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.
2. Verify your email address.
3. Set up two-factor authentication (recommended for security).

Planning to share compute resources with your team? You can convert your personal account to a team account later. See Manage accounts for details.

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 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.

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

Step 3: Explore the Pod detail pane

On the Pods page, 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 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.

Terminating a Pod permanently deletes all data that isn’t stored in a network volume. 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.

Next steps

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

• Generate API keys for programmatic resource management.
• Experiment with various options for accessing and managing Runpod resources.
• Learn how to choose the right Pod for your workload.
• Review options for Pod pricing.
• Explore our tutorials for specific AI/ML use cases.
• Start building production-ready applications with Runpod Serverless.

Need help?

• Join the Runpod community on Discord.
• Submit a support request using our contact page.
• Reach out to us via email.