After function autoscaling support, it's impossible for qinling-engine to get execution log because it doesn't know which pod it is talking to. So, it's neccessary for the runtime to return execution logs to qinling engine. The qinling client is not affected. Change-Id: I96dfd00cc83d8b8a5e8c601ee3800b1ef1a45b1b
Qinling: Python Environment
This is the Python environment for Qinling.
It's a Docker image containing a Python 2.7 runtime, along with a dynamic loader. A few common dependencies are included in the requirements.txt file. End users need to provide their own dependencies in their function packages through Qinling API or CLI.
Rebuilding and pushing the image
You'll need access to a Docker registry to push the image, by default it's docker hub. After modification, build a new image and upload to docker hub:
docker build -t USER/python-runtime . && docker push USER/python-runtime
Using the image in Qinling
After the image is ready in docker hub, create a runtime in Qinling:
http POST http://127.0.0.1:7070/v1/runtimes name=python2.7 image=USER/python-runtime