Deep Learning Model Hub

System Overview

The Deep Learning Model Hub is a full-stack web application designed to facilitate the sharing, discovery, testing, and deployment of deep learning models. It serves as a centralized platform where data scientists and ML engineers can upload models, browse existing models, and deploy them for inference.

Core Capabilities

• Model Management: Upload, version, and organize ML models
• Model Discovery: Browse, search, and filter models by various criteria
• Model Deployment: Deploy models as REST API endpoints
• User Management: Authentication, authorization, and user profiles
• Model Testing: Interactive web interface for model inference
• Performance Monitoring: Track model usage and performance metrics

High-Level Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Frontend      │    │    Backend      │    │    Database     │
│   (Next.js)     │◄──►│   (FastAPI)     │◄──►│ (PostgreSQL/    │
│                 │    │                 │    │  SQLite)        │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   CDN/Vercel    │    │  Container      │    │   File Storage  │
│   Hosting       │    │  Platform       │    │   (S3/Local)    │
│                 │    │  (Railway)      │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Architecture Patterns

1. Layered Architecture

The backend follows a layered architecture pattern:

• Presentation Layer: FastAPI endpoints and request/response handling
• Business Logic Layer: Services for model management, deployment, and user operations
• Data Access Layer: SQLAlchemy ORM models and database operations
• Infrastructure Layer: External services (S3, deployment platforms)

2. Model-View-Controller (MVC)

• Models: SQLAlchemy database models (User, Model, ModelVersion, ModelDeployment)
• Views: Next.js React components and pages
• Controllers: FastAPI route handlers and business logic services

3. Repository Pattern

Database operations are abstracted through SQLAlchemy ORM, providing a clean separation between business logic and data persistence.

4. Microservices-Ready Design

While currently monolithic, the system is designed with clear service boundaries that can be extracted into microservices:

• Authentication Service
• Model Management Service
• Deployment Service
• User Management Service

Technology Stack

Frontend Stack

Backend Stack

System Components

1. Frontend Components

Core Components

src/
├── app/                    # Next.js 13+ App Router
│   ├── layout.tsx         # Root layout with navigation
│   ├── page.tsx           # Landing page
│   ├── auth/              # Authentication pages
│   ├── dashboard/         # User dashboard
│   ├── models/            # Model browsing and details
│   └── deployments/       # Deployment management
├── components/
│   ├── Navigation.tsx     # Main navigation bar
│   ├── ThemeToggle.tsx    # Dark/light mode toggle
│   └── ui/                # Reusable UI components
└── lib/
    ├── api.ts            # API client and type definitions
    └── utils.ts          # Utility functions

2. Backend Components

Application Structure

app/
├── main.py               # FastAPI application entry point
├── core/
│   ├── config.py        # Application configuration
│   ├── database.py      # Database connection management
│   └── security.py      # Authentication utilities
├── api/
│   └── v1/
│       ├── auth.py      # Authentication endpoints
│       ├── users.py     # User management endpoints
│       ├── models.py    # Model management endpoints
│       └── deployments.py # Deployment endpoints
├── models/              # SQLAlchemy database models
├── schemas/             # Pydantic schemas for API
└── services/            # Business logic services

Data Models & Database Design

Entity Relationship Diagram

┌─────────────┐         ┌─────────────┐         ┌─────────────┐
│    User     │         │    Model    │         │ ModelVersion│
├─────────────┤         ├─────────────┤         ├─────────────┤
│ id (PK)     │────────┬│ id (PK)     │────────┬│ id (PK)     │
│ email       │        ││ name        │        ││ version     │
│ username    │        ││ description │        ││ changelog   │
│ full_name   │        ││ framework   │        ││ s3_path     │
│ password    │        ││ owner_id(FK)│        ││ model_id(FK)│
│ is_active   │        │└─────────────┘        │└─────────────┘
│ created_at  │        │                       │
└─────────────┘        │                       │
                       │ ┌─────────────────────┐│
                       └─│  ModelDeployment    ││
                         ├─────────────────────┤│
                         │ id (PK)             ││
                         │ name                ││
                         │ model_id (FK)       │┘
                         │ model_version_id(FK)│
                         │ deployment_type     │
                         │ status              │
                         │ endpoint_url        │
                         └─────────────────────┘

Database Models

User Model

class User(SQLAlchemyBaseUserTable):
    id: int (Primary Key)
    email: str (Unique, Index)
    username: str (Unique, Index)
    full_name: str
    hashed_password: str
    is_active: bool = True
    is_superuser: bool = False
    created_at: datetime
    updated_at: datetime

Model Entity

class Model(Base):
    id: int (Primary Key)
    name: str (Index)
    description: str
    framework: str
    model_type: str
    tags: JSON
    is_public: bool = True
    download_count: int = 0
    owner_id: int (Foreign Key -> User.id)
    created_at: datetime
    updated_at: datetime

Model Deployment

class ModelDeployment(Base):
    id: int (Primary Key)
    name: str
    model_id: int (Foreign Key -> Model.id)
    model_version_id: int (Foreign Key -> ModelVersion.id)
    deployment_type: str
    status: str
    endpoint_url: str
    created_at: datetime
    updated_at: datetime

API Design

RESTful API Structure

The API follows REST principles with consistent resource naming and HTTP methods:

/api/v1/
├── auth/
│   ├── POST /token          # Login (OAuth2 compatible)
│   ├── POST /register       # User registration
│   └── POST /refresh        # Token refresh
├── users/
│   ├── GET /me              # Current user profile
│   ├── PUT /me              # Update profile
│   └── GET /{id}            # User details (public)
├── models/
│   ├── GET /                # List models (with filters)
│   ├── POST /               # Upload new model
│   ├── GET /{id}            # Model details
│   ├── PUT /{id}            # Update model
│   ├── DELETE /{id}         # Delete model
│   └── POST /{id}/download  # Download model
└── deployments/
    ├── GET /                # List deployments
    ├── POST /               # Create deployment
    ├── GET /{id}            # Deployment details
    ├── PUT /{id}            # Update deployment
    ├── DELETE /{id}         # Delete deployment
    └── GET /{id}/logs       # Deployment logs

Authentication

The API uses OAuth2 with JWT tokens for authentication:

# Login Request
POST /api/v1/auth/token
Content-Type: application/x-www-form-urlencoded

username=user@example.com&password=secret

# Response
{
  "access_token": "eyJ0eXAiOiJKV1QiLCJhbGc...",
  "token_type": "bearer",
  "expires_in": 3600
}

Error Handling

Consistent error responses across all endpoints:

{
  "detail": "Error message",
  "status_code": 400,
  "timestamp": "2024-01-01T12:00:00Z",
  "path": "/api/v1/models/123"
}

Frontend Architecture

Next.js App Router Structure

The frontend uses Next.js 13+ App Router for enhanced performance and developer experience:

Page Organization

src/app/
├── layout.tsx              # Root layout (navigation, providers)
├── page.tsx               # Landing page
├── loading.tsx            # Global loading UI
├── error.tsx              # Global error boundary
├── not-found.tsx          # 404 page
├── auth/
│   ├── login/page.tsx     # Login page
│   └── register/page.tsx  # Registration page
├── dashboard/
│   ├── page.tsx           # User dashboard
│   ├── layout.tsx         # Dashboard layout
│   └── loading.tsx        # Dashboard loading
├── models/
│   ├── page.tsx           # Model listing
│   ├── [id]/page.tsx      # Model details
│   └── upload/page.tsx    # Model upload
└── deployments/
    ├── page.tsx           # Deployment listing
    └── [id]/page.tsx      # Deployment details

State Management

• React Context: Authentication state and user data
• React Query: Server state management and caching
• Local State: Component-specific state with useState/useReducer

UI Component Architecture

src/components/
├── ui/                    # Base UI components (shadcn/ui)
│   ├── Button.tsx
│   ├── Input.tsx
│   ├── Dialog.tsx
│   └── Table.tsx
├── forms/                 # Form components
│   ├── LoginForm.tsx
│   ├── ModelUploadForm.tsx
│   └── DeploymentForm.tsx
├── layout/                # Layout components
│   ├── Navigation.tsx
│   ├── Sidebar.tsx
│   └── Footer.tsx
└── features/              # Feature-specific components
    ├── ModelCard.tsx
    ├── DeploymentStatus.tsx
    └── UserProfile.tsx

Security Architecture

Authentication & Authorization

• JWT Tokens: Stateless authentication with configurable expiration
• Refresh Tokens: Secure token renewal mechanism
• Role-based Access: User and superuser roles
• Resource Ownership: Users can only modify their own resources

Data Validation

• Pydantic Schemas: Comprehensive input validation
• File Upload Validation: Type and size restrictions
• SQL Injection Protection: SQLAlchemy ORM parameterized queries

Security Headers

# FastAPI Security Middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["https://yourdomain.com"],
    allow_credentials=True,
    allow_methods=["GET", "POST", "PUT", "DELETE"],
    allow_headers=["*"],
)

# Security Headers
- X-Content-Type-Options: nosniff
- X-Frame-Options: DENY
- X-XSS-Protection: 1; mode=block
- Strict-Transport-Security: max-age=31536000

Environment Security

• Environment Variables: Sensitive data stored securely
• Secret Management: Platform-native secret storage
• API Key Rotation: Regular rotation of service API keys

Deployment Architecture

Multi-Environment Strategy

Development → Staging → Production
     ↓           ↓         ↓
  Local Dev   → Testing → Live Users

Container Strategy

• Development: Docker Compose for local multi-service setup
• Production: Railway/Render container platforms
• Scaling: Horizontal pod autoscaling based on CPU/memory

Cloud Platform Architecture

Environment Configuration

# Production Environment Variables
DATABASE_URL=postgresql://...
SECRET_KEY=super-secret-key
CORS_ORIGINS=https://yourdomain.com
S3_BUCKET_NAME=model-storage
S3_ACCESS_KEY=...
S3_SECRET_KEY=...
S3_REGION=us-east-1

Data Flow

User Authentication Flow

1. User submits credentials
2. Backend validates against database
3. JWT token generated and returned
4. Token stored in frontend
5. Token included in subsequent requests
6. Backend validates token for protected routes

Model Upload Flow

1. User selects model file
2. Frontend validates file type/size
3. File uploaded to backend
4. Backend stores file and metadata
5. Database record created
6. Success response returned

Model Deployment Flow

1. User initiates deployment
2. Backend creates deployment record
3. Model files prepared for deployment
4. Deployment platform API called
5. Endpoint URL received and stored
6. Deployment status updated

Real-time Updates

• Polling: Frontend polls for deployment status updates
• WebSockets: Real-time deployment logs (future enhancement)
• Webhooks: Platform notifications for deployment events

Scalability & Performance

Backend Scaling Strategy

• Stateless Design: No server-side sessions
• Load Balancing: Multiple backend instances
• Database Connection Pooling: Efficient connection reuse
• Async Processing: Non-blocking I/O operations

Database Performance

• Optimized Indexes: Strategic indexing for common queries
• Query Optimization: Efficient SQLAlchemy queries
• Connection Pooling: PostgreSQL connection management
• Read Replicas: Scale read operations (future)

Frontend Performance

• Code Splitting: Route-based and component-level splitting
• Static Site Generation: Next.js optimization
• CDN Delivery: Vercel edge network
• Image Optimization: Automatic WebP conversion

Caching Strategy

Infrastructure as Code

Docker Configuration

Backend Dockerfile

# Multi-stage build for optimization
FROM python:3.11-slim as requirements-stage
WORKDIR /tmp
RUN pip install poetry
COPY ./pyproject.toml ./poetry.lock* /tmp/
RUN poetry export -f requirements.txt --output requirements.txt --without-hashes

FROM python:3.11-slim
WORKDIR /app
COPY --from=requirements-stage /tmp/requirements.txt /app/requirements.txt
RUN pip install --no-cache-dir --upgrade -r /app/requirements.txt
COPY ./app /app/app
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

Docker Compose (Development)

version: '3.8'
services:
  backend:
    build: ./backend
    ports:
      - "8000:8000"
    environment:
      - DATABASE_URL=postgresql://user:pass@db:5432/modeldb
    depends_on:
      - db
    volumes:
      - ./backend:/app
      
  frontend:
    build: ./frontend
    ports:
      - "3000:3000"
    environment:
      - NEXT_PUBLIC_API_URL=http://localhost:8000
    volumes:
      - ./frontend:/app
      
  db:
    image: postgres:15
    environment:
      POSTGRES_DB: modeldb
      POSTGRES_USER: user
      POSTGRES_PASSWORD: pass
    volumes:
      - postgres_data:/var/lib/postgresql/data
    ports:
      - "5432:5432"

volumes:
  postgres_data:

Deployment Scripts

• Automated Deployment: Railway and Vercel integration
• Environment Management: Configuration via environment variables
• Database Migrations: Alembic migration workflow
• Health Checks: Container health monitoring

Monitoring & Observability

Logging Strategy

• Structured Logging: JSON format for easy parsing
• Log Levels: ERROR, WARN, INFO, DEBUG
• Centralized Logging: Platform-native log aggregation
• Request Tracing: Correlation IDs for request tracking

Health Checks

# Health Check Endpoint
@app.get("/health")
async def health_check():
    return {
        "status": "healthy",
        "timestamp": datetime.utcnow(),
        "version": "1.0.0",
        "database": await check_database_connection(),
        "storage": await check_storage_connection()
    }

Performance Metrics

• Response Time: API endpoint performance
• Error Rates: HTTP error status tracking
• Resource Usage: CPU, memory, disk utilization
• Business Metrics: Model uploads, downloads, deployments

Alerting

CI/CD Pipeline

GitHub Actions Workflow

name: CI/CD Pipeline
on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.11'
      - name: Install dependencies
        run: pip install -r requirements.txt
      - name: Run tests
        run: pytest --cov=app --cov-report=xml
      - name: Upload coverage
        uses: codecov/codecov-action@v3

  deploy:
    needs: test
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    steps:
      - name: Deploy to Railway
        run: railway deploy

Quality Gates

• All Tests Pass: Unit, integration, and E2E tests
• Code Coverage: Backend (80%+), Frontend (70%+)
• Security Scan: Dependency vulnerability assessment
• Performance Tests: Load testing for production releases

Deployment Strategy

• Branch Protection: Main branch requires PR approval
• Staging Environment: Automatic deployment from develop branch
• Production Deployment: Manual approval for main branch
• Rollback Strategy: Quick rollback capability

Testing Strategy