# Terradev Architecture

## Overview

Terradev is a comprehensive platform that automates the provisioning of GPU compute resources and connects them with open source models and datasets. The platform uses Terraform for infrastructure-as-code and provides a user-friendly interface for environment generation.

## Core Components

### 1. Model Registry
- **Purpose**: Discovers and catalogs open source models from various repositories
- **Sources**: Hugging Face Hub, TensorFlow Hub, PyTorch Hub
- **Features**: Model metadata, resource requirements, popularity scoring

### 2. Dataset Registry
- **Purpose**: Manages dataset discovery and integration
- **Sources**: Public datasets, custom data feeds
- **Features**: Automatic preprocessing, format conversion, versioning

### 3. Compute Optimizer
- **Purpose**: Recommends optimal GPU instances based on requirements
- **Features**: Cost optimization, performance matching, budget constraints

### 4. Environment Generator
- **Purpose**: Creates complete ML environments with a single command
- **Features**: Interactive setup, Terraform generation, dependency management

### 5. Infrastructure Modules
- **GPU Compute**: Auto-scaling GPU instances with monitoring
- **Storage**: S3, EFS, EBS integration
- **Data Feeds**: Automated data ingestion and processing
- **Networking**: VPC, security groups, load balancing

## How It Works

### 1. Model Discovery
```
User selects task type → Framework selection → Model catalog → Model selection
```

### 2. Dataset Integration
```
Task type → Available datasets → Dataset selection → Data ingestion
```

### 3. Compute Optimization
```
Model requirements + Dataset size → Instance matching → Cost analysis → Recommendation
```

### 4. Environment Generation
```
All selections → Terraform templates → Infrastructure deployment → Ready-to-use environment
```

## Technical Architecture

### Frontend Interface
- **CLI Tool**: `environment-generator.py` - Interactive environment setup
- **Query Tools**: `query-gpu.py`, `data-manager.py` - Resource management

### Backend Services
- **Model Registry Service**: Lambda-based model discovery
- **Data Processing Service**: Automated dataset preprocessing
- **Compute Optimization Engine**: Instance recommendation algorithm

### Infrastructure Layer
- **Terraform Modules**: Reusable infrastructure components
- **AWS Services**: EC2, S3, Lambda, API Gateway, CloudWatch
- **Multi-cloud Support**: AWS, GCP, Azure integration

### Data Flow
```
User Input → Model Registry → Dataset Registry → Compute Optimizer → Terraform Generation → AWS Deployment
```

## Supported Models and Frameworks

### Computer Vision
- **TensorFlow**: EfficientNet, ResNet, MobileNet
- **PyTorch**: ResNet, Vision Transformer, CLIP
- **Hugging Face**: ViT, CLIP, DETR

### Natural Language Processing
- **Hugging Face**: BERT, GPT-2, T5, RoBERTa
- **TensorFlow**: Universal Sentence Encoder, BERT
- **PyTorch**: TransformerXL, GPT

### Reinforcement Learning
- **PyTorch**: PPO, A2C, DQN (via Stable Baselines3)
- **TensorFlow**: TF-Agents implementations

### Time Series
- **TensorFlow**: LSTM, Prophet
- **PyTorch**: LSTM, GRU models

## Instance Types and Pricing

### AWS GPU Instances
| Instance | GPU Memory | RAM | On-Demand | Spot | Use Cases |
|----------|-------------|-----|-----------|------|-----------|
| p3.2xlarge | 16GB | 61GB | $3.06/hr | $0.92/hr | Medium training |
| p3.8xlarge | 64GB | 244GB | $12.24/hr | $3.68/hr | Large training |
| p4d.24xlarge | 320GB | 1152GB | $32.77/hr | $9.83/hr | Large models |
| g4dn.xlarge | 16GB | 16GB | $0.53/hr | $0.16/hr | Inference |
| g5.xlarge | 24GB | 16GB | $1.01/hr | $0.30/hr | Training/Inference |

## Security and Compliance

### Data Security
- **Encryption**: All data encrypted at rest and in transit
- **Access Control**: IAM roles and policies
- **Network Security**: VPC isolation, security groups

### Model Security
- **Model Verification**: Checksums and integrity validation
- **Secure Storage**: Encrypted model storage
- **Access Logging**: Audit trails for model access

## Cost Optimization

### Spot Instances
- **Cost Savings**: Up to 90% savings vs on-demand
- **Interruption Handling**: Automatic checkpointing
- **Mixed Strategy**: Combine spot and on-demand

### Auto-scaling
- **Dynamic Scaling**: Scale based on workload
- **Cost Controls**: Budget limits and alerts
- **Resource Optimization**: Right-sizing recommendations

## Monitoring and Observability

### Infrastructure Monitoring
- **CloudWatch Metrics**: CPU, GPU, memory utilization
- **Custom Dashboards**: Real-time performance views
- **Alerting**: Cost and performance alerts

### Model Monitoring
- **Training Metrics**: Loss, accuracy, convergence
- **Resource Usage**: GPU memory, compute time
- **Performance Tracking**: Model benchmarking

## Extensibility

### Adding New Models
1. Update model registry with new model metadata
2. Add requirements and compute specifications
3. Update discovery service

### Supporting New Clouds
1. Create cloud-specific Terraform modules
2. Update compute optimizer with pricing
3. Add cloud provider configurations

### Custom Frameworks
1. Add framework to enum definitions
2. Create framework-specific requirements
3. Update model discovery logic

## Use Cases

### 1. ML Research
- Rapid prototyping with different models
- Dataset experimentation
- Cost-effective training

### 2. Production Deployment
- Scalable inference endpoints
- Model versioning
- A/B testing support

### 3. Education
- Learning environments for students
- Pre-configured setups
- Budget controls

### 4. Enterprise
- Standardized environments
- Compliance and security
- Cost management

## Future Enhancements

### 1. Advanced Model Registry
- Model performance benchmarks
- Automated model selection
- Fine-tuning recommendations

### 2. Multi-cloud Orchestration
- Cross-cloud deployments
- Cost optimization across providers
- Disaster recovery

### 3. Collaboration Features
- Team workspaces
- Model sharing
- Experiment tracking

### 4. Advanced Analytics
- Usage analytics
- Cost optimization insights
- Performance recommendations

## Implementation Timeline

### Phase 1: Core Platform (Current)
- ✅ Basic infrastructure modules
- ✅ Model and dataset registries
- ✅ Environment generator
- ✅ AWS integration

### Phase 2: Enhanced Features
- 🔄 Advanced model discovery
- 🔄 Multi-cloud support
- 🔄 Performance optimization
- 🔄 Collaboration features

### Phase 3: Enterprise Features
- ⏳ Advanced security
- ⏳ Compliance frameworks
- ⏳ Advanced monitoring
- ⏳ Cost optimization

## Conclusion

Terradev provides a comprehensive solution for automating GPU compute provisioning and connecting it with open source models and datasets. The platform is designed to be extensible, cost-effective, and user-friendly, making it suitable for researchers, data scientists, and enterprises alike.

The modular architecture allows for easy customization and extension, while the Terraform-based infrastructure ensures reproducibility and scalability.
