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Building Your First AI Workload

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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

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Prerequisites

• Completed the Getting Started Guide
• Basic knowledge of Python and machine learning
• Docker installed locally (for testing)

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Step 1: Create a GPU Machine

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

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

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Step 2: Set Up Your Environment

# 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

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Step 3: Deploy Your Model

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...

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Step 4: Monitor Performance

# Monitor GPU usage
watch nvidia-smi

# Monitor system resources
htop

# Check machine status via CLI
airon machines status YOUR_MACHINE_ID

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Step 5: Scale Your Workload

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

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

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Best Practices

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Performance Optimization

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

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Cost Management

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

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Security

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

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Next Steps

• Nomad Autoscaler Plugin - Automatically scale based on demand
• API Integration - Integrate Airon into your workflows
• Advanced Tutorials - Explore specialized use cases

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Troubleshooting

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Common Issues

# Restart the machine
airon machines restart YOUR_MACHINE_ID

# Check driver installation
nvidia-smi

# Monitor memory usage
nvidia-smi -l 1

# Reduce batch size or model complexity

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

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Getting Help

• Join our community forum
• Check the troubleshooting guide
• Contact support at [email protected]