Using Archives to Store Models

All the models listed on the Models page can be stored as and loaded from archives, simply by setting the location property to any supported archive format file.

Currently we support tar and zip formats of archives which can be paired with gzip, lzma and bzip2 compression.

archive.py

import asyncio

from dffml import Model, Features, Feature, train


async def main():
    # Load the model using the entrypoint listed on the model plugins page
    SLRModel = Model.load("slr")

    # Configure the model
    model = SLRModel(
        features=Features(Feature("Years", int, 1)),
        predict=Feature("Salary", int, 1),
        # You can set this as any archive format like zip, tgz, etc.
        location="slr-model.tar.gz",
    )

    # Train the model
    await train(
        model,
        {"Years": 0, "Expertise": 1, "Trust": 0.1, "Salary": 10},
        {"Years": 1, "Expertise": 3, "Trust": 0.2, "Salary": 20},
    )


if __name__ == "__main__":
    asyncio.run(main())

$ python archive.py