dark_hal/REMOTE_CONTROL_README.md

LLM_Train Remote Control

A standalone GUI application for remotely controlling and managing LLM_Train MCP servers. This allows you to connect to a running MCP server from a separate application and perform model management and inference operations remotely.

Features

🔗 Connection Management

• Connect to local or remote MCP servers
• Real-time connection status monitoring
• Automatic reconnection handling
• Server path configuration and browsing

🤖 Model Management

• List all available models from the server
• Remote model loading with configuration
• Context size and GPU layer settings
• Current model status display
• Model unloading capabilities

💬 Inference Interface

• Text generation with configurable parameters
• Chat mode for conversational interactions
• Adjustable temperature and max tokens
• Real-time output display
• Chat history management

📊 System Monitoring

• Real-time system information display
• GPU acceleration status
• Platform and architecture details
• Performance metrics

📝 Logging & Debugging

• Comprehensive operation logging
• Error tracking and display
• Log saving functionality
• Connection event monitoring

Quick Start

Option 1: Batch File (Windows)

# Double-click launch_remote_control.bat

Option 2: Direct Python Execution

python remotecontrol.py

Requirements

• Python 3.7+ with tkinter support
• Running MCP Server (from main LLM_Train application)
• Network access to the MCP server (if remote)

Usage Guide

1. Starting the Remote Control

1. Launch the application using one of the methods above
2. The GUI will open with the connection panel at the top

2. Connecting to MCP Server

1. Specify server path:

   • Default: mcp_server.py (local)
   • Browse to select different server script
   • Can be local file or network path

2. Click "Connect" button

3. Connection status will show:

   • 🔴 Disconnected (Red): Not connected
   • 🟢 Connected (Green): Successfully connected

3. Model Management

Models Tab:

• View available models: Automatically populated on connection
• Select model: Click on model in the list
• Configure parameters:
  • Context Size: 512 - 32768 tokens
  • GPU Layers: 0 - 100 layers
• Load model: Click "Load Model" button
• Monitor status: Current model display shows loaded model

4. Text Generation

Inference Tab:

• Enter prompt: Type or paste text in the input area
• Configure generation:
  • Max Tokens: 1 - 8192
  • Temperature: 0.0 - 2.0
• Generate: Click "Generate" button
• View output: Results appear in the output area
• Chat mode: Enable for conversational interface

5. System Information

System Tab:

• View system details: Platform, architecture, acceleration
• Monitor GPU status: CUDA, ROCm, Metal availability
• Check performance: Current acceleration method
• Refresh: Update information in real-time

6. Logging

Log Tab:

• Monitor operations: All actions are logged with timestamps
• Error tracking: Errors are highlighted and detailed
• Save logs: Export logs to text file for debugging
• Clear logs: Reset log display

Advanced Configuration

Server Connection Options

Local Server:

Server Path: mcp_server.py

Custom Path:

Server Path: C:\path\to\your\mcp_server.py

Network Server (if supported):

Server Path: \\network\path\mcp_server.py

Model Configuration

High-Performance Setup:

• Context Size: 8192+
• GPU Layers: Maximum supported
• Temperature: 0.1-0.3 for focused responses

Balanced Setup:

• Context Size: 4096
• GPU Layers: Auto-detected optimum
• Temperature: 0.7 for creative responses

CPU-Only Setup:

• Context Size: 2048
• GPU Layers: 0
• Temperature: 0.5-1.0

Generation Parameters

Creative Writing:

• Max Tokens: 1024+
• Temperature: 0.8-1.2

Code Generation:

• Max Tokens: 512
• Temperature: 0.1-0.3

Question Answering:

• Max Tokens: 256
• Temperature: 0.3-0.7

Troubleshooting

Connection Issues

"Server file not found"

• Verify the server path is correct
• Ensure the MCP server file exists
• Check file permissions

"Connection failed"

• Ensure the MCP server is not already running
• Check if the server script is executable
• Verify Python dependencies are installed

"Disconnected unexpectedly"

• Check server logs for errors
• Verify system resources are available
• Restart both applications

Model Loading Issues

"No models found"

• Ensure model library is configured in main application
• Verify model files exist in specified directories
• Check library settings and scan depth

"Failed to load model"

• Verify model file is not corrupted
• Check available system memory
• Reduce context size or GPU layers

"Out of memory"

• Reduce context size
• Lower GPU layers
• Close other applications

Generation Issues

"No model loaded"

• Load a model first using the Models tab
• Verify model loaded successfully
• Check current model display

"Generation timeout"

• Reduce max tokens
• Simplify the prompt
• Check system resources

"Invalid parameters"

• Verify temperature is between 0.0-2.0
• Ensure max tokens is reasonable
• Check prompt is not empty

Technical Details

MCP Protocol

• Uses JSON-RPC 2.0 over stdin/stdout
• Asynchronous request/response handling
• Automatic request ID management
• Error handling and recovery

Threading Model

• Main UI thread for interface
• AsyncIO event loop for MCP communication
• Background threads for I/O operations
• Thread-safe callback system

Security Considerations

• Local process communication only
• No network ports exposed
• Input validation on all parameters
• Error sanitization in logs

Integration Examples

Automated Workflows

# Example: Batch text generation
prompts = ["Explain AI", "Code a function", "Write a story"]
for prompt in prompts:
    # Use remote control to generate text
    # Save results to files

API Integration

# Example: Integration with other tools
remote_control = RemoteControlClient()
remote_control.connect("mcp_server.py")
result = remote_control.generate("Your prompt here")

Monitoring Scripts

# Example: System monitoring
while True:
    system_info = remote_control.get_system_info()
    log_performance_metrics(system_info)
    time.sleep(60)

Support and Development

Extending Functionality

• Add new MCP tool integrations
• Implement custom inference modes
• Create automation scripts
• Build monitoring dashboards

Contributing

• Follow Python coding standards
• Add comprehensive logging
• Include error handling
• Write unit tests

Reporting Issues

• Include full log output
• Specify system configuration
• Provide reproduction steps
• Attach relevant files

License

This remote control application is part of the LLM_Train project and follows the same licensing terms as the main application.