Framework to create containers for DataCybernetic's PineXQ computing platform
Project description
PineXQ ProCon Framework
Computations in DC-Cloud are done by Workers running inside a ProcessingContainer, or short "ProCon", which is also the name of the framework. ProCon provides an unobtrusive wrapper around function definitions without introducing new semantics, allowing for a clean definition of the computational task, while handling all cloud-related communication and data-management transparently in the background. This removes the code and configuration required from the function implementation.
Installation
To install the package use the pip command, to either install from a package feed:
pip install pinexq-procon
Creating a container
To publish a function in a container it has to be a method of a class inheriting from the Step class.
from pinexq.procon.step import Step # import package
class MyStepCollection(Step): # define the container class
def calculate_square(self, x: float) -> float: # define a step function
"""Calculate the square of x
:param x: a float number
:returns: the square of x
"""
return x ** 2
# More step functions can go in the same class
if __name__ == '__main__': # add script guard
MyStepCollection() # run the container - this will spawn the cli
It is mandatory to annotate the types of parameters and return value. Docstrings are optional, but highly recommended.
The documentation has a detailed section about implementing processing-steps.
Running a Step-function locally
The Python file with the container is itself a cli-tool.
You get a list of all available commands with the --help parameter.
python ./my_step_file.py --help
With the run option you can call a function in the container directly and the result is written to the console.
python ./my_step_file.py run --function calculate_square --parameters "{'x': 5}"
25
You can find full list of available commands in the cli documentation. All possible parameters and environment variables are listed here.
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