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fastai v1
Note: 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.
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import wandb
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from wandb.fastai import WandbCallback
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wandb.init()
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learn = cnn_learner(data,
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model,
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callback_fns=WandbCallback)
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learn.fit(epochs)
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Requested logged data is configurable through the callback constructor.
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from functools import partial
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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.
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learn.fit(epochs, callbacks=WandbCallback(learn))
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Custom parameters can also be given at that stage.
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learn.fit(epochs, callbacks=WandbCallback(learn, input_type='images'))
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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
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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 1yr ago
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