> ## Documentation Index
> Fetch the complete documentation index at: https://operator.inference.net/llms.txt
> Use this file to discover all available pages before exploring further.

# Quick Start

> Deploy your first Inference.net node using Docker

<Warning>
  Before proceeding, make sure you have compatible [hardware](/getting-started/hardware).
</Warning>

This guide will walk you through setting up and running an Inference.net node using Docker on Linux.

## Prerequisites

Before you can run an Inference.net node, you'll need the following dependencies installed and configured:

### Required Dependencies

* **Docker Engine** - Container runtime for running the Inference node
* **NVIDIA Drivers** - GPU drivers compatible with your hardware
* **NVIDIA Container Toolkit** - Enables GPU support in Docker containers

Please ensure all dependencies are properly installed before proceeding with node setup.

### Verify Installation

Before proceeding, verify that all components are properly installed:

```bash theme={"system"}
# Check Docker installation
docker --version
docker info

# Verify NVIDIA drivers
nvidia-smi

# Test NVIDIA Docker support
docker run --rm --gpus all nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smi
```

If all commands execute successfully and the last command shows your GPU information, you're ready to proceed.

## Setting Up Your Account

1. **Register an account** at [https://inference.net/register](https://inference.net/register)
2. Contact the Inference.net team to verify your account. You will not be able to start a node until your account is verified.

## Creating and Running Your First Node

### Step 1: Create a Worker

1. Navigate to the **Workers** tab in your dashboard
2. Click **Create Worker** in the top-right corner
3. Enter a descriptive name for your worker (e.g., "8x-h200-1")
4. Ensure **Docker** is selected as the deployment method
5. Click **Create Worker**

### Step 2: Launch Your Worker

1. On the Worker Details page, click **Launch Worker**

2. You'll see a Docker command with your unique worker code. It will look like this:

   ```bash theme={"system"}
   docker run \
     --pull=always \
     --restart=always \
     --runtime=nvidia \
     --gpus all \
     -v ~/.inference:/root/.inference \
     inferencecloud/amd64-nvidia-inference-node:latest \
     --code <your-worker-code>
   ```

3. Copy and run this command in your terminal. Make sure to replace `<your-worker-code>` with the worker code on the launch worker modal.

### Step 3: Monitor Initialization

Once started, your node will enter the "Initializing" state on the dashboard. This initialization phase:

* Typically takes 1-2 minutes
* May take up to 10 minutes depending on your GPU and network speed
* Downloads necessary model files and prepares the inference environment

You can monitor the progress by checking:

* The dashboard status
* Docker logs: `docker logs -f $(docker ps -lq)`

## Understanding the Docker Command

Let's break down what each parameter does:

* `--pull=always`: Always pulls the latest image version
* `--restart=always`: Automatically restarts the container if it stops or the system reboots
* `--runtime=nvidia`: Enables NVIDIA GPU support
* `--gpus all`: Grants access to all available GPUs
* `-v ~/.inference:/root/.inference`: Persists data between container restarts
* `--code <your-worker-code>`: Your unique worker authentication code

## Managing Your Node

### Viewing Logs

To monitor your node's activity:

```bash theme={"system"}
# View recent logs
docker logs $(docker ps -lq)

# Follow logs in real-time
docker logs -f $(docker ps -lq)
```

### Checking Status

```bash theme={"system"}
# List running containers
docker ps

# View resource usage
docker stats
```

### Stopping Your Node

```bash theme={"system"}
# Stop the container gracefully
docker stop $(docker ps -lq)

# Or stop by container ID
docker stop <container-id>
```

## Troubleshooting

### Common Issues

**Container won't start:**

* Check Docker daemon: `sudo systemctl status docker`
* Restart Docker: `sudo systemctl restart docker`
* View Docker logs: `sudo journalctl -fu docker`

**GPU not detected:**

* Verify NVIDIA runtime: `docker info | grep nvidia`
* Check GPU availability: `nvidia-smi`
* Test GPU access: `docker run --rm --gpus all nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smi`

**Node stuck in "Initializing":**

* Check container logs for errors: `docker logs $(docker ps -lq)`
* Ensure you have sufficient disk space for model downloads
* Verify your internet connection is stable

## Next Steps

<CardGroup cols={2}>
  <Card title="Hardware Requirements" icon="microchip" href="/getting-started/hardware">
    Check out the list of officially supported hardware
  </Card>
</CardGroup>