Thinking Claude 实战：如何构建高可用的 AI 推理服务架构

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AI 推理服务在生产环境中面临几个核心挑战：

长尾延迟(Long-tail Latency)：部分请求因资源竞争或模型初始化出现异常高延迟
GPU 资源竞争 ：多个模型实例共享 GPU 时显存溢出(OOM) 风险
模型热切换：版本更新需保证服务连续性，传统方案存在服务中断

每个请求独占 GPU 计算资源
高并发时显存碎片化严重
无法利用 tensor 并行计算优势

请求队列 + 批量调度器(Batch Scheduler)
动态合并可并行请求
CUDA 流 (CUDA Stream) 并发控制

@startuml
participant Client
participant "API Gateway" as Gateway
participant "Batch Queue" as Queue
participant "Inference Worker" as Worker
participant "GPU Memory" as GPU

Client -> Gateway: POST /predict
Gateway -> Queue: Enqueue(request)
Worker -> Queue: Dequeue(batch)
Worker -> GPU: Batch Inference
GPU --> Worker: Results
Worker --> Gateway: Response
Gateway --> Client: Return
@enduml

from threading import Lock
from queue import Queue
from typing import List, Optional
import time

class BatchQueue:
    def __init__(self, max_size: int = 100):
        self._queue = Queue()
        self._lock = Lock()
        self._max_size = max_size

    def put(self, item, timeout: float = 0.1) -> bool:
        with self._lock:
            if self._queue.qsize() >= self._max_size:
                return False
            self._queue.put(item, timeout=timeout)
            return True

    def get_batch(self, max_batch_size: int, timeout: float = 0.5) -> Optional[List]:
        start_time = time.time()
        batch = []

        while len(batch) < max_batch_size:
            try:
                remaining = timeout - (time.time() - start_time)
                if remaining <= 0:
                    break

                item = self._queue.get(timeout=min(0.1, remaining))
                batch.append(item)
            except Empty:
                break

        return batch if batch else None

import torch
from contextlib import contextmanager

@contextmanager
def cuda_memory_guard(max_alloc: float = 0.8):
    """显存使用保护上下文"""
    allocated = torch.cuda.memory_allocated() / (1024 ** 3)
    cached = torch.cuda.memory_reserved() / (1024 ** 3)

    if allocated > max_alloc:
        torch.cuda.empty_cache()

    try:
        yield
    finally:
        torch.cuda.empty_cache()

class ModelPool:
    def __init__(self):
        self._models = {}

    def load_model(self, model_path: str, version: str):
        with cuda_memory_guard():
            model = torch.load(model_path).half().cuda()
            self._models[version] = model

    def warmup(self, dummy_input):
        """预热 CUDA 内核"""
        for model in self._models.values():
            with torch.no_grad():
                model(dummy_input)