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更新时间：2022-06-15 03:21:29
我无法使插件正常工作，但是无论如何我都找到了更好的解决方案-Lambda Layers.这是一个好处，因为它减小了lambda的大小并允许代码/文件重用.有一个用于numpy和scipy的预先构建的lambda层，您可以使用，但是我建立了自己的lambda层，向我展示了它们如何工作.这是我的工作方式:
I was unable to make the plugin work but I found a better solution anyhow - Lambda Layers. This is a bonus because it reduces the size of the lambda and allows code/file reuse. There is a pre-built lambda layer for numpy and scipy that you can use, but I built my own to show myself how it all works. Here's how I made it work:
创建图层包:
制作一个依赖包zip-必须在运行时使用目录结构python/lib/python3.6/site-packages进行python查找
Make a dependencies package zip - Must use the directory structure python/lib/python3.6/site-packages for python to find during runtime
mkdir -p tmpdir/python/lib/python3.6/site-packages
pip install -r requirements.txt --no-deps -t tmpdir/python/lib/python3.6/site-packages
cd tmpdir zip -r ../py_dependencies.zip .
cd ..
rm -r tmpdir
将层zip推送到AWS-需要最新的awscli
Push layer zip to AWS - requires latest awscli
sudo pip install awscli --upgrade --user
sudo aws lambda publish-layer-version \
--layer-name py_dependencies \
--description "Python 3.6 dependencies [numpy=0.15.4]" \
--license-info "MIT" \
--compatible-runtimes python3.6 \
--zip-file fileb://py_dependencies.zip \
--profile python_dev_serverless
要在需要numpy的任何功能中使用，只需使用控制台中或上面的上传过程中显示的arn
To use in any function that requires numpy, just use the arn that is shown in the console or during the upload above
f1:
handler: index.handler_f_use_numpy
include:
- functions/f_use_numpy.py
layers:
- arn:aws:lambda:us-west-2:XXXXX:layer:py_dependencies:1
作为一项额外的好处，您还可以将诸如常量之类的常见文件推送到一层.这是我在Windows和Lambda上测试使用的方式:
As an added bonus, you can push common files like constants to a layer as well. Here's how I did it for testing use in windows and on the lambda:
import platform
\# Set common path
COMMON_PATH = "../../layers/common/"
if platform.system() == "Linux": COMMON_PATH = "/opt/common/"
def handler_common(event, context):
# Read from a constants.json file
with open(COMMON_PATH + 'constants.json') as f:
return text = json.load(f)