DeepSeek-R1-Distill-Qwen-7B模型持续集成与交付实践-洪萨配资

DeepSeek-R1-Distill-Qwen-7B模型持续集成与交付实践

你是不是也有过这样的经历？好不容易把模型部署好了，结果发现新版本出来了，又要重新折腾一遍。或者团队里有人改了代码，结果把整个推理服务搞崩了，大家互相甩锅。更头疼的是，每次部署都要手动操作，一不小心就出错，排查起来又费时费力。

这些问题在模型开发和部署过程中太常见了。今天我就来分享一套完整的解决方案——为DeepSeek-R1-Distill-Qwen-7B模型建立CI/CD流水线。这套方案能让你的模型部署从“手工活”变成“自动化流水线”，大大提升效率和可靠性。

1. 为什么需要CI/CD？

在聊具体怎么做之前，咱们先说说为什么需要CI/CD。很多人觉得模型部署不就是跑几个命令吗，搞那么复杂干嘛？其实不然。

想象一下这样的场景：你的团队有3个人在开发基于DeepSeek-R1-Distill-Qwen-7B的应用。A同学改了模型加载逻辑，B同学优化了推理代码，C同学调整了API接口。如果没有CI/CD，每次有人提交代码，你都得手动测试、手动部署，效率低下不说，还容易出错。

有了CI/CD之后，情况就完全不一样了。代码一提交，自动跑测试；测试通过，自动构建镜像；镜像构建成功，自动部署到测试环境；测试环境验证通过，一键发布到生产环境。整个过程全自动化，省时省力，还能保证质量。

2. 环境准备与基础配置

2.1 硬件与软件要求

首先得确保你的环境满足基本要求。DeepSeek-R1-Distill-Qwen-7B这个模型对硬件的要求不算太高，但也不能太寒酸。

硬件建议：

CPU：至少8核，推荐16核以上
内存：至少16GB，推荐32GB以上
存储：至少60GB可用空间
GPU：可选，如果有NVIDIA GPU（8GB显存以上）效果会更好

软件环境：

操作系统：Ubuntu 20.04/22.04 LTS，或者CentOS 8+
Docker：版本20.10+
Docker Compose：版本2.0+
Git：版本2.30+
Python：版本3.8+

2.2 项目结构设计

好的项目结构是成功的一半。咱们先来设计一个清晰的项目目录结构：

deepseek-r1-ci-cd/ ├── .github/ │ └── workflows/ │ ├── ci.yml # 持续集成工作流 │ └── cd.yml # 持续部署工作流 ├── src/ │ ├── model/ │ │ ├── __init__.py │ │ ├── loader.py # 模型加载逻辑 │ │ └── inference.py # 推理逻辑 │ ├── api/ │ │ ├── __init__.py │ │ └── server.py # FastAPI服务 │ └── tests/ │ ├── __init__.py │ ├── test_model.py # 模型测试 │ └── test_api.py # API测试 ├── docker/ │ ├── Dockerfile # 基础镜像 │ └── docker-compose.yml # 服务编排 ├── scripts/ │ ├── setup.sh # 环境设置脚本 │ ├── test.sh # 测试脚本 │ └── deploy.sh # 部署脚本 ├── requirements.txt # Python依赖 ├── .gitignore ├── README.md └── config.yaml # 配置文件

这个结构看起来清晰明了，每个文件都有明确的职责。接下来咱们一步步实现。

3. 持续集成流水线搭建

3.1 GitHub Actions配置

GitHub Actions是目前最流行的CI/CD工具之一，免费额度足够个人和小团队使用。咱们先来配置基础的CI工作流。

在.github/workflows/ci.yml文件中添加以下内容：

name: CI Pipeline on: push: branches: [ main, develop ] pull_request: branches: [ main ] jobs: test: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v3 - name: Set up Python uses: actions/setup-python@v4 with: python-version: '3.9' - name: Install dependencies run: | python -m pip install --upgrade pip pip install -r requirements.txt pip install pytest pytest-cov - name: Run unit tests run: | pytest src/tests/ -v --cov=src --cov-report=xml - name: Upload coverage uses: codecov/codecov-action@v3 with: file: ./coverage.xml fail_ci_if_error: false lint: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v3 - name: Set up Python uses: actions/setup-python@v4 with: python-version: '3.9' - name: Install linting tools run: | pip install black flake8 isort - name: Check code formatting run: | black --check src/ - name: Run linter run: | flake8 src/ --count --select=E9,F63,F7,F82 --show-source --statistics - name: Check imports ordering run: | isort --check-only src/

这个配置做了几件事：代码提交或PR创建时自动触发，运行单元测试并生成覆盖率报告，检查代码格式和代码质量。

3.2 模型测试策略

测试大模型和测试普通代码不太一样，因为模型推理有随机性，而且速度比较慢。咱们需要设计一套适合的测试策略。

在src/tests/test_model.py中：

import pytest import torch from unittest.mock import Mock, patch from src.model.loader import ModelLoader from src.model.inference import ModelInference class TestModelLoading: """测试模型加载功能""" def test_model_initialization(self): """测试模型初始化""" loader = ModelLoader() # 使用mock避免实际加载大模型 with patch('transformers.AutoModelForCausalLM.from_pretrained') as mock_load: mock_model = Mock() mock_load.return_value = mock_model model = loader.load_model("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B") assert model is not None mock_load.assert_called_once() def test_model_config(self): """测试模型配置""" loader = ModelLoader() with patch('transformers.AutoConfig.from_pretrained') as mock_config: mock_config.return_value = Mock(max_length=4096) config = loader.get_model_config() assert config.max_length == 4096 class TestModelInference: """测试模型推理功能""" @pytest.fixture def mock_model(self): """创建mock模型""" model = Mock() model.generate = Mock(return_value=torch.tensor([[1, 2, 3]])) return model def test_generate_text(self, mock_model): """测试文本生成""" inference = ModelInference(mock_model) result = inference.generate("你好，请介绍一下你自己") assert result is not None assert len(result) > 0 mock_model.generate.assert_called_once() def test_generate_with_params(self, mock_model): """测试带参数的文本生成""" inference = ModelInference(mock_model) result = inference.generate( "写一个Python函数", max_length=100, temperature=0.7, top_p=0.9 ) assert result is not None # 验证参数是否正确传递 call_args = mock_model.generate.call_args assert call_args[1]['max_length'] == 100

这些测试用例覆盖了模型加载和推理的基本功能，而且使用了mock技术避免实际加载大模型，测试速度很快。

4. Docker容器化部署

4.1 Dockerfile编写

容器化是CI/CD的关键一环。咱们来编写一个优化的Dockerfile：

# 使用轻量级Python镜像 FROM python:3.9-slim # 设置工作目录 WORKDIR /app # 安装系统依赖 RUN apt-get update && apt-get install -y \ git \ curl \ build-essential \ && rm -rf /var/lib/apt/lists/* # 复制依赖文件 COPY requirements.txt . # 安装Python依赖 RUN pip install --no-cache-dir -r requirements.txt \ && pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu # 复制应用代码 COPY src/ ./src/ COPY config.yaml . # 创建非root用户 RUN useradd -m -u 1000 appuser && chown -R appuser:appuser /app USER appuser # 暴露端口 EXPOSE 8000 # 健康检查 HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ CMD curl -f http://localhost:8000/health || exit 1 # 启动命令 CMD ["uvicorn", "src.api.server:app", "--host", "0.0.0.0", "--port", "8000"]

这个Dockerfile有几个优化点：使用slim镜像减小体积，分层构建加快构建速度，创建非root用户提高安全性，添加健康检查确保服务可用性。

4.2 Docker Compose配置

单容器部署不够用，咱们还需要数据库、缓存等组件。用Docker Compose来编排：

version: '3.8' services: deepseek-api: build: . container_name: deepseek-api ports: - "8000:8000" environment: - MODEL_PATH=deepseek-ai/DeepSeek-R1-Distill-Qwen-7B - MAX_LENGTH=4096 - TEMPERATURE=0.7 volumes: - ./models:/app/models - ./logs:/app/logs healthcheck: test: ["CMD", "curl", "-f", "http://localhost:8000/health"] interval: 30s timeout: 10s retries: 3 start_period: 40s restart: unless-stopped networks: - deepseek-network redis: image: redis:7-alpine container_name: deepseek-redis ports: - "6379:6379" volumes: - redis-data:/data command: redis-server --appendonly yes restart: unless-stopped networks: - deepseek-network postgres: image: postgres:15-alpine container_name: deepseek-db environment: - POSTGRES_USER=deepseek - POSTGRES_PASSWORD=your_password_here - POSTGRES_DB=deepseek_db ports: - "5432:5432" volumes: - postgres-data:/var/lib/postgresql/data - ./init.sql:/docker-entrypoint-initdb.d/init.sql restart: unless-stopped networks: - deepseek-network nginx: image: nginx:alpine container_name: deepseek-nginx ports: - "80:80" - "443:443" volumes: - ./nginx.conf:/etc/nginx/nginx.conf - ./ssl:/etc/nginx/ssl depends_on: - deepseek-api restart: unless-stopped networks: - deepseek-network volumes: redis-data: postgres-data: networks: deepseek-network: driver: bridge

这个配置包含了API服务、Redis缓存、PostgreSQL数据库和Nginx反向代理，构成了一个完整的生产环境。

5. 持续部署流水线

5.1 自动化部署脚本

有了CI，接下来就是CD。咱们先写一个部署脚本：

#!/bin/bash # scripts/deploy.sh set -e # 遇到错误立即退出 # 颜色输出 RED='\033[0;31m' GREEN='\033[0;32m' YELLOW='\033[1;33m' NC='\033[0m' # No Color echo -e "${GREEN}开始部署 DeepSeek-R1-Distill-Qwen-7B 服务...${NC}" # 检查环境变量 if [ -z "$DEPLOY_ENV" ]; then echo -e "${YELLOW}警告: DEPLOY_ENV 未设置，使用默认值 'staging'${NC}" export DEPLOY_ENV="staging" fi # 根据环境选择配置 case $DEPLOY_ENV in "production") echo -e "${GREEN}部署到生产环境${NC}" COMPOSE_FILE="docker-compose.prod.yml" ;; "staging") echo -e "${GREEN}部署到测试环境${NC}" COMPOSE_FILE="docker-compose.staging.yml" ;; *) echo -e "${RED}错误: 未知环境 $DEPLOY_ENV${NC}" exit 1 ;; esac # 检查Docker Compose文件是否存在 if [ ! -f "$COMPOSE_FILE" ]; then echo -e "${RED}错误: 找不到 $COMPOSE_FILE${NC}" exit 1 fi # 停止并删除旧容器 echo -e "${YELLOW}清理旧容器...${NC}" docker-compose -f $COMPOSE_FILE down --remove-orphans # 拉取最新镜像 echo -e "${YELLOW}拉取最新镜像...${NC}" docker-compose -f $COMPOSE_FILE pull # 构建服务 echo -e "${YELLOW}构建服务...${NC}" docker-compose -f $COMPOSE_FILE build --no-cache # 启动服务 echo -e "${YELLOW}启动服务...${NC}" docker-compose -f $COMPOSE_FILE up -d # 等待服务就绪 echo -e "${YELLOW}等待服务就绪...${NC}" sleep 10 # 检查服务健康状态 if curl -f http://localhost:8000/health > /dev/null 2>&1; then echo -e "${GREEN} 服务部署成功！${NC}" echo -e "${GREEN}API地址: http://localhost:8000${NC}" echo -e "${GREEN}文档地址: http://localhost:8000/docs${NC}" else echo -e "${RED} 服务健康检查失败${NC}" echo -e "${YELLOW}查看日志: docker-compose -f $COMPOSE_FILE logs${NC}" exit 1 fi # 显示容器状态 echo -e "\n${YELLOW}容器状态:${NC}" docker-compose -f $COMPOSE_FILE ps

这个脚本做了环境检查、容器清理、镜像拉取、服务构建和健康检查，确保部署过程可靠。

5.2 GitHub Actions部署工作流

现在把部署流程集成到GitHub Actions中：

# .github/workflows/cd.yml name: CD Pipeline on: push: branches: [ main ] workflow_dispatch: # 允许手动触发 jobs: deploy-staging: runs-on: ubuntu-latest environment: staging steps: - name: Checkout code uses: actions/checkout@v3 - name: Set up Docker Buildx uses: docker/setup-buildx-action@v2 - name: Login to Docker Hub uses: docker/login-action@v2 with: username: ${{ secrets.DOCKER_USERNAME }} password: ${{ secrets.DOCKER_PASSWORD }} - name: Build and push Docker image uses: docker/build-push-action@v4 with: context: . push: true tags: | ${{ secrets.DOCKER_USERNAME }}/deepseek-r1:latest ${{ secrets.DOCKER_USERNAME }}/deepseek-r1:${{ github.sha }} - name: Deploy to staging uses: appleboy/ssh-action@v0.1.4 with: host: ${{ secrets.STAGING_HOST }} username: ${{ secrets.STAGING_USERNAME }} key: ${{ secrets.STAGING_SSH_KEY }} script: | cd /opt/deepseek-r1 git pull origin main DEPLOY_ENV=staging ./scripts/deploy.sh deploy-production: runs-on: ubuntu-latest needs: deploy-staging environment: production if: github.ref == 'refs/heads/main' steps: - name: Wait for staging tests run: | echo "等待测试环境验证..." sleep 300 # 等待5分钟，手动验证 - name: Deploy to production uses: appleboy/ssh-action@v0.1.4 with: host: ${{ secrets.PRODUCTION_HOST }} username: ${{ secrets.PRODUCTION_USERNAME }} key: ${{ secrets.PRODUCTION_SSH_KEY }} script: | cd /opt/deepseek-r1 git pull origin main DEPLOY_ENV=production ./scripts/deploy.sh - name: Send deployment notification uses: rtCamp/action-slack-notify@v2 env: SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK }} SLACK_CHANNEL: deployments SLACK_COLOR: ${{ job.status }} SLACK_TITLE: "DeepSeek-R1 生产环境部署完成" SLACK_MESSAGE: "版本: ${{ github.sha }}\n环境: 生产\n状态: ${{ job.status }}"

这个工作流实现了完整的部署流程：先部署到测试环境，等待人工验证，然后部署到生产环境，最后发送通知。

6. 监控与日志管理

6.1 集成Prometheus监控

监控是生产环境必不可少的一环。咱们来集成Prometheus：

# src/api/monitoring.py from prometheus_client import Counter, Histogram, generate_latest, CONTENT_TYPE_LATEST from fastapi import Response import time # 定义指标 REQUEST_COUNT = Counter( 'http_requests_total', 'Total HTTP requests', ['method', 'endpoint', 'status'] ) REQUEST_LATENCY = Histogram( 'http_request_duration_seconds', 'HTTP request latency', ['method', 'endpoint'] ) MODEL_INFERENCE_COUNT = Counter( 'model_inferences_total', 'Total model inference requests', ['model_name', 'status'] ) MODEL_INFERENCE_LATENCY = Histogram( 'model_inference_duration_seconds', 'Model inference latency', ['model_name'] ) def monitor_request(method, endpoint): """监控HTTP请求的装饰器""" def decorator(func): async def wrapper(*args, **kwargs): start_time = time.time() try: response = await func(*args, **kwargs) status = 'success' REQUEST_COUNT.labels(method=method, endpoint=endpoint, status='200').inc() except Exception as e: status = 'error' REQUEST_COUNT.labels(method=method, endpoint=endpoint, status='500').inc() raise e finally: duration = time.time() - start_time REQUEST_LATENCY.labels(method=method, endpoint=endpoint).observe(duration) return response return wrapper return decorator def get_metrics(): """获取Prometheus指标""" return Response(generate_latest(), media_type=CONTENT_TYPE_LATEST)

然后在API路由中使用这个监控：

# src/api/server.py from fastapi import FastAPI, HTTPException from .monitoring import monitor_request, get_metrics, MODEL_INFERENCE_COUNT, MODEL_INFERENCE_LATENCY import time app = FastAPI(title="DeepSeek-R1 API", version="1.0.0") @app.get("/health") @monitor_request("GET", "/health") async def health_check(): """健康检查端点""" return {"status": "healthy", "timestamp": time.time()} @app.post("/generate") @monitor_request("POST", "/generate") async def generate_text(request: TextRequest): """文本生成端点""" start_time = time.time() try: # 记录模型推理 MODEL_INFERENCE_COUNT.labels(model_name="DeepSeek-R1-Distill-Qwen-7B", status="started").inc() result = await model_inference.generate( request.prompt, max_length=request.max_length, temperature=request.temperature ) duration = time.time() - start_time MODEL_INFERENCE_LATENCY.labels(model_name="DeepSeek-R1-Distill-Qwen-7B").observe(duration) MODEL_INFERENCE_COUNT.labels(model_name="DeepSeek-R1-Distill-Qwen-7B", status="success").inc() return {"text": result, "inference_time": duration} except Exception as e: MODEL_INFERENCE_COUNT.labels(model_name="DeepSeek-R1-Distill-Qwen-7B", status="failed").inc() raise HTTPException(status_code=500, detail=str(e)) @app.get("/metrics") async def metrics(): """Prometheus指标端点""" return get_metrics()

6.2 结构化日志配置

好的日志能大大提升排查效率。咱们配置结构化日志：

# src/utils/logger.py import logging import json from datetime import datetime import sys class JSONFormatter(logging.Formatter): """JSON格式的日志格式化器""" def format(self, record): log_record = { "timestamp": datetime.utcnow().isoformat() + "Z", "level": record.levelname, "logger": record.name, "message": record.getMessage(), "module": record.module, "function": record.funcName, "line": record.lineno, } # 添加额外字段 if hasattr(record, 'request_id'): log_record['request_id'] = record.request_id if hasattr(record, 'user_id'): log_record['user_id'] = record.user_id if record.exc_info: log_record['exception'] = self.formatException(record.exc_info) return json.dumps(log_record) def setup_logger(name, level=logging.INFO): """设置日志记录器""" logger = logging.getLogger(name) logger.setLevel(level) # 避免重复添加handler if not logger.handlers: # 控制台输出 console_handler = logging.StreamHandler(sys.stdout) console_handler.setFormatter(JSONFormatter()) logger.addHandler(console_handler) # 文件输出 file_handler = logging.FileHandler('/app/logs/deepseek.log') file_handler.setFormatter(JSONFormatter()) logger.addHandler(file_handler) return logger # 请求ID中间件 import uuid from fastapi import Request from starlette.middleware.base import BaseHTTPMiddleware class RequestIDMiddleware(BaseHTTPMiddleware): async def dispatch(self, request: Request, call_next): request_id = str(uuid.uuid4()) request.state.request_id = request_id # 设置日志记录器的请求ID logger = logging.getLogger(__name__) old_factory = logger.makeRecord def record_factory(*args, **kwargs): record = old_factory(*args, **kwargs) record.request_id = request_id return record logger.makeRecord = record_factory response = await call_next(request) response.headers["X-Request-ID"] = request_id return response

7. 安全与性能优化

7.1 安全配置

安全无小事，特别是对外提供API服务时：

# src/api/security.py from fastapi import Depends, HTTPException, status from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials import secrets from datetime import datetime, timedelta import jwt from typing import Optional security = HTTPBearer() # 配置 SECRET_KEY = secrets.token_urlsafe(32) ALGORITHM = "HS256" ACCESS_TOKEN_EXPIRE_MINUTES = 30 def create_access_token(data: dict, expires_delta: Optional[timedelta] = None): """创建访问令牌""" to_encode = data.copy() if expires_delta: expire = datetime.utcnow() + expires_delta else: expire = datetime.utcnow() + timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES) to_encode.update({"exp": expire}) encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM) return encoded_jwt def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)): """验证令牌""" token = credentials.credentials try: payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM]) return payload except jwt.ExpiredSignatureError: raise HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail="Token已过期", headers={"WWW-Authenticate": "Bearer"}, ) except jwt.InvalidTokenError: raise HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail="无效的Token", headers={"WWW-Authenticate": "Bearer"}, ) # 速率限制 from slowapi import Limiter, _rate_limit_exceeded_handler from slowapi.util import get_remote_address from slowapi.errors import RateLimitExceeded limiter = Limiter(key_func=get_remote_address) def setup_rate_limiting(app): """设置速率限制""" app.state.limiter = limiter app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler) # 不同的端点有不同的限制 limiter.limit("100/minute")(app.get("/health")) limiter.limit("10/minute")(app.post("/generate"))

7.2 性能优化

大模型推理比较耗资源，需要一些优化策略：

# src/model/optimization.py import torch from functools import lru_cache import asyncio from concurrent.futures import ThreadPoolExecutor import time class ModelOptimizer: """模型优化器""" def __init__(self, model, device=None): self.model = model self.device = device or ("cuda" if torch.cuda.is_available() else "cpu") self.executor = ThreadPoolExecutor(max_workers=2) # 移动到指定设备 self.model.to(self.device) # 设置为评估模式 self.model.eval() def optimize_for_inference(self): """优化模型用于推理""" # 使用半精度浮点数 if self.device == "cuda": self.model.half() # 启用CUDA图（如果可用） if torch.cuda.is_available(): torch.backends.cudnn.benchmark = True # 编译模型（PyTorch 2.0+） try: self.model = torch.compile(self.model) except: pass # 如果不支持编译，就跳过 @lru_cache(maxsize=100) def cached_generate(self, prompt: str, max_length: int = 100, temperature: float = 0.7): """带缓存的生成方法""" # 这里可以添加缓存逻辑 # 实际项目中可以使用Redis或Memcached return self._generate_impl(prompt, max_length, temperature) def _generate_impl(self, prompt, max_length, temperature): """实际的生成实现""" with torch.no_grad(): inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device) outputs = self.model.generate( **inputs, max_length=max_length, temperature=temperature, do_sample=True, top_p=0.9, pad_token_id=self.tokenizer.eos_token_id ) return self.tokenizer.decode(outputs[0], skip_special_tokens=True) async def async_generate(self, prompt, max_length=100, temperature=0.7): """异步生成方法""" loop = asyncio.get_event_loop() # 在线程池中运行推理 result = await loop.run_in_executor( self.executor, self.cached_generate, prompt, max_length, temperature ) return result def batch_generate(self, prompts, max_length=100, temperature=0.7): """批量生成""" results = [] # 简单的批量处理 for prompt in prompts: result = self.cached_generate(prompt, max_length, temperature) results.append(result) return results

8. 实际应用与扩展

8.1 多环境配置

实际项目中通常需要多个环境，咱们来配置不同环境的设置：

# config.yaml default: &default model: name: "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B" max_length: 4096 temperature: 0.7 api: host: "0.0.0.0" port: 8000 workers: 2 cache: redis_host: "redis" redis_port: 6379 ttl: 3600 development: <<: *default debug: true log_level: "DEBUG" model: device: "cpu" # 开发环境用CPU staging: <<: *default debug: false log_level: "INFO" model: device: "cuda" if available else "cpu" production: <<: *default debug: false log_level: "WARNING" model: device: "cuda" api: workers: 4 monitoring: enabled: true prometheus_port: 9090

8.2 扩展功能示例

最后，咱们来看几个实用的扩展功能：

# src/api/extensions.py from typing import List, Optional from pydantic import BaseModel import aiohttp from datetime import datetime class ChatMessage(BaseModel): """聊天消息""" role: str # "user" 或 "assistant" content: str class ChatRequest(BaseModel): """聊天请求""" messages: List[ChatMessage] stream: bool = False temperature: float = 0.7 class FileUploadRequest(BaseModel): """文件上传请求""" filename: str content: str class WebSearchRequest(BaseModel): """网页搜索请求""" query: str max_results: int = 5 class ExtendedAPI: """扩展API功能""" def __init__(self, model_inference): self.model_inference = model_inference async def chat_completion(self, request: ChatRequest): """聊天补全""" # 构建对话历史 conversation = "" for msg in request.messages: if msg.role == "user": conversation += f"User: {msg.content}\n" else: conversation += f"Assistant: {msg.content}\n" # 添加当前用户消息 if request.messages[-1].role == "user": conversation += f"Assistant: " # 生成回复 response = await self.model_inference.async_generate( conversation, temperature=request.temperature ) return {"message": response} async def process_file(self, request: FileUploadRequest): """处理文件内容""" # 使用DeepSeek-R1的文件处理模板 template = """[file name]: {filename} [file content begin] {content} [file content end] 请分析这个文件的内容。""" prompt = template.format( filename=request.filename, content=request.content[:10000] # 限制内容长度 ) analysis = await self.model_inference.async_generate(prompt) return {"analysis": analysis} async def web_search(self, request: WebSearchRequest): """网页搜索（示例）""" # 这里可以集成实际的搜索API # 比如Serper API、Google Custom Search等 search_results = [ { "title": "DeepSeek-R1 文档", "snippet": "DeepSeek-R1是一个强大的推理模型...", "url": "https://github.com/deepseek-ai/DeepSeek-R1" } ] # 使用搜索模板 search_template = """# 以下内容是基于用户发送的消息的搜索结果: {results} # 用户消息为： {query} 请根据搜索结果回答用户的问题。""" prompt = search_template.format( results="\n".join([f"[webpage {i}]\n{r['snippet']}" for i, r in enumerate(search_results)]), query=request.query ) answer = await self.model_inference.async_generate(prompt) return { "answer": answer, "sources": search_results }

9. 总结

整套CI/CD流水线搭建下来，你会发现模型部署和维护变得轻松多了。代码一提交，自动测试、自动构建、自动部署，整个过程全自动化。出现问题时，有完整的监控和日志，排查起来也很快。

实际用下来，这套方案在我们的项目中效果很不错。部署时间从原来的半小时缩短到几分钟，而且几乎不会出错。监控告警能及时发现问题，日志系统让排查效率大大提升。

当然，每个项目的情况都不一样，你可能需要根据实际情况调整。比如模型特别大，可能需要优化Docker镜像；访问量特别高，可能需要更复杂的负载均衡。但基本的思路是相通的：自动化、监控、安全、可扩展。

如果你刚开始接触CI/CD，建议先从简单的开始，把基础流程跑通，然后再逐步添加高级功能。遇到问题也不用怕，现在开源工具很丰富，社区支持也很好。最重要的是动手实践，在实际项目中积累经验。

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DeepSeek-R1-Distill-Qwen-7B模型持续集成与交付实践