# Nedo Vision Training Service
A distributed AI model training service for the Nedo Vision platform. This service manages training workflows, monitoring, and lifecycle management for computer vision models using RF-DETR architecture.
## Features
- **Configurable Training Service**: Automated training with customizable intervals and parameters
- **gRPC Communication**: Reliable communication with the vision manager and other services
- **Distributed Training**: Support for multi-GPU and distributed training scenarios
- **Real-time Monitoring**: System resource monitoring and training progress tracking
- **Cloud Integration**: AWS S3 integration for model storage and dataset management
- **Message Queue Support**: RabbitMQ integration for task queue management
## Installation
Install the package from PyPI:
```bash
pip install nedo-vision-training
```
For GPU support with CUDA 12.1:
```bash
pip install nedo-vision-training[gpu] --extra-index-url https://download.pytorch.org/whl/cu121
```
For development with all tools:
```bash
pip install nedo-vision-training[dev]
```
## Quick Start
### Using the CLI
After installation, you can use the training service CLI:
```bash
# Show CLI help
nedo-trainer --help
# Start training service with authentication token
nedo-trainer --token YOUR_TOKEN
# Start with custom server configuration
nedo-trainer --token YOUR_TOKEN --server-host custom.server.com --server-port 60000
# Start with custom system usage reporting interval (in seconds)
nedo-trainer --token YOUR_TOKEN --system-usage-interval 30
# Start with custom latency monitoring interval (in seconds)
nedo-trainer --token YOUR_TOKEN --latency-check-interval 15
```
### Configuration Options
The service supports various configuration options:
- `--token`: Authentication token for secure communication
- `--server-host`: gRPC server host (default: localhost)
- `--server-port`: gRPC server port (default: 50051)
- `--system-usage-interval`: System usage reporting interval in seconds (default: 30)
- `--latency-check-interval`: Latency monitoring interval in seconds (default: 10)
## Architecture
### Core Components
- **TrainingService**: Main service orchestrator for training workflows
- **RFDETRTrainer**: RF-DETR algorithm implementation with PyTorch backend
- **TrainerLogger**: Real-time training progress logging via gRPC
- **ResourceMonitor**: System resource monitoring (GPU, CPU, memory)
### Dependencies
The service relies on several key technologies:
- **PyTorch**: Deep learning framework with CUDA support
- **RF-DETR**: Roboflow's Real-time Detection Transformer
- **gRPC**: High-performance RPC framework
- **RabbitMQ**: Message queue for distributed task management
- **AWS SDK**: Cloud storage integration
- **NVIDIA ML**: GPU monitoring and management
## Development Setup
## Troubleshooting
### Common Issues
1. **gRPC Connection Timeouts**: Ensure the server host and port are correctly configured
2. **CUDA Out of Memory**: Reduce batch size or use gradient accumulation
3. **Missing Dependencies**: Reinstall with `pip install --upgrade nedo-vision-training`
### Support
For issues and questions:
- Check the logs for detailed error information
- Ensure your token is valid and not expired
- Verify network connectivity to the training manager
## License
This project is part of the Nedo Vision platform. Please refer to the main project license for usage terms.
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