> ## Documentation Index
> Fetch the complete documentation index at: https://runpod-b18f5ded-promptless-websocket-streaming-tutorial.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Deploy your first Pod

> Run code on a remote GPU in minutes.

Follow this guide to learn how to create an account, deploy your first GPU Pod, and use it to execute code remotely.

## Step 1: Create an account

Start by creating a Runpod account:

1. [Sign up here](https://www.console.runpod.io/signup).
2. Verify your email address.
3. Set up two-factor authentication (recommended for security).

<Tip>
  Planning to share compute resources with your team? You can convert your personal account to a team account later. See [Manage accounts](/get-started/manage-accounts) for details.
</Tip>

## Step 2: Deploy a Pod

Now that you've created your account, you're ready to deploy your first Pod:

1. Open the [Pods page](https://www.console.runpod.io/pods) in the web interface.
2. Click the **Deploy** button.
3. Select **A40** from the list of graphics cards.
4. In the field under **Pod Name**, enter the name **quickstart-pod**.
5. Keep all other fields (Pod Template, GPU Count, and Instance Pricing) on their default settings.
6. Click **Deploy On-Demand** to deploy and start your Pod. You'll be redirected back to the Pods page after a few seconds.

<Note>
  If you haven't set up payments yet, you'll be prompted to add a payment method and purchase credits for your account.
</Note>

## Step 3: Explore the Pod detail pane

On the [Pods page](https://www.console.runpod.io/pods), click the Pod you just created to open the Pod detail pane. The pane opens onto the **Connect** tab, where you'll find options for connecting to your Pod so you can execute code on your GPU (after it's done initializing).

Take a minute to explore the other tabs:

* **Details**: Information about your Pod, such as hardware specs, pricing, and storage.
* **Telemetry**: Realtime utilization metrics for your Pod's CPU, memory, and storage.
* **Logs**: Logs streamed from your container (including stdout from any applications inside) and the Pod management system.
* **Template Readme**: Details about the template your Pod is running. Your Pod is configured with the latest official Runpod PyTorch template.

## Step 4: Execute code on your Pod with JupyterLab

1. Go back to the **Connect** tab, and under **HTTP Services**, click **Jupyter Lab** to open a JupyterLab workspace on your Pod.
2. Under **Notebook**, select **Python 3 (ipykernel)**.
3. Type `print("Hello, world!")` in the first line of the notebook.
4. Click the play button to run your code.

And that's it—congrats! You just ran your first line of code on Runpod.

## Step 5: Clean up

To avoid incurring unnecessary charges, follow these steps to clean up your Pod resources:

1. Return to the [Pods page](https://www.console.runpod.io/pods) and click your running Pod.
2. Click the **Stop** button (pause icon) to stop your Pod.
3. Click **Stop Pod** in the modal that opens to confirm.

You'll still be charged a small amount for storage on stopped Pods (\$0.20 per GB per month). If you don't need to retain any data on your Pod, you should terminate it completely.

To terminate your Pod:

1. Click the **Terminate** button (trash icon).
2. Click **Terminate Pod** to confirm.

<Warning>
  Terminating a Pod permanently deletes all data that isn't stored in a [network volume](/storage/network-volumes). Be sure that you've saved any data you might need to access again.

  To learn more about how storage works, see the [Pod storage overview](/pods/storage/types).
</Warning>

## Next steps

Now that you've learned the basics, you're ready to:

* [Generate API keys](/get-started/api-keys) for programmatic resource management.
* [Manage your account](/get-started/manage-accounts) to create teams and invite collaborators.
* Learn how to [choose the right Pod](/pods/choose-a-pod) for your workload.
* Review options for [Pod pricing](/pods/pricing).
* [Explore our tutorials](/tutorials/introduction/overview) for specific AI/ML use cases.
* Start building production-ready applications with [Runpod Serverless](/serverless/overview).

## Need help?

* Join the Runpod community [on Discord](https://discord.gg/cUpRmau42V).
* Submit a support request using our [contact page](https://contact.runpod.io/hc/requests/new).
* Reach out to us via [email](mailto:help@runpod.io).