions = xgboost_predict_on_CSV_op(
+        data=classification_testing_data,
+        model=model,
+        # label_column needs to be set when doing prediction on a dataset that has labels
+        label_column_name=classification_label_column,
+    ).outputs["predictions"]
+
+    vertex_model_name = upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op(
+        model=model,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=vertex_model_name,
+        ).outputs["endpoint_name"]
+
+pipeline_func = train_tabular_classification_model_using_XGBoost_pipeline
+
+# %% Pipeline submission
+if __name__ == '__main__':
+    from google.cloud import aiplatform
+    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()
@@ -0,0 +1,224 @@
+# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
+from kfp import components
+
+# %% Loading components
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
+split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
+
+# TensorFlow
+create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Create_fully_connected_network/component.yaml")
+train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
+predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Predict/on_CSV/component.yaml")
+upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/component.yaml")
+
+# PyTorch
+create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_fully_connected_network/component.yaml")
+train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
+create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
+upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/component.yaml")
+
+# XGBoost
+train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
+xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
+upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
+
+# Scikit-learn
+#train_linear_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_linear_regression_model/from_CSV/component.yaml")
+train_logistic_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_logistic_regression_model/from_CSV/component.yaml")
+upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/component.yaml")
+
+# Vertex AI
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")
+
+# %% Pipeline definition
+def train_tabular_classification_model_using_all_frameworks_pipeline():
+    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
+    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
+    label_column = "tips"
+    training_set_fraction = 0.8
+    # Deploying the model might incur additional costs over time
+    deploy_model = False
+
+    classification_label_column = "class"
+    all_columns = [label_column] + feature_columns
+
+    dataset = download_from_gcs_op(
+        gcs_path=dataset_gcs_uri
+    ).outputs["Data"]
+
+    dataset = select_columns_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        column_names=all_columns,
+    ).outputs["transformed_table"]
+
+    dataset = fill_all_missing_values_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        replacement_value="0",
+        # # Optional:
+        # column_names=None,  # =[...]
+    ).outputs["transformed_table"]
+
+    classification_dataset = binarize_column_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        column_name=label_column,
+        predicate=" > 0",
+        new_column_name=classification_label_column,
+    ).outputs["transformed_table"]
+
+    split_task = split_rows_into_subsets_op(
+        table=classification_dataset,
+        fraction_1=training_set_fraction,
+    )
+    classification_training_data = split_task.outputs["split_1"]
+    classification_testing_data = split_task.outputs["split_2"]
+
+    # TensorFlow
+    tensorflow_network = create_fully_connected_tensorflow_network_op(
+        input_size=len(feature_columns),
+        # Optional:
+        hidden_layer_sizes=[10],
+        activation_name="elu",
+        output_activation_name="sigmoid",
+        # output_size=1,
+    ).outputs["model"]
+
+    tensorflow_model = train_model_using_Keras_on_CSV_op(
+        training_data=classification_training_data,
+        model=tensorflow_network,
+        label_column_name=classification_label_column,
+        # Optional:
+        loss_function_name="binary_crossentropy",
+        number_of_epochs=10,
+        #learning_rate=0.1,
+        #optimizer_name="Adadelta",
+        #optimizer_parameters={},
+        #batch_size=32,
+        #metric_names=["mean_absolute_error"],
+        #random_seed=0,
+    ).outputs["trained_model"]
+
+    tensorflow_predictions = predict_with_TensorFlow_model_on_CSV_data_op(
+        dataset=classification_testing_data,
+        model=tensorflow_model,
+        # label_column_name needs to be set when doing prediction on a dataset that has labels
+        label_column_name=classification_label_column,
+        # Optional:
+        # batch_size=1000,
+    ).outputs["predictions"]
+
+    tensorflow_vertex_model_name = upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op(
+        model=tensorflow_model,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        tensorflow_vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=tensorflow_vertex_model_name,
+        ).outputs["endpoint_name"]
+
+    # PyTorch
+    pytorch_network = create_fully_connected_pytorch_network_op(
+        input_size=len(feature_columns),
+        # Optional:
+        hidden_layer_sizes=[10],
+        activation_name="elu",
+        output_activation_name="sigmoid",
+        # output_size=1,
+    ).outputs["model"]
+
+    pytorch_model = train_pytorch_model_from_csv_op(
+        model=pytorch_network,
+        training_data=classification_training_data,
+        label_column_name=classification_label_column,
+        loss_function_name="binary_cross_entropy",
+        # Optional:
+        #number_of_epochs=1,
+        #learning_rate=0.1,
+        #optimizer_name="Adadelta",
+        #optimizer_parameters={},
+        #batch_size=32,
+        #batch_log_interval=100,
+        #random_seed=0,
+    ).outputs["trained_model"]
+
+    pytorch_model_archive = create_pytorch_model_archive_with_base_handler_op(
+        model=pytorch_model,
+        # Optional:
+        # model_name="model",
+        # model_version="1.0",
+    ).outputs["Model archive"]
+
+    pytorch_vertex_model_name = upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op(
+        model_archive=pytorch_model_archive,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        pytorch_vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=pytorch_vertex_model_name,
+        ).outputs["endpoint_name"]
+
+    # XGBoost
+    xgboost_model = train_XGBoost_model_on_CSV_op(
+        training_data=classification_training_data,
+        label_column_name=classification_label_column,
+        objective="binary:logistic",
+        # Optional:
+        #starting_model=None,
+        #num_iterations=10,
+        #booster_params={},
+        #booster="gbtree",
+        #learning_rate=0.3,
+        #min_split_loss=0,
+        #max_depth=6,
+    ).outputs["model"]
+
+    # Predicting on the testing data
+    xgboost_predictions = xgboost_predict_on_CSV_op(
+        data=classification_testing_data,
+        model=xgboost_model,
+        # label_column needs to be set when doing prediction on a dataset that has labels
+        label_column_name=classification_label_column,
+    ).outputs["predictions"]
+
+    xgboost_vertex_model_name = upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op(
+        model=xgboost_model,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        xgboost_vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=xgboost_vertex_model_name,
+        ).outputs["endpoint_name"]
+
+    # Scikit-learn
+    sklearn_model = train_logistic_regression_model_using_scikit_learn_from_CSV_op(
+        dataset=classification_training_data,
+        label_column_name=classification_label_column,
+        # Optional:
+        #penalty="l2",
+        #solver="lbfgs",
+        #max_iterations=100,
+        #multi_class_mode="auto",
+        #random_seed=0,
+    ).outputs["model"]
+
+    sklearn_vertex_model_name = upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op(
+        model=sklearn_model,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        sklearn_vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=sklearn_vertex_model_name,
+        ).outputs["endpoint_name"]
+
+pipeline_func=train_tabular_classification_model_using_all_frameworks_pipeline
+
+# %% Pipeline submission
+if __name__ == '__main__':
+    from google.cloud import aiplatform
+    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()
@@ -0,0 +1,57 @@
+# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
+from kfp import components
+
+# %% Loading components
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+train_linear_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_linear_regression_model/from_CSV/component.yaml")
+upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")
+
+# %% Pipeline definition
+def train_tabular_regression_linear_model_using_Scikit_learn_pipeline():
+    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
+    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
+    label_column = "tips"
+    all_columns = [label_column] + feature_columns
+    # Deploying the model might incur additional costs over time
+    deploy_model = False
+
+    training_data = download_from_gcs_op(
+        gcs_path=dataset_gcs_uri
+    ).outputs["Data"]
+
+    training_data = select_columns_using_Pandas_on_CSV_data_op(
+        table=training_data,
+        column_names=all_columns,
+    ).outputs["transformed_table"]
+
+    # Cleaning the NaN values.
+    training_data = fill_all_missing_values_using_Pandas_on_CSV_data_op(
+        table=training_data,
+        replacement_value="0",
+        #replacement_type_name="float",
+    ).outputs["transformed_table"]
+
+    model = train_linear_regression_model_using_scikit_learn_from_CSV_op(
+        dataset=training_data,
+        label_column_name=label_column,
+    ).outputs["model"]
+
+    vertex_model_name = upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op(
+        model=model,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        sklearn_vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=vertex_model_name,
+        ).outputs["endpoint_name"]
+
+pipeline_func = train_tabular_regression_linear_model_using_Scikit_learn_pipeline
+
+# %% Pipeline submission
+if __name__ == '__main__':
+    from google.cloud import aiplatform
+    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()
@@ -0,0 +1,85 @@
+# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
+from kfp import components
+
+# %% Loading components
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_fully_connected_network/component.yaml")
+train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
+create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
+upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")
+
+# %% Pipeline definition
+def train_tabular_regression_model_using_PyTorch_pipeline():
+    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
+    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
+    label_column = "tips"
+    all_columns = [label_column] + feature_columns
+    # Deploying the model might incur additional costs over time
+    deploy_model = False
+
+    training_data = download_from_gcs_op(
+        gcs_path=dataset_gcs_uri
+    ).outputs["Data"]
+
+    training_data = select_columns_using_Pandas_on_CSV_data_op(
+        table=training_data,
+        column_names=all_columns,
+    ).outputs["transformed_table"]
+
+    # Cleaning the NaN values.
+    training_data = fill_all_missing_values_using_Pandas_on_CSV_data_op(
+        table=training_data,
+        replacement_value="0",
+        #replacement_type_name="float",
+    ).outputs["transformed_table"]
+
+    network = create_fully_connected_pytorch_network_op(
+        input_size=len(feature_columns),
+        # Optional:
+        hidden_layer_sizes=[10],
+        activation_name="elu",
+        # output_activation_name=None,
+        # output_size=1,
+    ).outputs["model"]
+
+    model = train_pytorch_model_from_csv_op(
+        model=network,
+        training_data=training_data,
+        label_column_name=label_column,
+        # Optional:
+        #loss_function_name="mse_loss",
+        #number_of_epochs=1,
+        #learning_rate=0.1,
+        #optimizer_name="Adadelta",
+        #optimizer_parameters={},
+        #batch_size=32,
+        #batch_log_interval=100,
+        #random_seed=0,
+    ).outputs["trained_model"]
+
+    model_archive = create_pytorch_model_archive_with_base_handler_op(
+        model=model,
+        # Optional:
+        # model_name="model",
+        # model_version="1.0",
+    ).outputs["Model archive"]
+
+    vertex_model_name = upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op(
+        model_archive=model_archive,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=vertex_model_name,
+        ).outputs["endpoint_name"]
+
+pipeline_func=train_tabular_regression_model_using_PyTorch_pipeline
+
+# %% Pipeline submission
+if __name__ == '__main__':
+    from google.cloud import aiplatform
+    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()
@@ -0,0 +1,97 @@
+# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
+from kfp import components
+
+# %% Loading components
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
+create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Create_fully_connected_network/component.yaml")
+train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
+predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Predict/on_CSV/component.yaml")
+upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")
+
+# %% Pipeline definition
+def train_tabular_regression_model_using_Tensorflow_pipeline():
+    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
+    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
+    label_column = "tips"
+    training_set_fraction = 0.8
+    # Deploying the model might incur additional costs over time
+    deploy_model = False
+
+    all_columns = [label_column] + feature_columns
+
+    dataset = download_from_gcs_op(
+        gcs_path=dataset_gcs_uri
+    ).outputs["Data"]
+
+    dataset = select_columns_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        column_names=all_columns,
+    ).outputs["transformed_table"]
+
+    dataset = fill_all_missing_values_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        replacement_value="0",
+        # # Optional:
+        # column_names=None,  # =[...]
+    ).outputs["transformed_table"]
+
+    split_task = split_rows_into_subsets_op(
+        table=dataset,
+        fraction_1=training_set_fraction,
+    )
+    training_data = split_task.outputs["split_1"]
+    testing_data = split_task.outputs["split_2"]
+
+    network = create_fully_connected_tensorflow_network_op(
+        input_size=len(feature_columns),
+        # Optional:
+        hidden_layer_sizes=[10],
+        activation_name="elu",
+        # output_activation_name=None,
+        # output_size=1,
+    ).outputs["model"]
+
+    model = train_model_using_Keras_on_CSV_op(
+        training_data=training_data,
+        model=network,
+        label_column_name=label_column,
+        # Optional:
+        #loss_function_name="mean_squared_error",
+        number_of_epochs=10,
+        #learning_rate=0.1,
+        #optimizer_name="Adadelta",
+        #optimizer_parameters={},
+        #batch_size=32,
+        metric_names=["mean_absolute_error"],
+        #random_seed=0,
+    ).outputs["trained_model"]
+
+    predictions = predict_with_TensorFlow_model_on_CSV_data_op(
+        dataset=testing_data,
+        model=model,
+        # label_column_name needs to be set when doing prediction on a dataset that has labels
+        label_column_name=label_column,
+        # Optional:
+        # batch_size=1000,
+    ).outputs["predictions"]
+
+    vertex_model_name = upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op(
+        model=model,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=vertex_model_name,
+        ).outputs["endpoint_name"]
+
+pipeline_func=train_tabular_regression_model_using_Tensorflow_pipeline
+
+# %% Pipeline submission
+if __name__ == '__main__':
+    from google.cloud import aiplatform
+    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()
@@ -0,0 +1,85 @@
+# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
+from kfp import components
+
+# %% Loading components
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
+train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
+xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
+upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")
+
+# %% Pipeline definition
+def train_tabular_regression_model_using_XGBoost_pipeline():
+    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
+    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
+    label_column = "tips"
+    training_set_fraction = 0.8
+    # Deploying the model might incur additional costs over time
+    deploy_model = False
+
+    all_columns = [label_column] + feature_columns
+
+    dataset = download_from_gcs_op(
+        gcs_path=dataset_gcs_uri
+    ).outputs["Data"]
+
+    dataset = select_columns_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        column_names=all_columns,
+    ).outputs["transformed_table"]
+
+    dataset = fill_all_missing_values_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        replacement_value="0",
+        # # Optional:
+        # column_names=None,  # =[...]
+    ).outputs["transformed_table"]
+
+    split_task = split_rows_into_subsets_op(
+        table=dataset,
+        fraction_1=training_set_fraction,
+    )
+    training_data = split_task.outputs["split_1"]
+    testing_data = split_task.outputs["split_2"]
+
+    model = train_XGBoost_model_on_CSV_op(
+        training_data=training_data,
+        label_column_name=label_column,
+        # Optional:
+        #starting_model=None,
+        #num_iterations=10,
+        #booster_params={},
+        #objective="reg:squarederror",
+        #booster="gbtree",
+        #learning_rate=0.3,
+        #min_split_loss=0,
+        #max_depth=6,
+    ).outputs["model"]
+
+    # Predicting on the testing data
+    predictions = xgboost_predict_on_CSV_op(
+        data=testing_data,
+        model=model,
+        # label_column needs to be set when doing prediction on a dataset that has labels
+        label_column_name=label_column,
+    ).outputs["predictions"]
+
+    vertex_model_name = upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op(
+        model=model,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=vertex_model_name,
+        ).outputs["endpoint_name"]
+
+pipeline_func = train_tabular_regression_model_using_XGBoost_pipeline
+
+# %% Pipeline submission
+if __name__ == '__main__':
+    from google.cloud import aiplatform
+    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()
@@ -0,0 +1,208 @@
+# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
+from kfp import components
+
+# %% Loading components
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
+
+# TensorFlow
+create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Create_fully_connected_network/component.yaml")
+train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
+predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Predict/on_CSV/component.yaml")
+upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/component.yaml")
+
+# PyTorch
+create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_fully_connected_network/component.yaml")
+train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
+create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
+upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/component.yaml")
+
+# XGBoost
+train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
+xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
+upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
+
+# Scikit-learn
+train_linear_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_linear_regression_model/from_CSV/component.yaml")
+upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/component.yaml")
+
+# Vertex AI
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")
+
+# %% Pipeline definition
+def train_tabular_regression_model_using_all_frameworks_pipeline():
+    dataset_gcs_uri = "gs://ml-pipeline-dataset/Chicago_taxi_trips/chicago_taxi_trips_2019-01-01_-_2019-02-01_limit=10000.csv"
+    feature_columns = ["trip_seconds", "trip_miles", "pickup_community_area", "dropoff_community_area", "fare", "tolls", "extras"]  # Excluded "trip_total"
+    label_column = "tips"
+    training_set_fraction = 0.8
+    # Deploying the model might incur additional costs over time
+    deploy_model = False
+
+    all_columns = [label_column] + feature_columns
+
+    dataset = download_from_gcs_op(
+        gcs_path=dataset_gcs_uri
+    ).outputs["Data"]
+
+    dataset = select_columns_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        column_names=all_columns,
+    ).outputs["transformed_table"]
+
+    dataset = fill_all_missing_values_using_Pandas_on_CSV_data_op(
+        table=dataset,
+        replacement_value="0",
+        # # Optional:
+        # column_names=None,  # =[...]
+    ).outputs["transformed_table"]
+
+    split_task = split_rows_into_subsets_op(
+        table=dataset,
+        fraction_1=training_set_fraction,
+    )
+    training_data = split_task.outputs["split_1"]
+    testing_data = split_task.outputs["split_2"]
+
+    # TensorFlow
+    tensorflow_network = create_fully_connected_tensorflow_network_op(
+        input_size=len(feature_columns),
+        # Optional:
+        hidden_layer_sizes=[10],
+        activation_name="elu",
+        # output_activation_name=None,
+        # output_size=1,
+    ).outputs["model"]
+
+    tensorflow_model = train_model_using_Keras_on_CSV_op(
+        training_data=training_data,
+        model=tensorflow_network,
+        label_column_name=label_column,
+        # Optional:
+        #loss_function_name="mean_squared_error",
+        number_of_epochs=10,
+        #learning_rate=0.1,
+        #optimizer_name="Adadelta",
+        #optimizer_parameters={},
+        #batch_size=32,
+        metric_names=["mean_absolute_error"],
+        #random_seed=0,
+    ).outputs["trained_model"]
+
+    tensorflow_predictions = predict_with_TensorFlow_model_on_CSV_data_op(
+        dataset=testing_data,
+        model=tensorflow_model,
+        # label_column_name needs to be set when doing prediction on a dataset that has labels
+        label_column_name=label_column,
+        # Optional:
+        # batch_size=1000,
+    ).outputs["predictions"]
+
+    tensorflow_vertex_model_name = upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op(
+        model=tensorflow_model,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        tensorflow_vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=tensorflow_vertex_model_name,
+        ).outputs["endpoint_name"]
+
+    # PyTorch
+    pytorch_network = create_fully_connected_pytorch_network_op(
+        input_size=len(feature_columns),
+        # Optional:
+        hidden_layer_sizes=[10],
+        activation_name="elu",
+        # output_activation_name=None,
+        # output_size=1,
+    ).outputs["model"]
+
+    pytorch_model = train_pytorch_model_from_csv_op(
+        model=pytorch_network,
+        training_data=training_data,
+        label_column_name=label_column,
+        # Optional:
+        #loss_function_name="mse_loss",
+        #number_of_epochs=1,
+        #learning_rate=0.1,
+        #optimizer_name="Adadelta",
+        #optimizer_parameters={},
+        #batch_size=32,
+        #batch_log_interval=100,
+        #random_seed=0,
+    ).outputs["trained_model"]
+
+    pytorch_model_archive = create_pytorch_model_archive_with_base_handler_op(
+        model=pytorch_model,
+        # Optional:
+        # model_name="model",
+        # model_version="1.0",
+    ).outputs["Model archive"]
+
+    pytorch_vertex_model_name = upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op(
+        model_archive=pytorch_model_archive,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        pytorch_vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=pytorch_vertex_model_name,
+        ).outputs["endpoint_name"]
+
+    # XGBoost
+    xgboost_model = train_XGBoost_model_on_CSV_op(
+        training_data=training_data,
+        label_column_name=label_column,
+        # Optional:
+        #starting_model=None,
+        #num_iterations=10,
+        #booster_params={},
+        #objective="reg:squarederror",
+        #booster="gbtree",
+        #learning_rate=0.3,
+        #min_split_loss=0,
+        #max_depth=6,
+    ).outputs["model"]
+
+    # Predicting on the testing data
+    xgboost_predictions = xgboost_predict_on_CSV_op(
+        data=testing_data,
+        model=xgboost_model,
+        # label_column needs to be set when doing prediction on a dataset that has labels
+        label_column_name=label_column,
+    ).outputs["predictions"]
+
+    xgboost_vertex_model_name = upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op(
+        model=xgboost_model,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        xgboost_vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=xgboost_vertex_model_name,
+        ).outputs["endpoint_name"]
+
+    # Scikit-learn
+    sklearn_model = train_linear_regression_model_using_scikit_learn_from_CSV_op(
+        dataset=training_data,
+        label_column_name=label_column,
+    ).outputs["model"]
+
+    sklearn_vertex_model_name = upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op(
+        model=sklearn_model,
+    ).outputs["model_name"]
+
+    # Deploying the model might incur additional costs over time
+    if deploy_model:
+        sklearn_vertex_endpoint_name = deploy_model_to_endpoint_op(
+            model_name=sklearn_vertex_model_name,
+        ).outputs["endpoint_name"]
+
+pipeline_func=train_tabular_regression_model_using_all_frameworks_pipeline
+
+# %% Pipeline submission
+if __name__ == '__main__':
+    from google.cloud import aiplatform
+    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()
@@ -65,7 +65,7 @@
        "\n",
        "This tutorial demonstrates how to use Vertex AI Pipelines with pre-built Google Cloud Pipeline Components for custom training.\n",
        "\n",
-        "Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) and [Vertex AI Training components](https://cloud.google.com/vertex-ai/docs/training/create-training-pipeline)."
+        "Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) and [Custom training components](https://cloud.google.com/vertex-ai/docs/training/create-training-pipeline)."
      ]
    },
    {
@@ -784,7 +784,7 @@
      "outputs": [],
      "source": [
        "# Setup\n",
-        "SPARK_VERSION = \"3.1.2\"\n",
+        "DATAPROC_RUNTIME_VERSION = \"1.1.3\"\n",
        "SRC = path(\"src\")\n",
        "BUILD_PATH = path(\"build\")\n",
        "DELIVERABLES = path(\"deliverables\")\n",
@@ -856,7 +856,7 @@
        "    HPT_BUNDLE_URI,\n",
        "]\n",
        "HPT_RUNTIME_PROPERTIES = {\n",
-        "    \"spark.jars.packages\": \"ml.combust.mleap:mleap-spark-base_2.12:0.20.0,ml.combust.mleap:mleap-spark_2.12:0.20.0\"\n",
+        "    \"spark.jars.packages\": \"ml.combust.mleap:mleap-spark-base_2.12:0.21.1,ml.combust.mleap:mleap-spark_2.12:0.21.1\"\n",
        "}\n",
        "\n",
        "# Experiment\n",
@@ -2465,6 +2465,7 @@
        "    deploy_model: bool = DEPLOY_MODEL,\n",
        "    artifact_uri: str = ARTIFACT_URI,\n",
        "    serving_image_uri: str = SERVING_IMAGE_URI,\n",
+        "    dataproc_runtime_version: str = DATAPROC_RUNTIME_VERSION,\n",
        "):\n",
        "    from google_cloud_pipeline_components.v1.dataproc import \\\n",
        "        DataprocPySparkBatchOp\n",
@@ -2485,6 +2486,7 @@
        "        main_python_file_uri=preprocessing_main_python_file_uri,\n",
        "        args=build_preprocessing_args_op.output,\n",
        "        subnetwork_uri=subnetwork_uri,\n",
+        "        runtime_config_version=dataproc_runtime_version,\n",
        "    ).after(build_preprocessing_args_op)\n",
        "\n",
        "    # build training data args\n",
@@ -2502,6 +2504,7 @@
        "        main_python_file_uri=training_main_python_file_uri,\n",
        "        args=build_training_args_op.output,\n",
        "        subnetwork_uri=subnetwork_uri,\n",
+        "        runtime_config_version=dataproc_runtime_version,\n",
        "    ).after(build_training_args_op)\n",
        "\n",
        "    evaluate_model_op = evaluate_model(metrics_uri=metrics_path).after(\n",
@@ -2530,6 +2533,8 @@
        "            args=build_hpt_args_op.output,\n",
        "            runtime_config_properties=HPT_RUNTIME_PROPERTIES,\n",
        "            subnetwork_uri=subnetwork_uri,\n",
+        "            # TODO: change to Dataproc Serverless Runtime 1.1.x image when MLeap supports Spark 3.3\n",
+        "            runtime_config_version=\"1.0.29\",\n",
        "        ).after(model_training_op)\n",
        "\n",
        "        # evaluate condition to upload and deploy model to Vertex AI\n",
@@ -63,7 +63,7 @@
        "\n",
        "This notebook shows how to use the components defined in [`google_cloud_pipeline_components`](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud) to build a [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines) workflow that trains a [custom model](https://cloud.google.com/vertex-ai/docs/training/containers-overview), uploads the model as a `Model` resource, creates an `Endpoint` resource, and deploys the `Model` resource to the `Endpoint` resource.\n",
        "\n",
-        "Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) and [Vertex AI Training components](https://cloud.google.com/vertex-ai/docs/pipelines/customjob-component)."
+        "Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) and [Custom training components](https://cloud.google.com/vertex-ai/docs/pipelines/customjob-component)."
      ]
    },
    {
@@ -94,7 +94,7 @@
        "- Compile the KFP pipeline.\n",
        "- Execute the KFP pipeline using `Vertex AI Pipelines`\n",
        "\n",
-        "The components are [documented here](https://google-cloud-pipeline-components.readthedocs.io/en/latest/google_cloud_pipeline_components.aiplatform.html#module-google_cloud_pipeline_components.aiplatform).\n",
+        "The components are [documented here](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.39/google_cloud_pipeline_components.aiplatform.html).\n",
        "(From that page, see also the `CustomPythonPackageTrainingJobRunOp` and `CustomContainerTrainingJobRunOp` components, which similarly run 'custom' training, but as with the related `google.cloud.aiplatform.CustomContainerTrainingJob` and `google.cloud.aiplatform.CustomPythonPackageTrainingJob` methods from the [Vertex AI SDK](https://googleapis.dev/python/aiplatform/latest/aiplatform.html), also upload the trained model)."
      ]
    },
@@ -1,20 +0,0 @@
-
-[Training, tuning and deploying a PyTorch text sentiment classification model on Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pytorch/pytorch-text-sentiment-classification-custom-train-deploy.ipynb)
-
-```
-Learn to build, train, tune and deploy a PyTorch model on [Vertex AI](https://cloud.
-
-The steps performed include:
-
- Create training package for the text classification model.
- Train the model with custom training on Vertex AI.
- Check the created model artifacts.
- Create a custom container for predictions.
- Deploy the trained model to a Vertex AI Endpoint using the custom container for predictions.
- Send online prediction requests to the deployed model and validate.
- Clean up the resources created in this notebook.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
@@ -64,7 +64,7 @@
        "\n",
        "Note: you may incur charges for training, prediction, storage or usage of other GCP products in connection with testing this SDK.\n",
        "\n",
-        "Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/tutorials/video-classification-automl/training)."
+        "Learn more about [Classification for video data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_videos)."
      ]
    },
    {
@@ -773,7 +773,19 @@
        "\n",
        "The `run` method when completed returns the `Model` resource.\n",
        "\n",
-        "The execution of the training pipeline can take over 2 hours to complete."
+        "The execution of the training pipeline can take over 24 hours to complete."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8f135100f5d9"
+      },
+      "outputs": [],
+      "source": [
+        "if os.getenv(\"IS_TESTING\"):\n",
+        "    sys.exit(0)"
      ]
    },
    {
@@ -64,7 +64,7 @@
        "\n",
        "Note: You may incur charges for training, prediction, storage or usage of other GCP products in connection with testing this SDK.\n",
        "\n",
-        "Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
@@ -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."
@@ -8,7 +8,7 @@
      },
      "outputs": [],
      "source": [
-        "# Copyright 2022 Google LLC\n",
+        "# 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",
@@ -904,23 +904,8 @@
      "source": [
        "### Create the training job\n",
        "\n",
-        "Use the `get_prophet_train_pipeline_and_parameters` function to configure your training job. Here, you can configure the [data splitting](https://cloud.google.com/vertex-ai/docs/tabular-data/data-splits#forecasting) and [windowing](https://cloud.google.com/vertex-ai/docs/tabular-data/bp-tabular#context-window) strategies, hyperparameter tuning trials, and the Dataflow job for parallelizing model training.\n",
+        "Use the `get_prophet_train_pipeline_and_parameters` function to configure your training job.\n",
        "\n",
-        "The following parameters are used to configure splitting:\n",
-        "-  `predefined_split_key` (str): The predefined_split column name. Other splitting options include fractional splitting on a time column.\n",
-        "- `training_fraction` (float): The percentage of the data that belongs to the training set. Set this value if you are using a fraction split or a timestamp split.\n",
-        "- `training_fraction` (float): The percentage of the data that belongs to the validation set. Set this value if you are using a fraction split or a timestamp split.\n",
-        "- `training_fraction` (float): The percentage of the data that belongs to the test set. Set this value if you are using a fraction split or a timestamp split.\n",
-        "- `timestamp_split_key` (str): The name of the column containing the timestamps for the data split. Set this value if you are using a timestamp split.\n",
-        "\n",
-        "The following parameters are used to configure windowing:\n",
-        "- `window_column` (str): Name of the column that should be used to filter input rows. The column should contain either booleans or string booleans; if the value of the row is True, generate a sliding window from that row.\n",
-        "-  `window_stride_length` (int): Step length used to generate input examples. Every window_stride_length rows is used to generate a sliding window. Other windowing options include using a predefined column or setting a maximum number to downsample to.\n",
-        "- `window_max_count` (int): Number of rows that should be used to generate input examples. If the total row count is larger than this number, the input data will be randomly sampled to hit the count.\n",
-        "\n",
-        "For more information on how the pipeline parameters relate to splitting and windowing, see the [Prophet on Vertex AI documentation](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting-prophet).\n",
-        "\n",
-        "Other parameters used by the training job below include:\n",
        "-  `project` (str): The GCP project that runs the pipeline components.\n",
        "-  `location` (str): The GCP region for Vertex AI.\n",
        "-  `root_dir` (str): The Cloud Storage location to store the output.\n",
@@ -931,10 +916,16 @@
        "-  `optimization_objective` (str): Optimization objective for tuning. Supported metrics come from Prophet's performance_metrics function. These are mse, rmse, mae, mape, mdape, smape, and coverage.\n",
        "-  `data_granularity_unit` (str): String representing the units of time for the time column.\n",
        "- `data_source_bigquery_table_path` (str): The BigQuery table path of format bq://bq_project.bq_dataset.bq_table\n",
-        "- `max_num_trials` (int): Maximum number of tuning trials to perform per time series.\n",
-        "-  `trainer_dataflow_machine_type` (str): The dataflow machine type used for training.\n",
-        "- `trainer_dataflow_max_num_workers` (int): The maximum number of Dataflow workers used for training.\n",
        "- `dataflow_service_account` (str): Custom service account to run dataflow jobs. The dataflow job can also be configured to use private IPs and a specific VPC subnet.\n",
+        "- `max_num_trials` (int): Maximum number of tuning trials to perform per time series.\n",
+        "-  `predefined_split_key` (str): The name of the predefined_split column. Vertex AI splits the data using the TRAIN, VALIDATE, or TEST values in this column. To learn more about data splits and for the full set of data split parameters, refer to the public documentation page [Forecasting with Prophet](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting-prophet).\n",
+        "-  `window_stride_length` (int): Step length used to generate input examples. Every window_stride_length rows is used to generate a sliding window. To learn more about windowing and for the full set of windowing parameters, refer to the public documentation page [Forecasting with Prophet](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting-prophet).\n",
+        "-  `trainer_dataflow_machine_type` (str): The Dataflow machine type used for training.\n",
+        "- `trainer_dataflow_max_num_workers` (int): The maximum number of Dataflow workers used for training.\n",
+        "- `evaluation_dataflow_machine_type` (int): The Dataflow machine type used for evaluation.\n",
+        "- `evaluation_dataflow_max_num_workers` (int): The max number of Dataflow workers used for evaluation.\n",
+        "\n",
+        "Prophet training jobs run on Dataflow, meaning there is an initial startup time of 5-7 minutes. Any runtime past this startup time can be improved by scaling vertically (ex. changing the machine type from `n1-standard-2` to `e2-highcpu-8`) or horizontally (ex. increasing the number of workers from `5` to `100`).\n",
        "\n",
        "For a full list of parameters, see the GCPC SDK [documentation](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.33/google_cloud_pipeline_components.experimental.automl.forecasting.html#google_cloud_pipeline_components.experimental.automl.forecasting.utils.get_prophet_train_pipeline_and_parameters).\n"
      ]
@@ -974,7 +965,9 @@
        "    window_stride_length=window_stride_length,\n",
        "    max_num_trials=2,\n",
        "    trainer_dataflow_machine_type=\"n1-standard-2\",\n",
-        "    trainer_dataflow_max_num_workers=50,\n",
+        "    trainer_dataflow_max_num_workers=5,\n",
+        "    evaluation_dataflow_machine_type=\"n1-standard-1\",\n",
+        "    evaluation_dataflow_max_num_workers=1,\n",
        "    dataflow_service_account=SERVICE_ACCOUNT,\n",
        ")"
      ]
@@ -1118,7 +1111,7 @@
        "    data_source_bigquery_table_path=PREDICTION_DATASET_BQ_PATH,\n",
        "    bigquery_destination_uri=dataset_path,\n",
        "    machine_type=\"n1-standard-2\",\n",
-        "    max_num_workers=50,\n",
+        "    max_num_workers=5,\n",
        ")"
      ]
    },
@@ -91,7 +91,7 @@
        "With Vertex AI TensorBoard, you can track, visualize, and compare\n",
        "ML experiments and share them with your team.\n",
        "\n",
-        "Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview) and [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
@@ -91,7 +91,7 @@
        "With Vertex AI TensorBoard, you can track, visualize, and compare\n",
        "ML experiments and share them with your team.\n",
        "\n",
-        "Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview) and [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
@@ -72,7 +72,7 @@
        "\n",
        "This tutorial demonstrates how to create a multi-node, distributed image classification using PyTorch on Vertex AI SDK with GPU. This can help your training job scale to handle large amounts of data.\n",
        "\n",
-        "Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
@@ -33,18 +33,18 @@
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pytorch/pytorch-text-sentiment-classification-custom-train-deploy.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/pytorch-text-sentiment-classification-custom-train-deploy.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/pytorch/pytorch-text-sentiment-classification-custom-train-deploy.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/pytorch-text-sentiment-classification-custom-train-deploy.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/pytorch/pytorch-text-sentiment-classification-custom-train-deploy.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/training/pytorch-text-sentiment-classification-custom-train-deploy.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",
@@ -65,7 +65,7 @@
        "\n",
        "You can find more details about the model at [Hugging Face Hub](https://huggingface.co/bert-base-cased). For more notebooks with the state of the art PyTorch/Tensorflow/JAX, you can explore [Hugging FaceNotebooks](https://huggingface.co/transformers/notebooks.html).\n",
        "\n",
-        "Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
@@ -1301,12 +1301,26 @@
        "- `machine_type`: Mahcine type on which the job needs to run.\n",
        "- `accelerator_type`: Hardware accelerator type for running the job. One of _ACCELERATOR_TYPE_UNSPECIFIED_,\n",
        "        _NVIDIA_TESLA_K80_, _NVIDIA_TESLA_P100_, _NVIDIA_TESLA_V100_, _NVIDIA_TESLA_P4_,\n",
-        "        _NVIDIA_TESLA_T4_.\n",
+        "        _NVIDIA_TESLA_T4_, _NVIDIA_TELSA_A100_\n",
        "- `accelerator_count`: The number of accelerators to attach to a worker replica.\n",
        "- `replica_count`: The number of worker replicas.\n",
        "- `args`: Command line arguments to be passed to the Python script.\n",
        "\n",
-        "Learn more about Vertex AI's [Custom Python-Package Trainining](https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.CustomPythonPackageTrainingJob)."
+        "Learn more about Vertex AI's [Custom Python-Package Trainining](https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.CustomPythonPackageTrainingJob).\n",
+        "\n",
+        "*Note*: This training job may take over 24 hours."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b3709beb73e2"
+      },
+      "outputs": [],
+      "source": [
+        "if os.getenv(\"IS_TESTING\"):\n",
+        "    sys.exit(0)"
      ]
    },
    {
@@ -63,7 +63,7 @@
        "\n",
        "This tutorial shows you how to create a distributed custom training job on Vertex AI that can handle large amounts of training data.\n",
        "\n",
-        "Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
@@ -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."
      ]
    },
    {
@@ -887,6 +905,8 @@
        "    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",
        ")"
      ]
    },
@@ -908,7 +928,7 @@
        "id": "tVktIbToRpmR"
      },
      "source": [
-        "### Access the Dask dashboard"
+        "### 2. Run a CustomContainerTraining Job with gcloud"
      ]
    },
    {
@@ -917,7 +937,7 @@
        "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 +995,16 @@
    {
      "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\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."
      ]
    },
    {
@@ -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"
      ]
    },
@@ -63,7 +63,7 @@
        "\n",
        "This tutorial shows you how to build, deploy, and analyze predictions from a simple [random forest](https://en.wikipedia.org/wiki/Random_forest) model using tools like scikit-learn, Vertex AI, and the [What-IF Tool (WIT)](https://cloud.google.com/ai-platform/prediction/docs/using-what-if-tool) on a synthetic fraud transaction dataset to solve a financial fraud detection problem.\n",
        "\n",
-        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
+        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
Author	SHA1	Message	Date
reznitskiiandGitHub	5d6aa4479d	Update automl_video_classification_model_evaluation.ipynb	2023-03-02 15:11:07 -05:00
Andrew FerlitschandGitHub	974610a555	Merge pull request #1562 from GoogleCloudPlatform/reznitskii-patch-8 Updated link	2023-03-02 08:25:51 +00:00
Andrew FerlitschandGitHub	d78574e640	Merge pull request #1565 from ninataneja/dask-sdk Add SDK support for Dask dashboard to Training	2023-03-02 08:25:22 +00:00
Nina Taneja	ff367ae9f5	Fixed formatting problem	2023-03-02 01:34:46 +00:00
Nina Taneja	40b5e74645	Addressed formatting and wording changes	2023-03-02 01:26:52 +00:00
Nina Taneja	dee509e8d7	Add SDK support for Dask dashboard to Training	2023-03-01 23:39:06 +00:00
reznitskiiandGitHub	c74a44a4a8	Update sdk_automl_tabular_classification_online_explain.ipynb	2023-03-01 17:22:30 -05:00
Andrew FerlitschandGitHub	367c985642	Merge pull request #1560 from GoogleCloudPlatform/autoindex_tensorboard fix: tensorboard branding	2023-03-01 22:03:24 +00:00
Andrew Ferlitsch	9a914a5af4	fix: tensorboard branding	2023-03-01 21:46:57 +00:00
Andrew FerlitschandGitHub	6daf663a69	Merge pull request #1557 from GoogleCloudPlatform/custom_eval feat: notebook for custom evaluations	2023-03-01 20:15:56 +00:00
Andrew Ferlitsch	45a65f1db8	fix: install issue	2023-03-01 20:05:54 +00:00
Andrew Ferlitsch	240c291728	feat: add eval on versioned model	2023-03-01 19:21:21 +00:00
gericdongandGitHub	4aec14c576	Merge pull request #1558 from GoogleCloudPlatform/andrewferlitsch-patch-4 tmp file added by mistake	2023-03-01 18:34:45 +00:00
Andrew FerlitschandGitHub	f7d4d3a9c3	tmp file added by mistake	2023-03-01 10:08:20 -08:00
Andrew Ferlitsch	3c64a8aa58	fix: review nits	2023-03-01 18:04:34 +00:00
Andrew Ferlitsch	5f692ea299	fix: missing installs	2023-03-01 16:06:54 +00:00
Andrew FerlitschandGitHub	ab1e97ac18	Merge pull request #1553 from GoogleCloudPlatform/autoindex_max_3 fix: 5 branding bugs	2023-03-01 16:04:23 +00:00
Andrew FerlitschandGitHub	78630ae2c0	Merge pull request #1552 from GoogleCloudPlatform/reznitskii-patch-3 Fixed typo	2023-03-01 16:03:51 +00:00
Andrew Ferlitsch	0f48628782	feat: notebook for custom evaluations	2023-03-01 00:52:20 +00:00
Andrew Ferlitsch	93e9fbac92	fix: 5 branding bugs	2023-02-28 20:42:03 +00:00
Andrew FerlitschandGitHub	231b2ef02b	Merge pull request #1556 from GoogleCloudPlatform/reznitskii-patch-6 Fixed typo	2023-02-28 20:40:04 +00:00
Andrew FerlitschandGitHub	7132c12831	Merge pull request #1555 from GoogleCloudPlatform/reznitskii-patch-5 Fixed typos	2023-02-28 20:31:30 +00:00
reznitskiiandGitHub	2b547e8279	Update forecasting-retail-demand.ipynb	2023-02-28 15:24:25 -05:00
reznitskiiandGitHub	8b84524244	Update ai-explanations-tabnet-algorithm.ipynb	2023-02-28 15:21:53 -05:00
Andrew Ferlitsch	60fe1bd2c6	fix: 5 branding bugs	2023-02-28 20:16:57 +00:00
reznitskiiandGitHub	0edea80ffa	Update custom_tabular_regression_model_evaluation.ipynb	2023-02-28 15:02:47 -05:00
Yvonne LiandGitHub	493e50a999	Merge pull request #1550 from GoogleCloudPlatform/autoindex_max_2 fix: extra period in link	2023-02-28 19:50:08 +00:00
Andrew Ferlitsch	8440e7f164	fix: extra period in link	2023-02-28 19:44:33 +00:00
gericdongandGitHub	906aa91fe7	Merge pull request #1549 from GoogleCloudPlatform/mv_pytorch fix: reorg	2023-02-28 18:41:15 +00:00
gericdongandGitHub	34e282172f	Merge pull request #1548 from GoogleCloudPlatform/rm_pytorch_folder fix: reorg	2023-02-28 18:29:13 +00:00
Andrew Ferlitsch	bddf642b58	fix: reorg	2023-02-28 18:24:55 +00:00
Andrew Ferlitsch	25b89c497f	fix: reorg	2023-02-28 18:19:55 +00:00
Andrew FerlitschandGitHub	23a51dbcaa	Merge pull request #1526 from GoogleCloudPlatform/doc_tag_1 update tag/linkback #1 AutoML Video	2023-02-28 18:15:56 +00:00
Andrew FerlitschandGitHub	26761198d7	Merge pull request #1547 from GoogleCloudPlatform/autoindex_max_1 fix: web index tune	2023-02-28 17:39:34 +00:00
Andrew Ferlitsch	5b4e88e791	fix: web index tune	2023-02-28 17:29:45 +00:00
Andrew Ferlitsch	83cc796687	fix: workaround for running > 24hrs	2023-02-28 17:05:34 +00:00
Andrew FerlitschandGitHub	3e29cc62c7	Merge pull request #1536 from GoogleCloudPlatform/doc_tag_11 update tag/linkback #11 AutoML Video	2023-02-28 17:02:09 +00:00
Andrew FerlitschandGitHub	2b1bb77f4b	Merge pull request #1535 from GoogleCloudPlatform/doc_tag_10 update tag/linkback #10 AutoMLVideo	2023-02-28 16:55:55 +00:00
Andrew Ferlitsch	7144b87f01	fix: workaround for running > 24hrs	2023-02-28 16:50:51 +00:00
Andrew Ferlitsch	ebf4d6d8be	fix: workaround for running > 24hrs	2023-02-28 16:47:14 +00:00
Andrew FerlitschandGitHub	2ccd913e2e	Merge pull request #1528 from GoogleCloudPlatform/doc_tag_3 update tag/linkback #3 AutoML Video	2023-02-28 16:45:02 +00:00
Andrew Ferlitsch	d777dd8625	fix: workaround for running > 24hrs	2023-02-28 16:32:16 +00:00
Andrew FerlitschandGitHub	d912d3d4d8	Merge pull request #1545 from GoogleCloudPlatform/reznitskii-patch-1 Fixed typo	2023-02-28 02:29:29 +00:00
reznitskiiandGitHub	bafe623590	Update automl_tabular_regression_model_evaluation.ipynb	2023-02-27 17:34:34 -05:00
gericdongandGitHub	152077a823	Merge pull request #1543 from GoogleCloudPlatform/guidelines feat: add authoring guidelines	2023-02-27 20:43:43 +00:00
Andrew Ferlitsch	81c94f9711	feat: add authoring guidelines	2023-02-27 19:53:10 +00:00
Andrew FerlitschandGitHub	897e8e3e47	Merge pull request #1541 from GoogleCloudPlatform/template_linkback fix: add tag/linkback	2023-02-27 18:06:24 +00:00
Andrew Ferlitsch	ddc125da69	fix: smaller dataset	2023-02-27 16:16:13 +00:00
Eric SchmidtandGitHub	abb66ade41	Merge pull request #1542 from gericdong/b270683209 fix: bad links in notebook	2023-02-24 17:06:07 +00:00
gericdong	2915824641	Lint	2023-02-24 09:03:19 -05:00
gericdong	44447637bd	fixed bad links	2023-02-24 08:59:24 -05:00
Andrew FerlitschandGitHub	6a8345446d	Merge pull request #1504 from reznitskii/b267661933-2 Replaced Vertex AI Training linkbacks with Custom training	2023-02-24 02:13:42 +00:00
Andrew Ferlitsch	27a7b8b1da	fix: add tag/linkback	2023-02-24 00:22:18 +00:00
Andrew FerlitschandGitHub	04471e7104	Merge pull request #1540 from GoogleCloudPlatform/issue_1522 fix: link	2023-02-23 17:43:06 +00:00
Andrew FerlitschandGitHub	e78ea4e805	Merge pull request #1538 from wintwoo/dataproc Specify Dataproc Serverless Runtime version to use for batch workloads.	2023-02-23 04:14:06 +00:00
Andrew Ferlitsch	7487a14783	fix: link	2023-02-22 23:07:37 +00:00
Andrew FerlitschandGitHub	757d0c5087	change copyright back to 2022. Policy is year is the year first authored	2023-02-22 15:00:49 -08:00
Andrew FerlitschandGitHub	60cb93a9a9	Delete pytorch-text-sentiment-classification-custom-train-deploy.ipynb	2023-02-22 14:43:12 -08:00
Andrew FerlitschandGitHub	0fb72773f4	Merge pull request #1533 from GoogleCloudPlatform/doc_tag_8 update tag/linkback #8	2023-02-22 22:21:16 +00:00
Andrew FerlitschandGitHub	5d2fc74f78	Merge pull request #1532 from GoogleCloudPlatform/doc_tag_7 update tag/linkback #7	2023-02-22 22:20:58 +00:00
Andrew FerlitschandGitHub	1cc26481b2	Merge pull request #1531 from GoogleCloudPlatform/doc_tag_6 update tag/linkback #6	2023-02-22 22:20:50 +00:00
Andrew FerlitschandGitHub	85b0346eab	Merge pull request #1530 from GoogleCloudPlatform/doc_tag_5 update tag/linkback #5	2023-02-22 22:20:24 +00:00
Andrew FerlitschandGitHub	910cbacb54	Merge pull request #1529 from GoogleCloudPlatform/doc_tag_4 update tag/linkback #4	2023-02-22 22:19:44 +00:00
Andrew FerlitschandGitHub	c7206e3cc8	Merge pull request #1527 from GoogleCloudPlatform/doc_tag_2 update tag/linkback #2	2023-02-22 21:46:40 +00:00
Win Woo	12d721d33e	Specify Dataproc runtime versions to use for batch jobs	2023-02-22 02:46:00 +00:00
Andrew FerlitschandGitHub	16df2216d1	Merge pull request #1525 from GoogleCloudPlatform/triton_ensenble_2 feat: triton ensemble	2023-02-21 21:32:16 +00:00
Andrew Ferlitsch	fb26b7213c	fix: links	2023-02-21 21:30:12 +00:00
Andrew Ferlitsch	d90f52e122	fix: links	2023-02-21 19:47:23 +00:00
Andrew Ferlitsch	652f34b814	update tag/linkback	2023-02-21 18:37:09 +00:00
Andrew Ferlitsch	c84f8738f8	update tag/linkback	2023-02-21 18:16:13 +00:00
Andrew Ferlitsch	9d72bbf237	update tag/linkback	2023-02-21 18:02:11 +00:00
Andrew Ferlitsch	85df50d06f	update tag/linkback	2023-02-21 17:39:51 +00:00
Andrew Ferlitsch	9ab6cddade	update tag/linkback	2023-02-21 17:33:56 +00:00
Andrew Ferlitsch	02ffffaeb7	update tag/linkback	2023-02-21 17:30:03 +00:00
Andrew Ferlitsch	dfc5635d34	update tag/linkback	2023-02-21 17:25:40 +00:00
Andrew Ferlitsch	90b686cf69	update tag/linkback	2023-02-21 17:02:33 +00:00
Andrew Ferlitsch	ad63153778	update tag/linkback	2023-02-21 16:58:56 +00:00
Andrew Ferlitsch	7858f8644e	update tag/linkback	2023-02-21 16:55:10 +00:00
Andrew Ferlitsch	3df4098364	update tag/linkback	2023-02-21 16:46:03 +00:00
Andrew Ferlitsch	ae3c1877e4	update tag/linkback	2023-02-21 16:41:17 +00:00
Andrew FerlitschandGitHub	2798ab1e02	set timeout	2023-02-21 08:33:40 -08:00
Andrew FerlitschandGitHub	7bd2c567a1	Merge pull request #1490 from GoogleCloudPlatform/imkc--matching-engine-embedding-tweak Fixed typo in sdk_matching_engine_for_indexing.ipynb	2023-02-18 00:48:17 +00:00
Andrew Ferlitsch	56503cce6a	feat: triton ensemble	2023-02-17 22:10:20 +00:00
Andrew FerlitschandGitHub	a5a9f53a32	Merge pull request #1515 from junyanxu/add_experimental_info_to_automl_image_montioring_notebook Add experimental information to automl image classifcation monitoring…	2023-02-17 15:52:07 +00:00
Junyan Xu	469e8436e9	Format the automl image online for model monitoring	2023-02-16 22:37:50 +00:00
Junyan Xu	e9f9a5c29b	Merge branch 'add_experimental_info_to_automl_image_montioring_notebook' of https://github.com/junyanxu/vertex-ai-samples into add_experimental_info_to_automl_image_montioring_notebook	2023-02-16 17:19:49 +00:00
Junyan Xu	102400a67f	update online pip install package	2023-02-16 17:19:00 +00:00
Andrew FerlitschandGitHub	0ae53f47d4	Update get_started_with_model_monitoring_automl_image_batch.ipynb	2023-02-16 08:27:40 -08:00
Eric SchmidtandGitHub	ff3e0b4784	Merge pull request #1519 from GoogleCloudPlatform/autoindex_official_6 tune: web index	2023-02-15 21:03:10 +00:00
Andrew Ferlitsch	0f4546c18c	tune: web index	2023-02-15 20:56:47 +00:00
Andrew FerlitschandGitHub	ee381e15f5	Update get_started_with_model_monitoring_automl_image_batch.ipynb	2023-02-15 12:13:36 -08:00
Andrew FerlitschandGitHub	4ddeeb290d	Merge pull request #1295 from Ark-kun/Train_tabular_models Train tabular models with many frameworks and import to Vertex AI using Pipelines	2023-02-15 19:54:11 +00:00
Andrew FerlitschandGitHub	7e99b07440	Merge branch 'main' into Train_tabular_models	2023-02-15 11:53:17 -08:00
Andrew FerlitschandGitHub	bc1ecd6260	Merge pull request #1334 from renovate-bot/renovate/isort-5.x chore(deps): update dependency isort to v5.12.0	2023-02-15 19:49:21 +00:00
Andrew FerlitschandGitHub	f662ffb3e1	Merge pull request #1328 from renovate-bot/renovate/black-22.x chore(deps): update dependency black to v22.12.0	2023-02-15 19:48:46 +00:00
Andrew FerlitschandGitHub	c40c0e5247	Merge pull request #1227 from sudarshan-SpringML/auto_tab_on_vertex_pipeline Update the file automl_tabular_on_vertex_pipelines	2023-02-15 19:32:19 +00:00
Eric SchmidtandGitHub	bd3a0f5af0	Merge pull request #1518 from GoogleCloudPlatform/issue_265061259 fix: issue	2023-02-15 17:46:37 +00:00
Andrew Ferlitsch	99f318333b	fix: issue	2023-02-15 17:10:28 +00:00
Andrew FerlitschandGitHub	11e7ba47a4	Merge pull request #1470 from GoogleCloudPlatform/dependabot/pip/community-content/pytorch_image_classification_single_gpu_with_vertex_sdk_and_torchserve/trainer/torch-1.13.1 Build(deps): Bump torch from 1.8.1 to 1.13.1 in /community-content/pytorch_image_classification_single_gpu_with_vertex_sdk_and_torchserve/trainer	2023-02-15 16:54:21 +00:00
Andrew FerlitschandGitHub	399427c2de	Merge pull request #1495 from TheMichaelHu/mh-prophet Reduce cost of running prophet notebook	2023-02-15 15:43:17 +00:00
gericdongandGitHub	0f116ab253	Merge pull request #1517 from GoogleCloudPlatform/stable-diffusion-fixes chore: revisions to Stable Diffusion and TorchServe nb	2023-02-15 13:33:54 +00:00
Michael Hu	304000d719	use n1-standard-2s	2023-02-14 22:18:13 -05:00
Eric Schmidt	36df462615	chore: revisions to Stable Diffusion and TorchServe nb	2023-02-15 02:32:02 +00:00
Andrew FerlitschandGitHub	909f771bfd	Merge pull request #1514 from abcdefgs0324/pytorch_ga Update wording for pre-built pytorch images on Vertex Prediction.	2023-02-14 17:07:03 +00:00
Eric SchmidtandGitHub	e9bef3542d	Merge pull request #1510 from GoogleCloudPlatform/stable-diffusion-try2 feat: adds stable diffusion notebook with PyTorch serving	2023-02-13 20:33:24 +00:00
Junyan Xu	c51a82febb	format notebook	2023-02-10 19:15:15 +00:00
Junyan Xu	8e6e4729aa	Add experimental information to automl image classifcation monitoring notebook	2023-02-10 18:48:09 +00:00
Chun-Hsiang Wang	52385a6071	samples: Updated wording and removed preview email.	2023-02-10 00:43:43 +00:00
Chun-Hsiang WangandGitHub	2365d733c4	Merge branch 'GoogleCloudPlatform:main' into pytorch_ga	2023-02-09 12:55:15 -08:00
Max Reznitskii	4e82877269	Reverting changes to failing notebooks	2023-02-09 16:42:44 +00:00
Andrew FerlitschandGitHub	6ef111144d	fix: lost updates (#1513 )	2023-02-08 18:35:05 -05:00
Max Reznitskii	c4f08589a9	Reverting changes to notebooks that fail tests	2023-02-03 19:21:48 +00:00
Max Reznitskii	bcb0b19dc4	Replaced Vertex AI Training linkbacks with Custom training	2023-02-02 23:04:08 +00:00
Michael Hu	e908774b5b	doc updates	2023-01-31 13:23:25 -05:00
Renovate Bot	c889a57c9e	chore(deps): update dependency isort to v5.12.0	2023-01-28 18:21:42 +00:00
Michael Hu	dbe5d61929	Reduce cost of running prophet notebook	2023-01-27 20:18:33 -05:00
ivanmkc@google.com	bcd86a3707	Fixed typo for index display name	2023-01-25 08:56:59 +09:00
ivanmkc@google.com	4e5c75fed6	Renamed json to jsonl	2023-01-24 21:08:08 +09:00
Ivan Cheung	b28f941abe	Fixed typo	2023-01-24 21:02:46 +09:00
dependabot[bot]andGitHub	6628866130	Build(deps): Bump torch Bumps [torch](https://github.com/pytorch/pytorch) from 1.8.1 to 1.13.1. - [Release notes](https://github.com/pytorch/pytorch/releases) - [Changelog](https://github.com/pytorch/pytorch/blob/master/RELEASE.md) - [Commits](https://github.com/pytorch/pytorch/compare/v1.8.1...v1.13.1) --- updated-dependencies: - dependency-name: torch dependency-type: direct:production ... Signed-off-by: dependabot[bot] <support@github.com>	2023-01-13 17:52:59 +00:00
wintwooandGitHub	f681879a8a	Merge branch 'GoogleCloudPlatform:main' into dataproc	2022-12-28 11:23:34 +11:00
Renovate Bot	e01174f169	chore(deps): update dependency black to v22.12.0	2022-12-09 16:42:13 +00:00
Win Woo	62c903f47d	Addressed PR feedback	2022-12-08 03:01:56 +00:00
Alexey Volkov	da77846b88	fix: Fixed the Scikit-learn components	2022-12-06 15:42:27 -08:00
Chun-Hsiang Wang	7c123e68d3	samples: Remove experimental text from Pytorch sample.	2022-11-30 07:46:08 +00:00
Win Woo	9f26e8385d	Show how to use experiments	2022-11-18 10:02:02 +00:00
uday kumarandGitHub	0fffaee6dc	Merge branch 'main' into auto_tab_on_vertex_pipeline	2022-11-14 15:42:17 +05:30
udaypunna	c34b7ab651	linter test	2022-11-14 07:47:02 +00:00
udaypunna	d57d9be9d4	downgraded gcpc version	2022-11-14 07:46:18 +00:00
udaypunna	88f3ecf567	downgraded gcpc version	2022-11-14 07:45:12 +00:00
uday kumarandGitHub	ecfbeaa8ce	Merge branch 'main' into auto_tab_on_vertex_pipeline	2022-11-10 15:46:16 +05:30
udaypunna	3e5a319455	ran linter test	2022-11-10 10:13:43 +00:00
udaypunna	f35ce3432a	gcpc version downgraded	2022-11-10 10:12:56 +00:00
udaypunna	2e9ff4a941	linter test	2022-11-09 14:40:27 +00:00
udaypunna	61fbd37fa4	service account and version chnages	2022-11-09 14:39:38 +00:00
udaypunna	474e4e602b	ran linter test	2022-11-09 14:14:47 +00:00
udaypunna	e409cf710b	added service account and downgraded gcpc version	2022-11-09 14:14:06 +00:00
udaypunna	e9bac09dce	ran linter test	2022-11-08 05:40:59 +00:00
udaypunna	b799aad08a	gcpc version and textual corrections	2022-11-08 05:40:09 +00:00
udaypunna	4d6bd1afff	ran linter test	2022-11-07 07:46:14 +00:00
udaypunna	b4040ff01d	textual corrections	2022-11-07 07:45:25 +00:00
Alexey Volkov	c29e4fde26	Train tabular models with many frameworks and import to Vertex AI using Pipelines These pipelines were previously in community content. We'd like to move them to the official folder. These pipelines are: * Working out of the box (code runs with zero modifications) * End-to-end (from nothing to a Vertex Model) * Feature multiple ML frameworks (TensorFlow, PyTorch, XGBoost, Scikit-learn) * Feature multiple training objectives: tabular classification and tabular regression The main files are Python-based pipeline code (`pipeline.py`).	2022-11-03 01:53:55 -07:00
udaypunna	c6fdad745f	linter test	2022-10-18 12:01:03 +00:00
udaypunna	3c60c2cb97	DAG issues	2022-10-18 12:00:18 +00:00
udaypunna	278a014817	ran linter test	2022-10-17 06:36:00 +00:00
udaypunna	89ec43c706	Cloud Storage bucket permission issues resolved	2022-10-17 06:35:26 +00:00
udaypunna	ba6be7ee99	Cloud Storage bucket permission issues resolved	2022-10-17 06:30:12 +00:00