weightie 0.0.1

Utility library for downloading and (de)serialising model weights for my little deep learning projects.

Weightie

Weightie is a little utility library for downloading, serialising (and deserialising) weights for my little deep learning projects.

You probably shouldn't use this if you're not me!

Serialisation

Quick-start usage:

>>> # Say your model weights are stored in some custom structure like so:

>>> from typing import NamedTuple
>>> from numpy.typing import NDArray

>>> class MyWeights(NamedTuple):
...     foo: NDArray
...     bar: list[NDArray]
...     baz: bool  # Data need not be entirely NDArrays


>>> # They can be dumped and loaded from disk like so:

>>> from weightie import dump, load

>>> weights = MyWeights(foo=np.zeros(1000), bar=np.zeros(10000), baz=True)
>>> with open("example.weights", "wb") as f:
...     dump(weights, f)

>>> with open("example.weights", "rb") as f:
...     loaded_weights = load(weights)

Weightie's serialisation format stores Python objects containing Numpy arrays such that, when loaded from disk, the Numpy arrays will be memory mapped directly from the files on disk. This means that data isn't actually read from disk until it is used and is easily swapped out of memory by the operating system when not in use.

See weightie/serialiser.py for details of the on-disk format (it's pretty simple!).

Weight Downloading

Weightie provides a download function which can be used to download weights filed published as assets on GitHub. (GitHub has very generous usage allowances for release assets!)

Quick-start usage:

>>> from weightie import download

>>> download(
...     repository="mossblaser/example",
...     asset_filenames=["foo.weights", "bar.weights"],
...     target_version="v1.2.3",
... )
{
    "foo": Path("/path/to/downloaded/v1.1.0-foo.weights"),
    "bar": Path("/path/to/downloaded/v1.1.0-bar.weights"),
}

The download function handles various fiddly aspects of downloading weights including:

• Downloading to platform-native data directories by default (using platformdirs)
• Re-using already downloaded weights when appropriate.
• Ensuring downloaded weight file versions are compatible with the running software version.
• Keeping multiple weight files versions in sync.

See weightie/downloader.py for the full list of options.