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DesoMonitor Azure Deployment Guide

Option 1: Azure Container Instances (Recommended for simplicity)

Step 1: Create Dockerfile

FROM python:3.11-slim

WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    gcc \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application
COPY . .

# Create directory for logs and graphs
RUN mkdir -p /app/data

# Environment variables will be set in Azure
ENV PYTHONUNBUFFERED=1

# Run the monitor
CMD ["python", "deso_monitor.py"]

Step 2: Create requirements.txt

requests==2.31.0
python-dotenv==1.0.0
matplotlib==3.7.2
numpy==1.24.3

Step 3: Build and Deploy

# Build container
docker build -t desomonitor .

# Push to Azure Container Registry (ACR)
az acr create --resource-group myResourceGroup --name myregistry --sku Basic
az acr login --name myregistry
docker tag desomonitor myregistry.azurecr.io/desomonitor:latest
docker push myregistry.azurecr.io/desomonitor:latest

# Create container instance
az container create \
    --resource-group myResourceGroup \
    --name desomonitor \
    --image myregistry.azurecr.io/desomonitor:latest \
    --cpu 1 \
    --memory 1 \
    --restart-policy Always \
    --environment-variables \
        DESO_PUBLIC_KEY="your_public_key" \
        DESO_SEED_HEX="your_seed_hex"

Option 2: Azure App Service (Web Apps)

Free tier: F1 (1GB RAM, 1GB storage)

Requirements:

  • Remove matplotlib GUI backends (already done with 'Agg')
  • Handle file storage differently (Azure provides temp storage)

Deploy steps:

  1. Create App Service Plan (Free F1)
  2. Create Web App (Python 3.11)
  3. Deploy via Git or ZIP
  4. Set environment variables in Azure portal

Option 3: Azure Functions (Serverless)

Free tier: 1M requests/month + 400,000 GB-s

Challenges:

  • 10-minute execution limit (need to break into smaller functions)
  • Stateless (need external storage for measurements)

Option 4: Azure VM (Free for 12 months)

Free tier: B1S (1 vCPU, 1GB RAM)

Benefits:

  • Full control
  • Can run exactly as-is
  • SSH access

Recommended: Container Instances

Why ACI is best for DesoMonitor:

✅ Always running (restart-policy: Always) ✅ Simple deployment (single container) ✅ Cost effective (pay per second) ✅ No modifications needed (runs your code as-is) ✅ Environment variables (secure credential storage)

Estimated costs (after free tier):

  • Free tier: ~1 million vCPU-seconds/month FREE
  • Paid: ~$30-50/month for 24/7 operation

Alternative Free Options

GitHub Actions (Creative approach)

# .github/workflows/monitor.yml
name: DeSo Monitor
on:
  schedule:
    - cron: '*/10 * * * *'  # Every 10 minutes
  workflow_dispatch:

jobs:
  monitor:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3
    - name: Setup Python
      uses: actions/setup-python@v4
      with:
        python-version: '3.11'
    - name: Install dependencies
      run: pip install -r requirements.txt
    - name: Run single measurement
      env:
        DESO_PUBLIC_KEY: ${{ secrets.DESO_PUBLIC_KEY }}
        DESO_SEED_HEX: ${{ secrets.DESO_SEED_HEX }}
      run: python single_measurement.py

Railway.app (Free tier)

  • 500 hours/month free
  • Simple git deployment
  • Good for small applications

Heroku alternatives:

  • Render.com: Free tier available
  • fly.io: Free allowances
  • PythonAnywhere: Free tier with limitations

Next Steps

  1. Choose platform (I recommend Azure Container Instances)
  2. Modify code slightly for cloud environment
  3. Set up deployment
  4. Monitor costs (start with free tier)

Would you like me to help you set up any of these options?