README.md

Mode versioning with hugging face hub!

WIP This repo is a work in progress, so bugs are expected :)

What if we use a git-based cloud hosting serving (like hf hub) to version our models?

How it works

Model versioning means keeping track of a set of parameters and a model obtained using them.

So what if, each set of parameters defines a branch and each model trained using that specific set is a commit on that branch?

We can keep the main one untouched and use it to list all the branches with their parameters.

Example

Let's see an example (copied from ./example.py).

from pathlib import Path

from model import BoringModel
from versioning import ModelVersioningHandler

# define some parameters
all_params = [{"hidden_size": 8}, {"hidden_size": 16}, {"hidden_size": 32}]

# create our model versioning handler
model_versioning_handler = ModelVersioningHandler(
    local_dir=Path("./test"), repo_id="zuppif/versioning-test"
)

for params in all_params:
    # here we may want to train the model
    model = BoringModel(**params)
    # our handler will push and keep track of both params and model's weights
    model_versioning_handler(params, model)

The above code has three sets of parameters:

• {"hidden_size": 8}
• {"hidden_size": 16}
• {"hidden_size": 32}

These parameters are used to create BoringModel, we don't care about it, just keep in mind it's a pytorch model

So, to version our model, we will create 3 branches, one for each set of parameters, + main. Every time we change our model's weight but we use the same parameter, we will push on the same branch.

So, the main branch keeps a list of all the other branches

A branch is identified using the current parameters hash, it contains the model's weights and the parameters

Let's see inside params.json

Different runs with the same parameters will commit the model's weight to the same branch, making it easier to version it.