A PyTorch Lightning Callback for pushing models to the Hugging Face Hub πŸ€—βš‘οΈ

Related tags

Deep Learningmachine-learninghuggingfacepytorch-lightning
Overview

hf-hub-lightning

Open In Colab

A callback for pushing lightning models to the Hugging Face Hub.

Note: I made this package for myself, mostly...if folks seem to be interested in it, we'll move this into huggingface_hub or an official PyTorch Lightning repo directly and archive this one. Since there are a lot of options/considerations, I decided to just post it separately for now as a proof of concept.

Setup

pip install hf-hub-lightning

Usage

To periodically upload your model ckpt and TensorBoard logs while training, you can do the following...

import pytorch_lightning as pl
from hf_hub_lightning import HuggingFaceHubCallback

train_dataloader = ...
model = YourCoolModel()
trainer = pl.Trainer(callbacks=[HuggingFaceHubCallback('your_username/model_id')])
trainer.fit(model, train_dataloader)

To load your model back from the huggingface hub, just do...

from huggingface_hub import hf_hub_download

ckpt_path = hf_hub_download('your_username/model_id', 'lit_model.ckpt')
model = YourCoolModel.load_from_checkpoint(ckpt_path)
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Comments
  • Saving only the best models

    Saving only the best models

    Hi, just curious how it is interconnected with PL internal checkpointing callback, and saving only the best models according to some given monitoring criteria like max validation accuracy etc.?

    # saves a file like: my/path/sample-mnist-epoch=02-val_loss=0.32.ckpt
checkpoint_callback = ModelCheckpoint(
    monitor="val_loss",
    dirpath="my/path/",
    filename="sample-mnist-{epoch:02d}-{val_loss:.2f}",
    save_top_k=3,
    mode="min",
)

trainer = Trainer(callbacks=[checkpoint_callback])

    see: https://pytorch-lightning.readthedocs.io/en/latest/common/weights_loading.html

    Thx :rabbit:

    opened by Borda 1
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