Live provides a console to execute python code via REST integration
Dependencies required:
live-python-server-1.0.0+
plugin-py-1.1.0+
live-3.24.0+
The python server can run via Docker container image available on Intelie Marketplace with some code validation and common machine learning libraries pre installed:
Tensorflow/Keras
Numpy
Pandas
Scikit-Learn
Matplotlib
Pytorch
The integration does not support image rendering.
First install plugin-py and check that is valid at the admin of plugins:
Python plugin valid at the admin
Configure the server host name and port:
Python server host and port configuration
The option to open python console will be available at the header:
It is possible to execute code or drag and drop a python file into the console area:
Python console
The result will be shown for each print statement call invocation:
Python console results and expressions
It is possible to clear, export, copy and paste files on the upper options:
Python console actions
Python Exceptions are captured and indicated in the response:
Python console exceptions
There are also a few modules, which are considered harmful, that are restricted by default:
Restricted modules
If the option “persist as result event” is checked the result will be persisted as events on Live storage. It is possible to custom the event_type to be persisted or use default “py”:
Event persitence
By executing the query __src:'pysrc' __type:'py' on pipes console it is possible to see the persisted results:
Events consumed
It is possible to execute for example Tensorflow and Keras statements and save results as other event types:
Tensorflow and Keras machine learning libraries code execution
By consuming the evets with __src:’pysrc’ it is possible to find result events with different event_types:
Events consumed with different event types
Reading data from Live storage
By executing run_query inside the console it is possible to retrieve data from events with Live incoming events:
from pipes.query import run_query
data = run_query(expression="=> random() * 200 as value every 2 seconds", span="last 10 seconds")
for i in data: print(data)
Or it is possible to retrieve data from an asset:
from pipes.query import run_query
data = run_query(expression="=> well('mywell') at the end", span="")
for i in data: print(data)
Structured result
For structured result as a object use send_event method specifying the object with fields as example below:
Python console structured result
Multiple files persistence
Live provides the multiple files support for executing and saving python statements
