fastai v1

  2 minute read  

For scripts using fastai v1, we have a callback that can automatically log model topology, losses, metrics, weights, gradients, sample predictions and best trained model.

import wandb
from wandb.fastai import WandbCallback

wandb.init()

learn = cnn_learner(data, model, callback_fns=WandbCallback)
learn.fit(epochs)
Python

Requested logged data is configurable through the callback constructor.

from functools import partial

learn = cnn_learner(
    data, model, callback_fns=partial(WandbCallback, input_type="images")
)
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It is also possible to use WandbCallback only when starting training. In this case it must be instantiated.

learn.fit(epochs, callbacks=WandbCallback(learn))
Python

Custom parameters can also be given at that stage.

learn.fit(epochs, callbacks=WandbCallback(learn, input_type="images"))
Python

Example Code

We’ve created a few examples for you to see how the integration works:

Fastai v1

Options

WandbCallback() class supports a number of options:

Keyword argument Default Description
learn N/A the fast.ai learner to hook.
save_model True save the model if it’s improved at each step. It will also load best model at the end of training.
mode auto min, max, or auto: How to compare the training metric specified in monitor between steps.
monitor None training metric used to measure performance for saving the best model. None defaults to validation loss.
log gradients gradients, parameters, all, or None. Losses & metrics are always logged.
input_type None images or None. Used to display sample predictions.
validation_data None data used for sample predictions if input_type is set.
predictions 36 number of predictions to make if input_type is set and validation_data is None.
seed 12345 initialize random generator for sample predictions if input_type is set and validation_data is None.

Last modified February 26, 2025: d8c9f41