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

# Building Your First AI Workload

> Complete tutorial for deploying AI workloads on Airon's bare-metal infrastructure

# Building Your First AI Workload

In this tutorial, you'll learn how to deploy a machine learning workload on Airon's bare-metal infrastructure. We'll walk through creating a GPU machine, setting up your environment, and running a sample AI model.

## What You'll Learn

* How to provision GPU machines for AI workloads
* Setting up machine learning frameworks
* Optimizing performance on bare-metal hardware
* Monitoring and scaling your workloads

## Prerequisites

* Completed the [Getting Started Guide](/getting-started)
* Basic knowledge of Python and machine learning
* Docker installed locally (for testing)

## Step 1: Create a GPU Machine

First, let's create a powerful GPU machine for our AI workload:

```bash
airon machines create \
  --type gpu \
  --architecture x86 \
  --brand nvidia \
  --number 4 \
  --region us-west-2 \
  --image ubuntu-22.04-ml
```

This creates a machine with:

* 4x NVIDIA GPUs
* Ubuntu 22.04 with ML frameworks pre-installed
* Optimized for AI workloads

## Step 2: Set Up Your Environment

Connect to your machine and set up the environment:

```bash
# Connect to your machine
airon machines ssh YOUR_MACHINE_ID

# Update system packages
sudo apt update && sudo apt upgrade -y

# Verify GPU availability
nvidia-smi
```

## Step 3: Deploy Your Model

Let's deploy a sample computer vision model:

```python
import torch
import torchvision
from torch import nn

# Verify CUDA is available
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU count: {torch.cuda.device_count()}")

# Load a pre-trained model
model = torchvision.models.resnet50(pretrained=True)
model = model.cuda()

# Your model training code here...
```

## Step 4: Monitor Performance

Monitor your workload performance:

```bash
# Monitor GPU usage
watch nvidia-smi

# Monitor system resources
htop

# Check machine status via CLI
airon machines status YOUR_MACHINE_ID
```

## Step 5: Scale Your Workload

When you need more compute power:

```bash
# Create additional machines
airon machines create --type gpu --count 3

# Use with orchestration tools
# (Kubernetes, Docker Swarm, etc.)
```

## Best Practices

### Performance Optimization

* Use NVMe storage for fast data access
* Optimize batch sizes for your specific GPU configuration
* Consider multi-GPU training strategies

### Cost Management

* Destroy machines when not in use
* Use spot instances for development
* Monitor usage with Airon's billing dashboard

### Security

* Use SSH keys instead of passwords
* Configure firewall rules appropriately
* Keep systems updated

## Next Steps

* **[Nomad Autoscaler Plugin](/plugins/nomad-autoscaler-plugin)** - Automatically scale based on demand
* **[API Integration](/api/getting-started)** - Integrate Airon into your workflows
* **Advanced Tutorials** - Explore specialized use cases

## Troubleshooting

### Common Issues

**GPU not detected**

```bash
# Restart the machine
airon machines restart YOUR_MACHINE_ID

# Check driver installation
nvidia-smi
```

**Out of memory errors**

```bash
# Monitor memory usage
nvidia-smi -l 1

# Reduce batch size or model complexity
```

**Slow training**

* Verify data loading pipeline
* Check if using all available GPUs
* Monitor network I/O for data-intensive workloads

## Getting Help

If you need assistance:

* Join our [community forum](https://community.airon.ai)
* Check the [troubleshooting guide](https://docs.airon.ai/troubleshooting)
* Contact support at [support@airon.ai](mailto:support@airon.ai)