feat(notebooks): add Notebooks API v2 Workbench instance samples and remove deprecated content

notebooks/README.md

@@ -1,82 +1,12 @@
-# AI Platform Notebooks
+# Google Cloud Notebooks & Workbench Samples
 
-[AI Platform Notebooks](https://cloud.google.com/ai-platform-notebooks)
-is a managed service that offers an integrated and secure JupyterLab
-environment for data scientists and machine learning developers to
-experiment, develop, and deploy models into production.
+This directory contains samples and tutorials for managing **Workbench instances** and executing Jupyter notebooks on Google Cloud.
 
-This directory contains code samples and
-tutorials. The tutorials are divided into 2 sections:
-- [API code samples](samples)
-- [tutorials](tutorials)
+Workbench instances provide enterprises with a managed, secure JupyterLab environment that protects organizational data while enabling centralized administration and configuration. Data scientists can experiment, develop, and deploy machine learning and analytics workloads on Google Cloud while maintaining their organization's required security posture.
 
-## Notebooks API
+## Directory Structure
 
-In [this](samples) folder you will find Python code samples to interact
-with the
-[API](https://cloud.google.com/ai-platform/notebooks/docs/reference/rest)
-
-## Tutorials
-
-**Run in Notebooks**
-
-1. Create a Notebook
-   [instance](https://cloud.google.com/ai-platform/notebooks/docs#how-to)
-2. Clone this repository via Jupyter console or using the Git clone
-   button
-
-```bash
-git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
-```
-
-3. Open the tutorials folder and open the Jupyter notebooks
-
-
-**Run locally**
-
-1. Install Jupyter notebooks
-   ([instructions](https://jupyter.org/install))
-1. Install the dependencies in the [requirements.txt](./requirements.txt) file ([instructions below](#install-the-dependencies))
-1. Registered the `google-cloud-bigquery` magic commands ([instructions below](#register-magics-and-configure-matplotlib))
-1. Set `matplotlib` to render inline ([instructions below](#register-magics-and-configure-matplotlib))
-
-### Install the dependencies
-
-Install the dependencies with the following command:
-
-        pip install --upgrade -r requirements.txt
-
-### Register magics and configure matplotlib
-
-You can either perform these set up steps in a single notebook, or add the
-steps to your IPython configuration file to apply to all notebooks.
-
-### Perform set up steps within a notebook
-
-To perform the set up steps for a single notebook, run the following commands
-in your notebook to register the BigQuery magic commands and set `matplotlib`
-to render inline:
-```python
-%load_ext google.cloud.bigquery
-%matplotlib inline
-```
-
-### Perform set up steps in your IPython configuration file
-
-To perform the set up steps implicitly for all of your notebooks, add the
-following code to your `ipython_config.py` file to register the BigQuery magic
-commands and set `matplotlib` to render inline:
-```python
-c = get_config()
-
-# Register magic commands
-c.InteractiveShellApp.extensions = [
-    'google.cloud.bigquery',
-]
-
-# Enable matplotlib renderings to render inline in the notebook.
-c.InteractiveShellApp.matplotlib = 'inline'
-```
-See
-[IPython documentation](https://ipython.readthedocs.io/en/stable/config/intro.html)
-for more information about IPython configuration.
+- **[`v2/`](v2/)**: Atomic Python client library samples (`google-cloud-notebooks` / `google.cloud.notebooks_v2`) for creating, configuring, hardening, and managing **Workbench instances**, including enterprise security controls (private VPC networking, single-user least-privilege service accounts, CMEK encryption, Shielded VM configuration, and IAM access policies).
+- **[`rendered/`](rendered/)**: Rendered HTML views of tutorial notebooks.
+- **[`tutorials/bigquery/`](tutorials/bigquery/)**: Jupyter notebook tutorials demonstrating BigQuery integration from a notebook environment.
+- **[`tutorials/vision/`](tutorials/vision/)**: Jupyter notebook tutorials demonstrating Cloud Vision API integration from a notebook environment.