The simplest machine learning library for deploying prototypes, conducting quality assurance, and tracking production model performance.

Preview

Usage

Installation

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

Required Files

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.

Deployment

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.

External Dependencies

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.