nedo-vision-training 1.3.2

A comprehensive training service library for AI models in the Nedo Vision platform

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:

pip install nedo-vision-training

For GPU support with CUDA 12.1:

pip install nedo-vision-training[gpu] --extra-index-url https://download.pytorch.org/whl/cu121

For development with all tools:

pip install nedo-vision-training[dev]

Quick Start

Using the CLI

After installation, you can use the training service CLI:

# Show CLI help
nedo-training --help

# Check system dependencies and requirements
nedo-training doctor

# Start training service with authentication token
nedo-training run --token YOUR_TOKEN

# Start with custom server configuration
nedo-training run --token YOUR_TOKEN --server-host custom.server.com --server-port 60000

# Start with custom REST API port
nedo-training run --token YOUR_TOKEN --rest-api-port 8081

# Start with custom intervals
nedo-training run --token YOUR_TOKEN --system-usage-interval 30 --latency-check-interval 15

# Start with all custom configurations
nedo-training run --token YOUR_TOKEN \
  --server-host custom.server.com \
  --server-port 60000 \
  --rest-api-port 8081 \
  --system-usage-interval 30 \
  --latency-check-interval 15

Configuration Options

The service supports various configuration options:

Available Commands

• doctor: Check system dependencies and requirements (CUDA, NVIDIA drivers, etc.)
• run: Start the training service

Run Command Options

• --token: Authentication token for secure communication (required)
• --server-host: gRPC server host (default: localhost)
• --server-port: gRPC server port (default: 50051)
• --rest-api-port: Manager REST API port (default: 8081)
• --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.