HunYuan3D AI Docker Deployment Guide: Best Practices for Containerization

Introduction

This guide details how to deploy HunYuan3D AI using Docker containerization. Compared to local installation, Docker deployment offers better environment isolation and scalability. This guide will help you achieve efficient containerized deployment.

Prerequisites

System Requirements

Docker Engine 20.10+
Docker Compose 2.0+
NVIDIA Container Toolkit
4+ CPU cores
16GB+ RAM
CUDA-compatible NVIDIA GPU

Environment Setup

# Verify Docker installation
docker --version
docker-compose --version

# Verify NVIDIA support
nvidia-smi
nvidia-container-cli -V

Basic Deployment

1. Get Official Image

# Pull latest version
docker pull tencent/hunyuan3d-2:latest

# Or specific version
docker pull tencent/hunyuan3d-2:v2.1.0

2. Basic Run

docker run -d \
  --name hunyuan3d \
  --gpus all \
  -p 7860:7860 \
  -v $(pwd)/data:/app/data \
  tencent/hunyuan3d-2:latest

3. Verify Deployment

# Check container status
docker ps -a | grep hunyuan3d

# View logs
docker logs hunyuan3d

Advanced Configuration

Docker Compose Configuration

# docker-compose.yml
version: '3.8'

services:
  hunyuan3d:
    image: tencent/hunyuan3d-2:latest
    container_name: hunyuan3d
    runtime: nvidia
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - MODEL_CACHE_DIR=/app/cache
      - LOG_LEVEL=INFO
    volumes:
      - ./data:/app/data
      - ./cache:/app/cache
      - ./config:/app/config
    ports:
      - "7860:7860"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]

Environment Variables

# .env
HUNYUAN3D_VERSION=latest
MODEL_CACHE_SIZE=5GB
GPU_MEMORY_FRACTION=0.8
NUM_WORKERS=4
LOG_LEVEL=INFO

Performance Optimization

1. Container Resource Management

# Limit CPU and memory
docker run -d \
  --cpus=4 \
  --memory=16g \
  --memory-swap=20g \
  tencent/hunyuan3d-2

2. GPU Configuration

# gpu-config.yaml
compute:
  cuda_cache_size: "2GB"
  precision: "float16"
  batch_size: 1
  num_workers: 2

3. Cache Optimization

# cache-config.yaml
cache:
  model_cache_path: "/app/cache/models"
  texture_cache_path: "/app/cache/textures"
  max_cache_size: "10GB"
  cleanup_interval: "1h"

Multi-Container Deployment

1. Service Orchestration

# docker-compose.prod.yml
version: '3.8'

services:
  hunyuan3d-api:
    image: tencent/hunyuan3d-2:latest
    # API service configuration

  hunyuan3d-worker:
    image: tencent/hunyuan3d-2:latest
    # Worker node configuration

  hunyuan3d-cache:
    image: redis:alpine
    # Cache service configuration

  hunyuan3d-monitor:
    image: grafana/grafana
    # Monitoring service configuration

2. Load Balancing

# nginx.conf
upstream hunyuan3d {
    server hunyuan3d-1:7860;
    server hunyuan3d-2:7860;
    server hunyuan3d-3:7860;
}

Monitoring and Maintenance

1. Health Checks

healthcheck:
  test: ["CMD", "curl", "-f", "http://localhost:7860/health"]
  interval: 30s
  timeout: 10s
  retries: 3

2. Log Management

logging:
  driver: "json-file"
  options:
    max-size: "10m"
    max-file: "3"

3. Monitoring Metrics

monitoring:
  metrics:
    - container_cpu_usage
    - container_memory_usage
    - gpu_utilization
    - model_inference_time

Troubleshooting

1. Container Startup Issues

# Check GPU visibility
docker run --rm nvidia/cuda nvidia-smi

# Check port usage
netstat -tulpn | grep 7860

2. Performance Issues

# View container resource usage
docker stats hunyuan3d

# Monitor GPU usage
nvidia-smi -l 1

3. Network Issues

# Check network connectivity
docker network inspect bridge

# Test container communication
docker exec hunyuan3d ping redis

Production Deployment

1. Security Configuration

security:
  # Disable privileged mode
  privileged: false
  # Add security options
  security_opt:
    - no-new-privileges
  # Resource limits
  ulimits:
    nproc: 65535
    nofile:
      soft: 20000
      hard: 40000

2. Backup Strategy

# Volume backup
docker run --rm \
  -v hunyuan3d_data:/data \
  -v $(pwd):/backup \
  alpine tar czf /backup/data.tar.gz /data

3. Update Process

# Smooth update
docker-compose pull
docker-compose up -d --remove-orphans

Integration and Extensions

1. API Integration

Refer to API Documentation for interface configuration
Set up authentication and authorization
Configure CORS access

2. Blender Plugin

Refer to Blender Integration Guide
Configure plugin connection
Set up resource sharing

Next Steps

1. Deep Dive

2. Community Resources

Summary

Docker deployment of HunYuan3D AI enables flexible environment management and efficient resource utilization. This guide covers the complete process from basic deployment to production optimization, helping you build a stable and reliable 3D generation service.

This article is part of the HunYuan3D AI documentation series. For system overview, please refer to the Getting Started Guide.