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fastai v1


This documentation is for fastai v1. If you use the current version of fastai, you should refer to fastai page.

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


learn = cnn_learner(data,

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'))

It is also possible to use WandbCallback only when starting training. In this case it must be instantiated., callbacks=WandbCallback(learn))

Custom parameters can also be given at that stage., callbacks=WandbCallback(learn, input_type='images'))

Example Codeโ€‹

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

Fastai v1


WandbCallback() class supports a number of options:

Keyword argumentDefaultDescription
learnN/Athe learner to hook.
save_modelTruesave the model if it's improved at each step. It will also load best model at the end of training.
modeauto'min', 'max', or 'auto': How to compare the training metric specified in monitor between steps.
monitorNonetraining metric used to measure performance for saving the best model. None defaults to validation loss.
loggradients"gradients", "parameters", "all", or None. Losses & metrics are always logged.
input_typeNone"images" or None. Used to display sample predictions.
validation_dataNonedata used for sample predictions if input_type is set.
predictions36number of predictions to make if input_type is set and validation_data is None.
seed12345initialize random generator for sample predictions if input_type is set and validation_data is None.
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