All the required files and instructions are specified in the step for you to easily complete the tutorial.

By the end of the tutorial you will have your AI code in form of a Docker image, connected to your AI workflow. You will also know how to use Docker images together with AI core and how to debug your code if the **Execution** goes to an unexpected state. This tutorial is a precursor to the set up of data pipelines and models generation.

Select **Security** from the navigation bar and then click **New Access Token**.

In the same directory, create a file named `Dockerfile` **(no extension to be added after the filename)**. This file stores instructions for Docker to build an image. Your Docker image is a Linux distribution, therefore commands in this `Dockerfile` are similar to Linux commands with verbs for Docker as the suffix. Paste the following content into the file: *** tell them that they cannot change it***

You may notice that you did not specify the command to run the script `main.py` in the `Dockerfile`. This command will be written into the AI workflow and is covered later in this tutorial. ***customer must write exactly this***

Login to your Docker account from your terminal. This step is required only once, it will store you Docker account credentials in your local Docker Desktop.

Copy and paste, your generated Docker Access Token to use as your password. For security reasons you will not see anything getting printed on the screen when you type or paste the password.

This step is required once. Storing the credentials enables SAP AI Core to pull (download) your Docker images from a **private** Docker repository. Use of a private Docker image prevents others from seeing your content.

Select your SAP AI Core connection under **Workspaces** app.

Click **Docker Registry Secrets** in **AI Core Administration** app. Click **Add**.

Click on POST Create a secret. Paste and edit the snippet into the body of the request, using the information below as guides.

Paste and edit the snippet into the body of the request, using the information below as guides.

Observe what the changes difference between the `hello.yaml` (created in the prerequisite tutorial) and `code-pipeline.yaml`.

2. `image`: A key that specifies which code to use in workflow and which command (to run `main.py`) to execute within the Docker image.

Click though **Workspaces** > **Applications** > **<your SAP AI Core connection>** in SAP AI Launchpad.

Click though **Collections** > GET Returns the `ArgoCD` application status.

List all available scenarios by clicking through **Collections** > **Scenarios** > GET get list of scenarios.

Lis the executables for the scenario `learning-code`:

Click **refresh icon**, after a while, the status will show as **DEAD**.

Create configuration with the **BODY**:

**Example Response** The configuration ID differs every time you create one, and for all users.

**Example Response** The execution ID differs every time you create one, and for all users.

After a while, the status will show as **DEAD**.

**Example Response** The configuration ID differs every time you create one, and for all users.

**Example Response** The execution ID differs every time you create one, and for all users.

After a while, the status will show as **DEAD**.

Please don't panic when this execution goes from **UNKOWN** to **RUNNING** then to the **DEAD** state. Resolving this is covered in next step.

Click **Logs** tab to see stack trace generated from your code.

To query execution logs, paste and edit the snippet below, using your execution ID. You should see a stack trace of errors.

Update your workflow, by adding the new `02` tag to the `ai.sap.com/version` and the Docker image name.

Go to SAP AI Launchpad. Click through **ML Operations** > **Configuration**.