flowhive-agent 0.1.5

FlowHive User Server Agent

FlowHive Agent

User Server Agent - The compute node component of FlowHive distributed GPU task scheduling platform

Overview

FlowHive Agent is the compute node component that runs on GPU servers. It connects to the Control Server via WebSocket and handles:

• Node Registration & Heartbeat: Automatically registers with Control Server and maintains connection health
• Task Execution: Receives and executes tasks (Python scripts, shell commands, distributed training jobs)
• GPU Monitoring: Real-time GPU metrics collection (utilization, memory, processes) via NVML
• Log Streaming: Streams stdout/stderr logs back to Control Server in real-time
• Resource Management: Intelligent task scheduling based on GPU memory availability and priorities

Directory Structure

agent/
├── flowhive_agent/
│   ├── core/              # Core agent logic
│   │   ├── task.py        # Task models and state machine
│   │   ├── executor.py    # Task execution engine (subprocess, torchrun)
│   │   ├── task_manager.py # Task scheduler and manager
│   │   └── gpu_monitor.py # GPU monitoring via NVML
│   ├── cli/               # Command-line interface
│   │   └── flowhive.py    # Main CLI entry point
│   └── __init__.py
├── scripts/               # Demo and testing scripts
│   ├── demo_task_manager.py
│   └── demo_shell_test.py
├── tests/                 # Pytest test cases
├── pyproject.toml         # Package configuration
├── requirements.txt       # Python dependencies
└── README.md

Installation

Option 1: Install from PyPI (Recommended)

pip install flowhive-agent

Option 2: Install from Source

# Clone the repository
git clone https://github.com/Dramwig/FlowHive.git
cd FlowHive/agent

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On Linux/macOS:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Install in development mode
pip install -e .

Requirements

• Python 3.10 or higher
• NVIDIA GPU with CUDA drivers (for GPU monitoring)
• Operating System: Linux, Windows, or macOS

Quick Start

1. Configure Agent

After installation, configure the agent to connect to your Control Server:

# Set user credentials
flowhive config user.username "your-username"
flowhive config user.email "[e-mail]"
flowhive config user.password "your-password"

# Set Control Server URL
flowhive config control_base_url "http://127.0.0.1:8001"

# Set agent label (optional, for identification)
flowhive config label "gpu-server-01"

# Verify configuration
flowhive config

2. Start Agent

flowhive run

The agent will:

• Automatically register with the Control Server
• Start GPU monitoring (if NVIDIA GPU is available)
• Begin listening for task assignments
• Send periodic heartbeats

3. Test Locally (Without Control Server)

For quick testing of task execution without a Control Server:

cd agent
python scripts/demo_task_manager.py

Run custom commands:

python scripts/demo_task_manager.py "python -c \"print('Hello FlowHive!')\"" --timeout 10

Logs will be saved to agent_logs/ directory.

Configuration

WebSocket Connection

The agent communicates with Control Server via WebSocket. The protocol is automatically determined from the control_base_url:

• http:// → ws:// (Plain WebSocket)
• https:// → wss:// (Secure WebSocket, recommended for production)

Production Configuration Example

# Use HTTPS/WSS for secure communication (recommended)
flowhive config control_base_url "https://your-control-server.com"
flowhive config user.username "prod-user"
flowhive config user.password "secure-password"
flowhive config label "prod-gpu-node-01"

Configuration File Location

Configuration is stored in:

• Linux/macOS: ~/.config/flowhive/config.toml
• Windows: %USERPROFILE%\.config\flowhive\config.toml

Environment Variables

You can also use environment variables (they override config file):

export FLOWHIVE_CONTROL_URL="http://127.0.0.1:8001"
export FLOWHIVE_USERNAME="your-username"
export FLOWHIVE_PASSWORD="your-password"
flowhive run

Key Features

GPU Monitoring

• Real-time Metrics: GPU utilization, memory usage, temperature, power consumption
• Process Tracking: Per-process GPU memory allocation
• NVML Integration: Direct access to NVIDIA Management Library
• Multi-GPU Support: Automatic detection and monitoring of all available GPUs

Task Scheduling

• Memory-Aware Scheduling: Tasks scheduled based on available GPU memory
• Priority Queue: Support for task priorities and fair scheduling
• Concurrent Execution: Multiple tasks can run simultaneously if resources allow
• Retry Mechanism: Automatic retry for failed tasks with configurable policies

Task Execution

• Multiple Executors: Support for subprocess, torchrun, and shell commands
• Environment Variables: Inject custom environment variables per task
• Container Support: Execute tasks in containerized environments
• Distributed Training: Native support for PyTorch distributed training via torchrun

Reliability

• OOM Recovery: Automatic detection and handling of out-of-memory errors
• Heartbeat Service: Periodic health checks (1-5s interval) with Control Server
• Graceful Shutdown: Proper cleanup of running tasks on agent shutdown
• Log Streaming: Real-time stdout/stderr streaming to Control Server

Architecture

┌─────────────────────────────────────────────────────────────┐
│                      FlowHive Agent                         │
├─────────────────────────────────────────────────────────────┤
│  ┌──────────────┐   ┌──────────────┐   ┌─────────────────┐  │
│  │   CLI Tool   │   │ Task Manager │   │  GPU Monitor    │  │
│  │  (flowhive)  │   │              │   │   (NVML)        │  │
│  └──────┬───────┘   └──────┬───────┘   └────────┬────────┘  │
│         │                  │                    │           │
│         └──────────────────┼────────────────────┘           │
│                            │                                │
│                   ┌────────▼────────┐                       │
│                   │  WebSocket      │                       │
│                   │  Client         │                       │
│                   └────────┬────────┘                       │
└────────────────────────────┼────────────────────────────────┘
                             │
                             │ ws:// or wss://
                             │
                   ┌─────────▼──────────┐
                   │  Control Server    │
                   │  (FastAPI + WS)    │
                   └────────────────────┘

CLI Commands

# Configuration management
flowhive config                          # Show all configuration
flowhive config <key> <value>            # Set configuration value
flowhive config <key>                    # Get configuration value

# Run agent
flowhive run                             # Start agent and connect to Control Server

# Examples
flowhive config user.username "alice"
flowhive config control_base_url "https://control.example.com"
flowhive run

Development

Running Tests

# Install test dependencies
pip install pytest pytest-asyncio

# Run all tests
pytest tests/

# Run specific test
pytest tests/test_task_manager.py

# Run with coverage
pytest --cov=flowhive_agent tests/

Building Package

# Install build tools
pip install build twine

# Build distribution
python -m build

# Upload to PyPI (maintainers only)
twine upload dist/*

Troubleshooting

Agent Cannot Connect to Control Server

1. Verify Control Server is running and accessible
2. Check control_base_url configuration
3. Ensure firewall allows WebSocket connections
4. Check logs for connection errors

GPU Monitoring Not Working

1. Ensure NVIDIA drivers are installed: nvidia-smi
2. Verify nvidia-ml-py is installed: pip list | grep nvidia-ml-py
3. Check GPU permissions (may need to run as root/admin)

Tasks Stuck in Queue

1. Check GPU memory availability
2. Verify task resource requirements
3. Check agent logs for errors
4. Ensure agent is connected to Control Server

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Related Projects

• Control Server - Central scheduling and control component
• Web Client - Browser-based visualization and management UI
• FlowHive Documentation - Complete platform documentation

License

This Agent component is licensed under the MIT License. See LICENSE for details.

Note: This is only the Agent component. The Control Server and Web Client are proprietary software and not covered by this license.

Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions

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Made with ❤️ by the FlowHive Team