System Environment Setup

1.1 Prerequisites Verification

Before deployment, ensure your Ubuntu 24.04 system meets these requirements:

• Memory: Minimum 16GB (for inference workloads)
• Storage: 50GB+ free space (including model files)
• Network: Internet access for dependency installation
• Permissions: Non-root user operation recommended

Verify system specifications with:

lsb_release -a
uname -m  # Check architecture (x86_64/ARM64)

1.2 Dependency Installation

Install foundational development tools:

sudo apt update
sudo apt install -y git wget curl python3.11 python3-pip \
    build-essential cmake libopenblas-dev liblapack-dev

Create isolated Python environment:

python3.11 -m venv openclaw_env
source openclaw_env/bin/activate
pip install --upgrade pip setuptools wheel

Containerized Model Deployment

2.1 Docker Installation

Choose between native SDK deployment or containerized approach (recommended for production). Install Docker with:

# Remove legacy versions
sudo apt remove docker docker-engine docker.io containerd runc
# Install dependencies
sudo apt install -y apt-transport-https ca-certificates gnupg-agent software-properties-common
# Add Docker GPG key
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
# Configure repository
echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
# Final installation
sudo apt update
sudo apt install -y docker-ce docker-ce-cli containerd.io
# Verify installation
sudo docker run hello-world

2.2 Service Containerization

Create docker-compose.yml configuration:

version: '3.8'
services:
  glm-service:
    image: registry.example.com/glm-4.7-flash:latest  # Replace with actual registry
    container_name: glm_service
    environment:
      MODEL_NAME: glm-4.7-flash
      MAX_TOKENS: 2048
      TEMPERATURE: 0.7
    ports:
      - "8080:8080"
    volumes:
      - ./model_data:/app/models
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]  # Optional for GPU support

Important pre-launch checks:

1. Configure registry mirrors for China-based deployments
2. Install NVIDIA drivers for GPU acceleration
3. Open firewall port 8080

Start service with:

sudo docker compose up -d

Interactive Configuration

3.1 Web Interface Setup

Access http://localhost:8080 to configure these parameters:

• Model Settings:

  • Temperature (0.1-1.0): Controls response creativity
  • Max Tokens: Limits response length
  • Repetition Penalty: Prevents redundant answers

• API Configuration:

  • Base URL: http://localhost:8080/v1
  • Authentication: Supports API Key or JWT

• Advanced Options:

  • Quantization: FP16/INT8 trade-offs
  • Batch Size: Throughput optimization

3.2 Configuration Generation

After parameter adjustment, generate JSON config:

{
  "model_config": {
    "model_name": "glm-4.7-flash",
    "max_tokens": 2048,
    "temperature": 0.7,
    "top_p": 0.9,
    "repetition_penalty": 1.2
  },
  "service_config": {
    "host": "0.0.0.0",
    "port": 8080,
    "workers": 4
  },
  "logging_config": {
    "level": "INFO",
    "format": "json"
  }
}

Secure configuration file:

chmod 600 config.json

Service Integration

4.1 REST API Example

Test with cURL:

curl -X POST http://localhost:8080/v1/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
  "prompt": "Explain quantum computing principles",
  "max_tokens": 150,
  "temperature": 0.5
}'

Sample response:

{
  "id": "cmpl-1234567890",
  "object": "text_completion",
  "created": 1687654321,
  "model": "glm-4.7-flash",
  "choices": [{
    "text": "Quantum computing utilizes superposition...",
    "index": 0,
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 8,
    "completion_tokens": 150,
    "total_tokens": 158
  }
}

4.2 Python SDK Integration

Install official client:

pip install glm-client

Sample implementation:

from glm_client import Client
client = Client(
    base_url="http://localhost:8080",
    api_key="YOUR_API_KEY"
)
response = client.completions.create(
    model="glm-4.7-flash",
    prompt="Implement quicksort in Python",
    max_tokens=200,
    temperature=0.3
)
print(response.choices[0].text.strip())

Production Optimization

5.1 Performance Tuning

• Batch Processing: Combine short requests (recommended batch size: 8-32)
• Caching Strategies: Implement LRU prompt caching and result caching
• Resource Monitoring:

  nvidia-smi -l 1  # GPU monitoring
  sudo docker stats glm_service  # Container metrics

5.2 Security Hardening

• Network Isolation: Deploy in dedicated subnet with IP restrictions
• Data Protection: Enable TLS encryption (Let’s Encrypt recommended)
• Access Control: Implement JWT authentication and API logging

Troubleshooting Guide

6.1 Startup Failures

• Port Conflicts:

  sudo netstat -tulnp | grep 8080

• Missing Dependencies: Check Docker logs with sudo docker logs glm_service
• Resource Constraints: Monitor with docker stats and consider quantization

6.2 Latency Optimization

• Model Quantization: Convert FP32 to INT8 (reduces inference time by 50%)
• Asynchronous Processing: Implement message queues for long requests
• Load Balancing: Deploy multiple instances with Nginx

This deployment framework enables rapid establishment of stable conversation services on Ubuntu 24.04. Adjust parameters based on specific requirements and implement comprehensive monitoring for production environments. For high-traffic scenarios, consider Kubernetes orchestration and object storage for model version management to build complete AI service ecosystems.