DEPLOYMENT_GUIDE.md

Deployment Guide for Hugging Face Spaces

This guide provides step-by-step instructions for deploying the Multi-Modal Knowledge Distillation application to Hugging Face Spaces.

📋 Pre-Deployment Checklist

✅ Project Structure Complete

• All required files and directories are present
• Python syntax validation passed
• Frontend files are properly structured

✅ Configuration Validated

• requirements.txt contains all necessary dependencies
• spaces_config.yaml is properly configured
• API endpoints are implemented and accessible

✅ Documentation Complete

• Comprehensive README.md with usage instructions
• API documentation included
• Troubleshooting guide provided

🚀 Deployment Steps

Step 1: Create Hugging Face Space

1. Go to Hugging Face Spaces

   • Visit https://huggingface.co/spaces
   • Click "Create new Space"

2. Configure Space Settings

   • Space name: multi-modal-knowledge-distillation (or your preferred name)
   • License: MIT
   • SDK: Gradio
   • Hardware: T4 small (minimum) or T4 medium (recommended)
   • Visibility: Public or Private (your choice)

3. Initialize Repository

   • Choose "Initialize with README"
   • Click "Create Space"

Step 2: Upload Project Files

Upload all the following files to your Space repository:

Core Application Files

app.py                    # Main FastAPI application
requirements.txt          # Python dependencies
spaces_config.yaml       # Hugging Face Spaces configuration
README.md                # Project documentation
.gitignore               # Git ignore rules

Source Code

src/
├── __init__.py          # Package initialization
├── model_loader.py      # Model loading utilities
├── distillation.py      # Knowledge distillation engine
└── utils.py             # Utility functions

Frontend Files

templates/
└── index.html           # Main web interface

static/
├── css/
│   └── style.css        # Application styles
└── js/
    └── main.js          # Frontend JavaScript

Directory Structure (will be created automatically)

uploads/                 # Uploaded model files
models/                  # Trained models
temp/                    # Temporary files
logs/                    # Application logs

Step 3: Configure Hardware

1. Go to Space Settings

   • Click on "Settings" tab in your Space
   • Navigate to "Hardware" section

2. Select Hardware

   • Minimum: T4 small (16GB RAM, 1x T4 GPU)
   • Recommended: T4 medium (32GB RAM, 1x T4 GPU)
   • For large models: A10G small or larger

3. Apply Changes

   • Click "Update hardware"
   • Your Space will restart with new hardware

Step 4: Monitor Deployment

1. Build Process

   • Watch the "Logs" tab for build progress
   • Build typically takes 5-10 minutes
   • Dependencies will be installed automatically

2. Common Build Issues

   • PyTorch installation: May take several minutes
   • CUDA compatibility: Ensure PyTorch version supports your hardware
   • Memory issues: Upgrade hardware if needed

3. Successful Deployment

   • Space status shows "Running"
   • Application is accessible via the Space URL
   • Health check endpoint responds correctly

🔧 Configuration Options

Environment Variables

You can set these in your Space settings:

# Server Configuration
PORT=7860                 # Default port (usually not needed)
HOST=0.0.0.0             # Default host

# Resource Limits
MAX_FILE_SIZE=5368709120  # 5GB max file size
MAX_MODELS=10            # Maximum teacher models
MAX_TRAINING_TIME=3600   # 1 hour training limit

# GPU Configuration
CUDA_VISIBLE_DEVICES=0   # GPU device selection

Hardware Recommendations

Use Case	Hardware	RAM	GPU	Cost
Demo/Testing	CPU Basic	16GB	None	Free
Small Models	T4 small	16GB	T4	Low
Production	T4 medium	32GB	T4	Medium
Large Models	A10G small	24GB	A10G	High

🧪 Testing Your Deployment

1. Health Check

curl https://your-space-name-username.hf.space/health

2. Web Interface

• Visit your Space URL
• Test file upload functionality
• Verify model selection works
• Check training configuration options

3. API Endpoints

Test key endpoints:

• GET / - Main interface
• POST /upload - File upload
• GET /models - List models
• WebSocket /ws/{session_id} - Real-time updates

🐛 Troubleshooting

Build Failures

PyTorch Installation Issues:

# Check if CUDA version is compatible
# Update requirements.txt if needed
torch==2.1.0+cu118

Memory Issues During Build:

• Upgrade to higher hardware tier
• Reduce dependency versions
• Remove unnecessary packages

Runtime Issues

Out of Memory:

• Increase hardware tier
• Reduce batch size in training
• Implement model sharding

Model Loading Failures:

• Check file format compatibility
• Verify Hugging Face model exists
• Ensure sufficient disk space

WebSocket Connection Issues:

• Check browser compatibility
• Verify firewall settings
• Try refreshing the page

Performance Issues

Slow Training:

• Upgrade to GPU hardware
• Increase batch size
• Use mixed precision training

High Memory Usage:

• Monitor system resources
• Implement automatic cleanup
• Reduce model cache size

📊 Monitoring and Maintenance

Logs and Monitoring

• Check Space logs regularly
• Monitor resource usage
• Set up alerts for failures

Updates and Maintenance

• Keep dependencies updated
• Monitor for security issues
• Regular cleanup of temporary files

Scaling Considerations

• Monitor user load
• Consider multiple Space instances
• Implement load balancing if needed

🔒 Security Best Practices

File Upload Security

• Validate all uploaded files
• Implement size limits
• Scan for malicious content

API Security

• Implement rate limiting
• Validate all inputs
• Use HTTPS only

Resource Protection

• Monitor resource usage
• Implement timeouts
• Automatic cleanup procedures

📈 Performance Optimization

Model Loading

• Cache frequently used models
• Implement lazy loading
• Use model compression

Training Optimization

• Use mixed precision
• Implement gradient checkpointing
• Optimize batch sizes

Frontend Performance

• Minimize JavaScript bundle
• Optimize CSS delivery
• Use CDN for static assets

🎯 Success Metrics

Your deployment is successful when:

✅ Functionality

• All API endpoints respond correctly
• File uploads work without errors
• Training completes successfully
• Model downloads work properly

✅ Performance

• Page loads in < 3 seconds
• Training starts within 30 seconds
• Real-time updates work smoothly
• Resource usage is within limits

✅ User Experience

• Interface is responsive on all devices
• Error messages are clear and helpful
• Progress tracking works accurately
• Documentation is accessible

📞 Support and Resources

• Hugging Face Spaces Documentation: https://huggingface.co/docs/hub/spaces
• FastAPI Documentation: https://fastapi.tiangolo.com/
• PyTorch Documentation: https://pytorch.org/docs/

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Your Multi-Modal Knowledge Distillation application is now ready for production deployment! 🎉