The simplest machine learning library for deploying prototypes, conducting quality assurance, and tracking production model performance.
Catacomb's Python library can be installed from the PyPi registry:
pip install catacomb-ai
To test installation, run catacomb:
Usage: catacomb [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
build
push
run
The only file Catacomb expects is a system.py file that implements a class containing the __init__ and output() methods:
import catacomb
class UppercaseModel:
def __init__(self):
"""Initializing system and loading dependencies"""
self.variable = True
def output(self, text):
"""Performing inference and returning a prediction"""
return text.upper()
if __name__ == "__main__":
catacomb.connect(UppercaseModel, 'TEXT')Implementing the system interface allows Catacomb to auto-generate a UI for the system/model from the command line tool. Model hosting will fail unless all dependencies are defined within the current directory (i.e. a Pipfile or requirements.txt file is required).
Running Catacomb locally can be done by running python system.py.
Uploading to the Catacomb hosting platform can be done by running:
catacomb upload
and following the command-line prompts to configure meta-data and example test cases.
Additional external dependencies can be installed by specifying a catacomb.sh bash file to run on the created image. This file is detected during the catacomb upload build process.