Azure Deployment¶

Deploy GPUX models on Microsoft Azure with GPU support.

🎯 Overview¶

Complete guide for deploying GPUX on Azure with GPU VMs and managed services.

In Development

Detailed deployment guide for Azure is being developed. Basic functionality is available.

📚 What Will Be Covered¶

🔄 Azure GPU VMs: NC, ND, NV series instances
🔄 AKS with GPU: Kubernetes on Azure with GPU nodes
🔄 Container Instances: Serverless container deployment
🔄 Azure Functions: Serverless inference patterns
🔄 Blob Storage: Model artifact storage
🔄 Azure Monitor: Metrics and logging
🔄 Cost Optimization: Spot VMs and auto-scaling

🚀 Quick Start¶

Azure GPU VM¶

# Create GPU VM
az vm create \
  --resource-group myResourceGroup \
  --name gpux-gpu-vm \
  --image UbuntuLTS \
  --size Standard_NC6s_v3 \
  --admin-username azureuser \
  --generate-ssh-keys

# Connect and install GPUX
ssh azureuser@<vm-ip>
sudo apt update
sudo apt install -y python3 python3-pip
pip3 install gpux

# Pull and serve model
gpux pull microsoft/DialoGPT-medium
gpux serve microsoft/DialoGPT-medium --port 8080

Docker on Azure¶

FROM python:3.11-slim

WORKDIR /app
RUN pip install gpux

# Pull model from Hugging Face Hub
RUN gpux pull microsoft/DialoGPT-medium

EXPOSE 8080
CMD ["gpux", "serve", "microsoft/DialoGPT-medium", "--port", "8080"]

💡 Key Takeaways¶

Success

✅ Azure GPU Support: NC, ND, NV series VMs ✅ AKS Integration: Kubernetes with GPU nodes ✅ Model Registry Integration: Pull models from Hugging Face Hub ✅ Auto-scaling: Virtual Machine Scale Sets ✅ Monitoring: Azure Monitor integration

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