Quickstart

Get an account

1. Use your email/Google account/Github account to sign up here

2. Verify your email address

Add a payment method

1. Navigate to the Billling page

2. Choose the credit you'd like to pay

3. Click Pay now and add a card

4. You can view the bill on the Billing page.

Deploy a Pod

Once your account is ready, it’s time to deploy your first Pod:

1. Navigate to the Pods page in the web interface.

2. Click the Deploy button.

3. From the list of available GPUs, choose RTX 4090.

4. In the Pod Name field, enter "quickstart"

5. Keep the default settings for Pod Template, GPU Count, and Instance Pricing.

6. Hit Deploy to launch your Pod. After a few seconds, you’ll be redirected back to the Pods page.

7. For more guide, check this doc for GPU pods

Explore the Pod

1. Image Source&Image: This section defines the Operating System and Software Stack that will run on your GPU.Docker Hub is the most common option. It pulls pre-built software environments (containers) from the public Docker registry.

2. Container Storage: This is the Temporary Workspace (also known as "Root Storage").

   • Size Slider: This is the disk space available for your OS, installed libraries, and temporary files.

   • Cost: 256 GB is free, but extra space costs $0.00005 per GB per hour.

3. Persistent Storage: This is your virtual hard drive that survives even if the GPU pod is deleted.Ceph / S3: These are different storage protocols.

   • Ceph usually acts like a normal folder on your machine where you can save data permanently.

   • S3 connects to cloud "buckets" (like AWS S3) for massive datasets.

Running Code via JupyterLab

1. Return to the Pods page and click Connect

2. Choose Jupyter Lab -> :8888 service. Click on the Arrow Out Box icon.

3. Under the Notebook header, choose the Python 3 (ipykernel) environment.

4. Enter print("Hello, world!") into the first cell.

5. Press the Play button (or Shift + Enter) to execute. Success! You’ve officially deployed and executed code on your RunPod instance.

Clean up

1. Access Pod Settings: Return to the Pods dashboard and select your active instance.

2. Delete Operations: Click the three-dot icon->Terminate.

3. Confirm Deletion: Confirmation in the following pop-up.

4. Suspend Operations: Click the Pause button.

What's next?

1. Create API keys to manage your infrastructure through code.

2. Deep dive into the various pricing policies available for different GPU tiers.

3. Transition to elastic deployment computing to develop robust, production-grade AI applications.

4. See our FAQ page.