Autoindex 1 (#1339)

* feat: autogen index

* feat: autogen index

* feat: autogen index

* feat: update indices

* fix: update official indices

* fix: update autogen index in official

* fix: update indexes

notebooks/notebook_template_review.py

@@ -401,6 +401,16 @@ class NoticesRule(NotebookRule):
         if cell['source'][0].startswith('This notebook'):
             notebook.pop()
         return True
+    
+class TestEnvRule(NotebookRule):
+    def validate(self, notebook: Notebook) -> bool:
+        """
+        Parse the (optional) test in which environment cell
+        """
+        cell = notebook.peek()
+        if cell['source'][0].startswith('**_NOTE_**: This notebook has been tested'):
+            notebook.pop()
+        return True
 
 
 class TitleRule(NotebookRule): 
@@ -1039,6 +1049,7 @@ copyright = CopyrightRule()
 notices = NoticesRule()
 title = TitleRule()
 links = LinksRule()
+testenv = TestEnvRule()
 overview = OverviewRule()
 objective = ObjectiveRule()
 recommendations = RecommendationsRule()
@@ -1054,7 +1065,7 @@ enableapis = EnableAPIsRule()
 setupproject = SetupProjectRule()
 
  # Cell Validation
-rules = [ copyright, notices, title, links, overview, objective,
+rules = [ copyright, notices, title, links, testenv, overview, objective,
           recommendations, dataset, costs, setuplocal, helpers,
           installation, restart, versions, beforebegin, enableapis,
           setupproject

notebooks/official/prediction/README.md

@@ -0,0 +1,17 @@
+
+### prediction 
+
+
+[Custom model batch prediction with feature filtering](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/prediction/custom_batch_prediction_feature_filter.ipynb)
+
+```
+Learn how to create a custom-trained model from a Python script in a Docker container using the Vertex AI SDK for Python, and then run a batch prediction job by including or excluding a list of features.
+
+The steps performed include:
+
+- Create a Vertex AI custom `TrainingPipeline` for training a model.
+- Train a TensorFlow model.
+- Send batch prediction job.
+
+```
+

notebooks/official/pytorch/README.md

@@ -0,0 +1,21 @@
+
+### pytorch 
+
+
+[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.
+
+```
+

notebooks/official/reduction_server/README.md

@@ -1,6 +1,10 @@
 
+### reduction_server 
+
+
 [PyTorch distributed training with Vertex AI Reduction Server](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/reduction_server/pytorch_distributed_training_reduction_server.ipynb)
 
+```
 Learn how to create a PyTorch distributed training job that uses PyTorch distributed training framework and tools, and run the training job on the Vertex AI Training service with Reduction Server.
 
 The steps performed include:
@@ -8,4 +12,7 @@ The steps performed include:
 * Create a PyTorch distributed training application
 * Package the training application with pre-built containers
 * Create a custom job on Vertex AI with Reduction Server
-* Submit and monitor the job 
+* Submit and monitor the job
+
+```
+

notebooks/official/sdk/README.md

@@ -1,6 +1,10 @@
 
+### sdk 
+
+
 [AutoML Video Classification Example](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/sdk/SDK_AutoML_Video_Classification.ipynb)
 
+```
 The objective of this notebook is to build a AutoML Video Classification Model.
 
 The steps performed include the following:
@@ -13,9 +17,12 @@ The steps performed include the following:
 - Copy AutoML Video Demo Prediction Data for creating batch prediction job
 - Perform batch prediction job on the model
 
+```
+
 
 [Custom training using Python package, managed text dataset, and TF Serving container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/sdk/SDK_Custom_Training_Python_Package_Managed_Text_Dataset_Tensorflow_Serving_Container.ipynb)
 
+```
 Learn how to create a Custom Model using Custom Python Package Training and you learn how to serve the model using TensorFlow-Serving Container for online prediction.
 
 The steps performed include:
@@ -29,3 +36,6 @@ The steps performed include:
 - Deploy a Model and Create an Endpoint on Vertex AI
 - Predict on the Endpoint
 - Create a Batch Prediction Job on the Model
+
+```
+

notebooks/official/structured_data/README.md

@@ -1,6 +1,10 @@
 
-[BQML and AutoML - Experimenting with Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/structured_data/rapid_prototyping_bqml_automl.ipynb)
+### structured_data 
 
+
+[BQML and AutoML - Experimenting with Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/structured_data/rapid_prototyping_bqml_automl.ipynb)
+
+```
 Learn how to use `Vertex AI Predictions` for rapid prototyping a model.
 
 The steps performed include:
@@ -12,17 +16,5 @@ The steps performed include:
 - Deploying the best trained model.
 - Testing the deployed model infrastructure.
 
-
-
-
-
-
-
-
-
-
-
-
-
-
+```
 

notebooks/official/tabnet/README.md

@@ -1,6 +1,10 @@
 
+### tabnet 
+
+
 [Vertex AI Explainations with TabNet models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabnet/ai-explanations-tabnet-algorithm.ipynb)
 
+```
 Learn how to provide a sample plotting tool to visualize the output of TabNet, which is helpful in explaining the algorithm.
 
 The steps performed are:
@@ -9,8 +13,12 @@ The steps performed are:
 * Visualize and understand the feature importance based on the masks output.
 * Clean up the resource created by this tutorial.
 
+```
+
+
 [Vertex AI TabNet](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabnet/tabnet_vertex_tutorial.ipynb)
 
+```
 Learn how to run TabNet model on Vertex AI.
 
 The steps performed are:
@@ -20,3 +28,6 @@ The steps performed are:
 4. **Hyperparameter tuning**: Running a hyperparameter tuning job.
 5. **Hyperparameter on Vertex AI Training with BigQuery input**: Submitting a training job using BigQuery input.
 6. **Cleaning up**: Deleting resources created by this tutorial.
+
+```
+