> ## Documentation Index
> Fetch the complete documentation index at: https://anaconda.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Post-install configuration

export const Danger = ({children}) => {
  return <div class="callout my-4 px-5 py-4 overflow-hidden rounded-2xl flex gap-3 border danger-admonition dark:danger-admonition" data-callout-type="danger">
      <div class="mt-0.5 w-4">
        <svg width="14" height="14" viewBox="0 0 14 14" fill="rgb(239, 68, 68)" xmlns="http://www.w3.org/2000/svg" class="w-4 h-4 text-sky-500" aria-label="Danger">
          <path fill-rule="evenodd" clip-rule="evenodd" d="M7 1.3C10.14 1.3 12.7 3.86 12.7 7C12.7 10.14 10.14 12.7 7 12.7C5.48908 12.6974 4.0408 12.096 2.97241 11.0276C1.90403 9.9592 1.30264 8.51092 1.3 7C1.3 3.86 3.86 1.3 7 1.3ZM7 0C3.14 0 0 3.14 0 7C0 10.86 3.14 14 7 14C10.86 14 14 10.86 14 7C14 3.14 10.86 0 7 0ZM8 3H6V8H8V3ZM8 9H6V11H8V9Z"></path>
        </svg>
      </div>
      <div class="text-sm prose min-w-0 w-full">
        {children}
      </div>
    </div>;
};

Once you have successfully completed installation, there are some additional steps to take before beginning to use Data Science & AI Workbench.

## Final configuration steps

1. (Gravity only) To add worker nodes to the cluster, follow the instructions in our [adding and removing nodes](/docs/data-science/latest/admin/resources/gravity/add-remove-node) topic.
2. *Wait for the application to fully stabilize.* Workbench can take up to 30 minutes to fully finish loading, depending upon the performance of the cluster network and attached disks. You might be able to log in to the main UI immediately, but sessions and deployments will not be ready to start.
3. Confirm that the cluster has loaded by running the following command from the master node:

   ```sh theme={null}
   watch kubectl get pods
   ```

   Wait for every pod to indicate a status of either `Running` or `Completed`. The `app-images` pods will be the last to stabilize. You can exit the `watch` command at any time with `ctrl-C`.

## Testing your installation

Once the application has fully loaded, you can do the following to verify that your installation succeeded.

1. Access the Workbench console by entering the URL of your Workbench server in a web browser: `https://anaconda.example.com`, replacing `anaconda.example.com` with the fully qualified domain name of the host server.
2. Log in using the default username and password `anaconda-enterprise` / `anaconda-enterprise`.
3. Update the passwords for your `anaconda-enterprise` and `admin` users. See [Configuring user access](/docs/data-science/latest/admin/user-mgmt/main) for information and steps for updating your password.

   <Danger>
     Do not alter the `anaconda-enterprise` account username; it is used for certain cluster administration functions.

     If you do not update your passwords, anyone with the default credentials will be able to access your admin console. This poses a security risk, so it is important to change these passwords immediately.
   </Danger>

You can verify a successful installation by doing any or all of the following:

* [Creating](/docs/data-science/latest/data-science-workflows/projects/main) a new project and starting an editing session
* [Deploying](/docs/data-science/latest/data-science-workflows/deployments/main) a project
* [Generating a token](/docs/data-science/latest/data-science-workflows/deployments/share) from a deployment

<Note>
  Some of the sample projects can only be deployed after [mirroring the package repository](/docs/data-science/latest/admin/chan-pkg/mirror). To test your installation without doing this first, you can deploy the “Hello Anaconda Enterprise” sample project.
</Note>