DEPLOYMENT_GUIDE.md

Memo Model Deployment Guide

🌐 Inference Provider Options

Your Memo model is live at: https://huggingface.co/likhonsheikh/memo

Currently, it's available as source code but not deployed by any Inference Provider. Here are your options:

Option 1: Request Inference Provider Support

Steps to Request Provider Support:

1. Go to your model page: https://huggingface.co/likhonsheikh/memo
2. Click "Ask for provider support" (as shown in your screenshot)
3. Fill out the deployment request form
4. Hugging Face will review and potentially deploy your model

What This Provides:

• ✅ Hosted API endpoints
• ✅ Scalable infrastructure
• ✅ Automatic scaling based on demand
• ✅ Professional SLA
• ✅ Global CDN distribution

Option 2: Self-Deploy with Your Infrastructure

Local Deployment

# Clone your model
git clone https://huggingface.co/likhonsheikh/memo

# Install dependencies
pip install -r requirements.txt

# Start the API server
python api/main.py

# Your API will be available at:
# http://localhost:8000

Docker Deployment

FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .
EXPOSE 8000

CMD ["python", "api/main.py"]

Option 3: Cloud Platform Deployment

AWS Deployment

# Using AWS Lambda
pip install aws-lambda-python-concurrency

# Deploy to AWS ECS/EKS
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 123456789012.dkr.ecr.us-east-1.amazonaws.com

# Use AWS SageMaker
aws sagemaker create-endpoint-config \
  --endpoint-config-name memo-config \
  --production-variants ModelName=memo,InitialInstanceCount=1,InstanceType=ml.m5.large

Google Cloud Platform

# Deploy to Google Cloud Run
gcloud run deploy memo-api \
  --source . \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated

# Use Vertex AI
gcloud ai models upload \
  --display-name=memo \
  --artifact-uri=gs://your-bucket/memo-model \
  --serving-container-ports=8000

Azure Deployment

# Deploy to Azure Container Instances
az container create \
  --resource-group memo-rg \
  --name memo-api \
  --image your-registry.azurecr.io/memo:latest \
  --ports 8000 \
  --cpu 2 \
  --memory 4

# Use Azure Machine Learning
az ml model create \
  --name memo \
  --path ./memo \
  --type mlflow_model

Option 4: Serverless Deployment

Vercel Deployment

{
  "version": 2,
  "builds": [
    {
      "src": "api/main.py",
      "use": "@vercel/python"
    }
  ],
  "routes": [
    {
      "src": "/(.*)",
      "dest": "api/main.py"
    }
  ]
}

Netlify Functions

// netlify/functions/memo.js
exports.handler = async (event, context) => {
  // Import your Memo model logic here
  const result = await processMemoRequest(event.body);
  
  return {
    statusCode: 200,
    headers: {
      'Content-Type': 'application/json'
    },
    body: JSON.stringify(result)
  };
};

🚀 Recommended Approach

For Production Use:

1. Request Hugging Face Provider Support (Easiest)
2. Self-host with Docker (Most control)
3. Cloud platform deployment (Best scalability)

For Development/Testing:

1. Local deployment (Fastest setup)
2. Vercel/Netlify (Quick deployment)

📊 Model Performance Considerations

Your Memo model requires:

• Memory: 4GB-16GB depending on tier
• GPU: Optional but recommended for faster inference
• Storage: ~5GB for model weights
• Network: Stable internet for model loading

🔧 API Endpoints

Once deployed, your API will provide:

• GET /health - Health check
• POST /generate - Generate video content
• GET /status/{request_id} - Check generation status
• GET /tiers - List available model tiers
• GET /models/info - Model information

💰 Cost Considerations

Hugging Face Inference API

• Pay-per-use pricing
• Automatic scaling
• No infrastructure management

Self-Hosting

• Fixed server costs
• Full control
• Requires DevOps management

Cloud Platforms

• Pay-as-you-go
• Managed infrastructure
• Enterprise-grade reliability

🎯 Next Steps

1. Decide on deployment strategy
2. Request provider support or self-deploy
3. Set up monitoring and logging
4. Configure auto-scaling if needed
5. Test API endpoints thoroughly

Your production-grade Memo implementation is ready for deployment!