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Author: Milstein

docs/openshift-ai/other-projects/connect-vscode-to-rhoai-wb.md

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+# Connect to RHOAI Workbench Kernel from local Visual Studio Code (VS Code)
+
+Some users prefer to work directly in their local IDE and run Jupyter notebooks
+using a kernel on a remote workbench hosted on NERC's RHOAI. While most IDEs
+support connecting to a remote kernel as a standard feature, this does not work
+with RHOAI due to its authentication setup.
+
+Typically, IDEs use token-based authentication to connect to remote kernels. However
+workbench pods in RHOAI include an authentication layer in front of the workbench
+container that manages user access. This layer relies on OpenShift's authentication
+mechanism, which is not compatible with the standard remote kernel connection
+features provided by most IDEs.
+
+## Workaround: Connect to the remote kernel using Openshift port-forwarding
+
+Use the following steps to connect your local VS Code to RHOAI Workbench kernel:
+
+-   In your RHOAI data science project, create a workbench that you intend to use
+    as your remote kernel. If you require a GPU accelerator, choose a compatible
+    workbench image (i.e. PyTorch, TensorFlow based Workbench image).
+
+    ![Create workbench](images/open-object-detection-jupyter-lab.png)
+
+-   Open the workbench and copy the context path from the browser. You will need
+    this (i.e. `notebook/<your-project-namespace>/object-detection/lab`) later
+    when connecting from VS Code locally.
+
+    ![Workbench context path](images/workbench-context-path.png)
+
+-   Make sure you have the `oc` CLI tool installed and configured on your local
+    machine following [these steps](../../openshift/logging-in/setup-the-openshift-cli.md#first-time-usage).
+
+-   From terminal on your laptop/desktop login to the NERC OpenShift cluster and
+    switch to your project namespace:
+
+    ```sh
+    oc login --token=<your_token> --server=https://api.shift.nerc.mghpcc.org:6443  
+    ```
+
+    For example:
+
+    ```sh
+    oc login --token=<your_token> --server=https://api.shift.nerc.mghpcc.org:6443
+    Logged into "https://api.shift.nerc.mghpcc.org:6443" as "<your_account>" using the token provided.  
+    ```
+
+    !!! info "Information"
+
+        Some users may have access to multiple projects. Run the following command
+        to switch to a specific project space: `oc project <your-project-namespace>`.
+
+-   Switch to your data science project:
+
+    Please confirm the correct project is being selected by running `oc project`,
+    as shown below:
+
+        oc project
+        Using project "<your-project-namespace>" on server "https://api.shift.nerc.mghpcc.org:6443".
+
+-   Start port-forwarding to your workbench pod
+
+    *   List all the pods in your project. The pod running your workbench is named
+        based on your workbench name in RHOAI. For example, `object-detection-0`
+        corresponds to a workbench named `Object Detection`.
+
+        >   **Note:** Capital letters are converted to lowercase, and spaces are
+            replaced with hyphens (`-`).
+
+    *   Enable port-forwarding to your workbench pod. You need to forward to the
+        port the pod is listening on. It is usually `8888` for RHOAI workbench.
+        You can find this port from the service in your project with name same
+        as your workbench.
+
+        ![Port-forward to wb pod](images/port-forwarding.png)
+
+-   Open the Jupyter notebook in your local VS Code
+
+    ![Jupyter Notebook](images/jupyter-nb.png)
+
+-   From the top right corner of the notebook, click on `Select Kernel`.
+
+    ![Select Kernel](images/select-kernel.png)
+
+-   From the options, select `Existing Jupyter Server` and then enter the url as
+    follows:
+
+    ![Existing Jupyter Server](images/existing-jupyter-server.png)
+
+    `localhost` `[:port]` `/context-path` copied earlier that has the pattern
+    `/notebook/ds-project-name/workbench-name/lab`. e.g. `http://localhost:8888/notebook/<your-project-namespace>/object-detection/lab`
+    and then press `Enter` to confirm.
+
+    ![Existing Jupyter Server URL](images/jupyter-server-url.png)
+
+-   A prompt saying
+
+    `Connecting over HTTP without a token may be an insecure connection. Do you
+    want to connect to a possibly insecure server?` is displayed. select `Yes`
+
+    ![Insecure connection prompt](images/connect-over-http.png)
+
+-   Select the prompted `Server display name` or enter a new one and then press
+    `Enter` to confirm.
+
+    ![Server display name](images/name-connection.png)
+
+-   A list of available kernels is displayed. Choose `Python 3.9`.
+
+    ![Select Kernel](images/select-remote-kernel.png)
+
+-   You should see the selected Kernel in the top right corner.
+
+    ![Remote kernel selected](images/remote-kernel-selected.png)
+
+-   The code inside of your notebook will now execute using the remote kernel on
+    the RHOAI workbench pod.
+
+-   If your workbench uses a NVIDIA GPU, you can verify that it is being used in
+    the execution of your notebook by adding a command `!nvidia-smi`. You should
+    see output similar to the image below.
+
+    ![Nvidia GPU used ](images/nvidia-gpu-used.png)
+
+## Caveats
+
+-   Jupyter notebooks in your local VS Code environment will not be saved to the
+    workbench.
+
+-   If your notebook uses any files (models, inputdata etc.), they should be
+    present on the workbench and their path should match the path specified in
+    your notebook.
+
+---