GPU Pods

Pods are the core compute units in Yotta Labs’ GPU Cloud. They allow users to deploy, manage, and connect to isolated GPU workloads across heterogeneous hardware through the Yotta SaaS Console or the

🧭 Navigation

You can manage Pods via the Yotta Console or through the OpenAPI.

• Console: Go to Compute → Pods on the left sidebar.

• OpenAPI: API Reference

💻 Managing Pods via Console

Pod Page Overview

When entering the Pods page:

• The system by default displays Pods in In Progress state, including:

  • Initialize – Resources are being allocated; the Pod is deploying.

  • Running – The Pod is running normally.

  • Stopping – The Pod is pausing; resources are being reclaimed.

  • Stopped – The Pod has been paused.

  • Terminating – The Pod is being terminated; resources are being reclaimed.

• Click the History tab to view Pods that have completed within the last 24 hours:

  • Terminated – The Pod has been deleted.

  • Failed – Deployment failed. Common causes:

    • Insufficient system resources

    • Invalid image information

• You can:

  • Search by Pod name (supports fuzzy search)

  • Filter by Pod Status or GPU Type

  • Click Deploy (top right corner) to create a new Pod

⚙️ Deploying a Pod

Step-by-Step Guide

1. Navigate to: Compute → Pods

2. Click Deploy (top right)

   • You’ll enter the GPU Selection page.

3. Select GPU Type

   • Choose a GPU model suitable for your workload.

4. Configure Pod

   • Fill in required parameters (fields marked with * are mandatory).

   • Click Edit to choose between a Public Image or Private Image.

Image Requirements

Requirement	Description
Platform	Must be x86 architecture
OS Base	Must be Debian/Ubuntu
Unsupported	ARM and other Linux distributions

1. Deploy Click Deploy to complete the process.

🔌 Connecting to Your Pod

Once the Pod is launched:

• Click the Connect button on the Pod card to view exposed services.

• Availability depends on the port configuration defined at deployment.

• When the container port is Ready, the status will update automatically.

📜 Viewing Logs

• Click Logs on the Pod card to view both:

  • System Logs (platform-level)

  • Container Logs (application-level)

This helps with debugging deployment or runtime issues.

🧊 Pausing or Terminating Pods

🔸 Pause

If you only need to suspend temporarily:

• Click Pause on the Pod card.

• Only Volume storage will continue to incur charges.

• You can Run to restart anytime.

• Pods can be edited while paused.

🔸 Terminate

If you want to remove the Pod completely:

• Click the “...” on the Pod card → choose Terminate.

• The Pod will be permanently deleted and no longer billed.

• Terminated Pods cannot be edited or restarted.

✏️ Editing a Pod

1. Go to Compute → Pods and locate the Pod.

2. Click Pause and wait until the Pod enters Stopped state.

3. Click “...” → Edit, modify configurations, and save.

4. Click Run to restart the Pod with the new settings.

📈 Pod Status Reference

Status	Description
Initialize	Resource allocation in progress; Pod deploying
Running	Pod is running
Stopping	Pausing in progress; resources reclaiming
Stopped	Pod is paused
Terminating	Termination in progress; resources reclaiming
Terminated	Pod fully terminated
Failed	Deployment failed (insufficient resources / invalid image)

💰 Pricing & Billing

Formula

Pod hourly cost = (GPU unit price × number of GPUs)
                + (Disk hourly rate × GB size)
                + (Volume hourly rate × GB size)

Deduction Rules

• Billing starts once the Pod is Running.

• When balance nears $0, all active Pods will be terminated automatically.

• To avoid charges:

  • Use Pause to temporarily suspend (still charges for persistent volumes).

  • Use Terminate to completely stop billing.

Action	Billing Behavior
Pause	Charges continue for Volumes (Stopped state)
Terminate	No charges (Terminated state)

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🧾 Viewing Your Bill

Go to Billing in the left sidebar to view:

• Pod usage breakdown

• GPU, Disk, and Volume hourly costs

• Historical billing data

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🧩 Managing Pods via OpenAPI

You can also manage Pods programmatically via Yotta Labs’ OpenAPI.

API Reference

• 📘 API & SDK Documentation

Tip: Always review the API documentation before calling endpoints to avoid common request errors (invalid parameters, insufficient balance, etc.).

🧱 Example Use Cases

• Automated Pod Deployment via Python SDK

• Monitoring Pod Logs using API polling

• Scaling Workloads across multiple GPU types

• Integrating with CI/CD to trigger training jobs automatically

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🪄 Best Practices

• Use Pause instead of Terminate for short-term downtime.

• Monitor balance regularly to prevent auto-termination.

• Always verify image compatibility (x86 / Ubuntu-based).

• For debugging, prefer checking container logs first.

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🧩 Related Docs

• Pod API Reference

• Billing API

• Yotta Console Overview