# GPU Pods

## 💻 Managing Pods via Console

#### Pod Page Overview

By default, the system displays Pods with an **In Progress** status, which includes the following states:

* **Preparing**: Resources are being prepared
* **Initializing**: Resources are being allocated and the Pod is being deployed
* **Running**: The Pod is running
* **Stopping**: The Pod is being paused and resources are being reclaimed
* **Stopped**: The Pod has been paused
* **Terminating**: The Pod is being terminated and resources are being reclaimed
* **Terminated**: The Pod has been terminated
* **Failed**: The Pod failed to deploy. Common causes include:
  * **Insufficient system resources**
  * **Invalid image configuration**

<figure><img src="/files/m40kOOqBOgNOt8PCNLAD" alt=""><figcaption></figcaption></figure>

* There are buttons you can use at the bottom:

  * :link: `Connect` You can connect your machine to specific ports, such as 8888 for Jupyter Notebook. We currently support both **SSH** and **HTTP** ports.

  <figure><img src="/files/FiYh68dkP5eVGKzVI4a6" alt="" width="438"><figcaption></figcaption></figure>

  <figure><img src="/files/tyzidtTRU6Yj9MbgVf4Y" alt="" width="443"><figcaption></figcaption></figure>

  * :notepad\_spiral: `Log` You can check the container logs to view its current status and identify any errors or issues.

  <figure><img src="/files/5i2dHkQgjogaJwSt8TVs" alt="" width="563"><figcaption></figcaption></figure>

  * :chart\_with\_upwards\_trend:`Metrics` This provides real-time monitoring of GPU, CPU, memory, and storage usage to help you track system performance and resource utilization.

  <figure><img src="/files/PnfFbhAJIIRee7mBqXAI" alt=""><figcaption></figcaption></figure>
* Click the **History** tab to view Pods that have completed within the last 24 hours, which includes:
  * `Terminated` – The Pod has been deleted.
  * `Failed` – Deployment failed. Common causes:

    * Insufficient system resources
    * Invalid image configuration

    <figure><img src="/files/Uyf5KazYhzPTRCUzngmb" alt="" width="275"><figcaption></figcaption></figure>
* There is a search bar where you can use Pod name to find your Pod (fuzzy search supported). You can also use **Pod Status** or **GPU Type** to filter Pods.

## ⚙️ Deploying a Pod

#### Step-by-Step Guide

* **Navigate to:** `Compute → Pods`

<figure><img src="/files/qRLsdWnHNWD2mmmhnGfJ" alt=""><figcaption></figcaption></figure>

* **Click Deploy** (top right). You’ll enter the **GPU Selection** page.

<figure><img src="/files/uLIOdLdsDZsMV8ssOQwS" alt=""><figcaption></figcaption></figure>

* **Select GPU Type**

  * Choose a GPU model suitable for your workload.

  <figure><img src="/files/Xq0hvUTURTebMFE1Jd82" alt=""><figcaption></figcaption></figure>
* **Configure Pod**

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

  <figure><img src="/files/e6IvY3Klg3RLcPASd2zT" alt=""><figcaption></figcaption></figure>

**Image Requirements**

Click **Edit** next to image name to further configure your image. We provided a list of official images compiled by Yotta Labs. Also, we allow users to select custom images including both **Public Images** and **Private Images**.

Here are a few requirements if you want to build your custom image:

* Must be compiled for **x86** architecture
* Must be **Debian/Ubuntu**

5. **Deploy**\
   Click **Deploy** to complete the process.

<figure><img src="/files/jepE2TwDIe9gyMtgASsl" alt="" width="302"><figcaption></figcaption></figure>

## 💾 **System Volume**

The System Volume would automatically mount a list of system directories on the created Pod. This ensures that software, configurations, and data stored within these directories are persistent even if the Pod is edited or restarted.

#### **Supported Directories**

Read and write operations to the following directories will be persistent:

<table data-header-hidden data-full-width="false"><thead><tr><th width="169.5" align="center" valign="middle"></th><th align="center"></th></tr></thead><tbody><tr><td align="center" valign="middle"><strong>Directory</strong></td><td align="center"><strong>Brief Description</strong></td></tr><tr><td align="center" valign="middle"><code>/home</code></td><td align="center">User home directories; user-level configs and data.</td></tr><tr><td align="center" valign="middle"><code>/root</code></td><td align="center">Root user home directory; scripts and temp data.</td></tr><tr><td align="center" valign="middle"><code>/var</code></td><td align="center">Variable files (logs, caches, runtime data).</td></tr><tr><td align="center" valign="middle"><code>/run</code></td><td align="center">Runtime status files (PIDs, sockets).</td></tr><tr><td align="center" valign="middle"><code>/etc</code></td><td align="center">System and service configuration files.</td></tr><tr><td align="center" valign="middle"><code>/usr</code></td><td align="center">System-level apps, libraries, and runtime components.</td></tr></tbody></table>

#### **Size Requirements**

To ensure that the Pod can launch and run smoothly, we recommend using the following rule to decide the size of your system volume:

> The size of the system volume needs to ≥ Image Size × 3

**Example:**

* **Image:** PyTorch base image (10 GiB)
* **Recommended System Volume Size:** At least 30 GiB

{% hint style="info" %}
The "**For Development**" button is automatically turned on when you are creating a pod.

You can find it and change the settings beside the pod name bar.

System volume is set to 100GB by default.
{% endhint %}

#### **Recommended Use Cases:**

* **Preserving Environments:** Retaining toolchains or dependencies (e.g., pip packages) after a Pod rebuild.
* **Persisting Configurations:** Saving changes made in `/etc`.
* **Retaining Logs:** Keeping logs in `/var` for a specific period.

<figure><img src="/files/Kbhnk3vtlAzjkgQUnO2s" alt=""><figcaption></figcaption></figure>

## 🔌 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.

<figure><img src="/files/tyzidtTRU6Yj9MbgVf4Y" alt="" width="443"><figcaption></figcaption></figure>

## 📜 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.

<figure><img src="/files/90sPyLKoP4LPVUfEaJI1" alt="" width="563"><figcaption></figcaption></figure>

## 🧊 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

```
Hourly Pod cost = (GPU unit price per hour × number of GPUs) +
                  (Container Storage unit price per GB per hour × GB exceeding the free quota)
```

**Charges:**

* Your balance will be deducted once a Pod starts running.
* Once your balance drops to near $0, all running Pods will be terminated.
* If you no longer need a running Pod, you can Pause or Terminate it.

***

## 🧩 Managing Pods via OpenAPI

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

#### API Reference

* [📘 API & SDK Documentation](https://docs.yottalabs.ai/api-and-sdk/api-guides)

> **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

***

## 🪄 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.

***

## 🧩 Related Docs

* [Pod API Reference](https://docs.yottalabs.ai/api-and-sdk/api-guides)
* [Billing](https://docs.yottalabs.ai/products/billing)


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