A KGrid runtime for Knowledge Objects in a native python environment that connects to an activator using the proxy adapter.
python -m pip install kgrid-python-runtime
to download the latest packagepython -m kgrid_python_runtime
to start the runtimeNote: not currently being used. Docker file has been updated to the latest python version but not tested or published.
Download the latest image from docker hub: docker pull kgrid/kgrid-python-runtime
docker run --network host kgrid/kgrid-python-runtime
Or
docker run -it -p :5000:5000 -e KGRID_PROXY_ADAPTER_URL=http://host.docker.internal:8080 kgrid/kgrid-python-runtime
This starts the runtime pointed to an activator running on the same system at localhost:8080
The runtime exposes two endpoints which can be used to see the details of the runtime and what has been activated
GET /info
Displays details about the runtime such as the running version and status.
GET /endpoints
Displays a list of the activated endpoints in the engine.
Set these environment variables to customize your runtime’s settings.
KGRID_PYTHON_ENV_URL
http://localhost
KGRID_PYTHON_ENV_PORT
5000
KGRID_PROXY_ADAPTER_URL
Default value:http://localhost:8080
http://localhost:5000
.KGRID_PYTHON_CACHE_STRATEGY
never
, always
, or use_checksum
never
- existing objects from the activator are overwritten on every activation call.always
- existing objects stored in the runtime will never be re-downloaded from the activator and the local pyshelf and context.json files must be deleted and the runtime restarted for the objects to be replaced.use_checksum
- objects will look for a checksum in the deployment descriptor sent over during activation and only re-download the object if that checksum has changed.never
KGRID_PROXY_HEARTBEAT_INTERVAL
30
DEBUG
true
/false
false
Just like other knowledge objects, python objects have 4 basic parts: service.yaml, deployment.yaml, metadata.txt, and a payload that can be any number of python files.
The packaging spec for knowledge objects can be found here.
An example KO with naan of hello
, a name of neighbor
, api version of 1.0
, and endpoint welcome
,
a Deployment Specification might look like this:
/welcome:
post:
artifact:
- "src/hello.js"
engine: "python"
function: "main"
entry: "src/hello.js"
Where function
is the name of the main javascript entry function.
You would then execute this endpoint to see the code work:
POST <activator url>/<naan>/<name>/<api version>/<endpoint>
In this example: POST <activator url>/hello/neighbor/1.0/welcome
The Service Specification for this object would likewise then be
openapi: 3.0.2
info:
version: '1.0'
title: 'Hello neighbor'
description: An example of simple Knowledge Object
license:
name: GNU General Public License v3 (GPL-3)
url: >-
https://tldrlegal.com/license/gnu-general-public-license-v3-(gpl-3)#fulltext
contact:
name: KGrid Team
email: kgrid-developers@umich.edu
url: 'http://kgrid.org'
servers:
- url: /js/neighbor
description: Hello world
tags:
- name: KO Endpoints
description: Hello world Endpoints
paths:
/welcome:
post:
...
In the Service Specification the servers.url must match the naan and name of the object (/js/neighbor
) and the path must match the path in Deployment Specification (/welcome
).
The service spec conforms to the swagger OpenAPI spec.
If your python package requires other python packages,
simply specify them in a file called requirements.txt
at the root of your object:
package-name=0.1.5
other-package-name=1.3.5
third-package-name=1.5.4
Right now the python runtime only supports requirements.txt using pip, see the requirements.txt documentation.
That’s it! as long as the payload is written in valid python, and the object is built to the spec, you’re ready to go. Example python objects can be found in the example collection or in the example-objects directory in this repository.
Clone this project and set the environment variable: PYTHONPATH
to the project root. (For now the python runtime uses both the current working directory and the python path to create and manage KO storage.)
Example (Ubuntu): export PYTHONPATH={Project path}/kgrid-python-runtime
Run python kgrid_python_runtime/app.py
from the top level of the project.
Don’t forget to update the version file.
The following command will build both the .whl file and the .tar.gz
python -m build
For more information: https://packaging.python.org/en/latest/guides/distributing-packages-using-setuptools/
Note that the packaging did not work on Linux on Windows (failed to copy the version file)
Editing the cache directly from the runtime’s shelf will not propagate changes to the endpoints in the runtime. New KOs must come from the activator.
The runtime will attempt to load any Knowledge Objects that were previously loaded onto its shelf before registering with the activator and acquiring its objects. The shelf directory can be deleted if there is a need to get all objects fresh from the activator.