Agrippa

This python package is meant to assist in building/understanding/analyzing machine learning models. The core of the system is a markup language that can be used to specify a model architecture. This package contains utilities to convert that language into the ONNX format, which is compatible with a variety of deployment options and ML frameworks.

Installation

Agrippa can be installed with pip install agrippa. The requirements.txt file contains dependencies to run both the package and the tests found in the tests folder.

If you'd like to use the latest development version or contribute, you can clone this repo and run it in a virtual environment using:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

on 'nix based systems.

Usage

The principal function is export, which takes a project folder and compiles the contents into a .onnx file.

import agrippa

model_dir = '../path/to/dir'
agrippa.export(model_dir, 'outfile_name.onnx')

The function header for export is:

def export(
        infile,
        outfile=None,
        producer="Unknown",
        graph_name="Unknown",
        write_weights=True,
        suppress=False,
        reinit=False,
        bindings=None,
        log=False,  # Write a log file for the compilation
        log_filename=LOG_FILENAME,
        index=None  # Main file that things are imported into
    ):

Docs

Documentation is available on the Agrippa website under "Docs".

Examples

Examples of usage are available in the tests folder and on the Agrippa website.