Python中如何实现基于队列的分布式深度学习任务处理

import pika  # 导入 RabbitMQ 的 Python 客户端库

# 连接 RabbitMQ 服务器
connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))
channel = connection.channel()

# 定义任务队列和结果队列
task_queue = 'task_queue'
result_queue = 'result_queue'

# 将任务分发到队列中
channel.queue_declare(queue=task_queue, durable=True)

def send_task(task):
    channel.basic_publish(
        exchange='',
        routing_key=task_queue,
        body=task,
        properties=pika.BasicProperties(
            delivery_mode=2,  # 消息持久化
        )
    )

# 计算节点从队列中接收任务并处理
channel.queue_declare(queue=result_queue, durable=True)

def callback(ch, method, properties, body):
    # 进行数据预处理和计算任务
    result = compute(body)

    # 将结果放回队列中
    ch.basic_publish(
        exchange='',
        routing_key=result_queue,
        body=result,
        properties=pika.BasicProperties(
            delivery_mode=2,  # 消息持久化
        )
    )
    ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_qos(prefetch_count=1)
channel.basic_consume(queue=task_queue, on_message_callback=callback)

# 开始监听任务队列
print(' [*] Waiting for tasks. To exit press CTRL+C')
channel.start_consuming()

# 结果聚合
results = []

def consume_results(channel, method, properties, body):
    results.append(body)

# 主节点监听结果队列
channel.basic_consume(queue=result_queue, on_message_callback=consume_results)
channel.start_consuming()

# 结果合并
final_result = merge(results)

# 关闭连接
channel.close()
connection.close()