在AWS Lambda上部署sentence-transformers的优化方案
原始方案的问题
最初我尝试在AWS Lambda上直接使用完整的sentence-transformers库来处理文本嵌入任务。这个方案的主要问题在于：
包体积过大：sentence-transformers及其依赖项（如PyTorch）的总大小超过250MB，而Lambda的部署包限制为250MB（未压缩）
冷启动时间长：大体积的依赖导致Lambda冷启动时需要较长的初始化时间
内存消耗高：完整版模型需要较大的内存，增加了Lambda运行成本
优化后的方案
技术栈调整
我最终采用了以下替代方案：
ONNX Runtime：替换PyTorch作为推理引擎
- 显著减小包体积（ONNX Runtime约50MB）
- 提供优化的推理性能
- 支持多种硬件加速选项
HuggingFace Tokenizers：单独使用tokenizers库
- 仅包含必要的文本预处理功能
- 体积小且高效（约10MB）
模型选择：all-MiniLM-L6-v2的ONNX版本
- 轻量级模型（约90MB）
- 专为生产环境优化的ONNX格式
实现细节
Lambda配置：
- Python 3.12运行时
- 内存设置为512MB（实测256MB也可运行）
- 超时时间15秒（足够处理批量请求）
部署包优化：
pip install –target ./package onnxruntime tokenizers
# 手动添加预转换的ONNX模型文件
cd package && zip -r ../function.zip .
代码结构：
import onnxruntime as ort
from tokenizers import Tokenizer
# 初始化模型和tokenizer
sess = ort.InferenceSession("model.onnx")
tokenizer = Tokenizer.from_file("tokenizer.json")
def lambda_handler(event, context):
# 文本预处理
inputs = tokenizer.encode(event["text"]).ids
# ONNX推理
outputs = sess.run(None, {"input_ids": [inputs]})
return {"embedding": outputs[0].tolist()}
性能对比
| 包体积 | ~250MB | ~150MB |
| 冷启动时间 | 3-5秒 | 1-2秒 |
| 每次调用延迟 | 300-500ms | 150-300ms |
| 内存使用 | 700MB+ | 300-400MB |
适用场景
这个优化方案特别适合：
未来还可以考虑进一步优化，如使用Quantized ONNX模型或尝试更小的蒸馏模型。
安装记录
# 1. 宿主机创建 layer 目录（必做）
mkdir -p $(pwd)/layer && \\
# 2. 仅安装依赖，不做任何清理（先保证依赖装成功）
docker run –rm -v $(pwd)/layer:/layer –entrypoint "" public.ecr.aws/lambda/python:3.12 \\
bash -c "
# 强制创建容器内路径
mkdir -p /layer/python/lib/python3.12/site-packages/ && \\
# 安装核心依赖（NumPy 1.x + ONNX Runtime + tokenizers）
pip install numpy==1.26.4 onnxruntime==1.17.0 tokenizers==0.15.2 \\
–no-cache-dir -t /layer/python/lib/python3.12/site-packages/ \\
–force-reinstall
"
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瘦身
# 进入宿主机的 layer 目录
cd $(pwd)/layer/python/lib/python3.12/site-packages/ && \\
# 清理冗余文件（宿主机操作，路径绝对存在）
rm -rf tests test examples docs doc *.egg-info && \\
find . -name '*.pyc' -delete && \\
find . -type d -name '__pycache__' -exec rm -rf {} + && \\
rm -rf onnxruntime/{tools,experimental,contrib} && \\
rm -rf tokenizers/{bindings,scripts}
