未来已来：Docker 集群引领智能深度学习平台新纪元

 # 经典代码：基础深度学习环境构建

    
 FROM python:3.8-slim
    
  
    
 # 设置工作目录
    
 WORKDIR /app
    
  
    
 # 安装必要的系统依赖
    
 RUN apt-get update && apt-get install -y \
    
     build-essential \
    
     && rm -rf /var/lib/apt/lists/*
    
  
    
 # 安装深度学习框架及常用库
    
 RUN pip install --no-cache-dir tensorflow torch numpy pandas
    
  
    
 # 拷贝应用代码
    
 COPY . /app
    
  
    
 # 暴露端口（如有需要）
    
 EXPOSE 8080
    
  
    
 CMD ["python", "main.py"]

 # 前沿代码：基于 Docker Compose 的集群部署示例

    
 version: "3.8"
    
 services:
    
   master:
    
     image: my-deep-learning-master:latest
    
     deploy:
    
       replicas: 1
    
       resources:
    
     limits:
    
       cpus: "2.0"
    
       memory: 4G
    
     ports:
    
       - "8080:8080"
    
     environment:
    
       - ROLE=master
    
  
    
   worker:
    
     image: my-deep-learning-worker:latest
    
     deploy:
    
       replicas: 4
    
       resources:
    
     limits:
    
       cpus: "1.0"
    
       memory: 2G
    
     environment:
    
       - ROLE=worker

 # 创新代码：智能调度器示例

    
 import random
    
 import numpy as np
    
  
    
 class SmartScheduler:
    
     def __init__(self, nodes):
    
     self.nodes = nodes  # 节点列表，每个节点包含 CPU 和内存信息
    
  
    
     def evaluate_load(self):
    
     # 模拟获取每个节点当前负载
    
     return {node: random.uniform(0, 1) for node in self.nodes}
    
  
    
     def decide_allocation(self, task_requirements):
    
     loads = self.evaluate_load()
    
     # 选择负载最小的节点
    
     target_node = min(loads, key=loads.get)
    
     print(f"将任务分配给节点：{target_node}")
    
     # 模拟资源分配
    
     allocation = {"node": target_node, "cpu": task_requirements["cpu"], "memory": task_requirements["memory"]}
    
     return allocation
    
  
    
 if __name__ == "__main__":
    
     nodes = ["node1", "node2", "node3", "node4"]
    
     scheduler = SmartScheduler(nodes)
    
     # 假设任务需要1个CPU和2G内存
    
     task_requirements = {"cpu": 1, "memory": 2}
    
     allocation = scheduler.decide_allocation(task_requirements)
    
     print("资源分配方案：", allocation)