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.

import wandb
from wandb.fastai import WandbCallback

wandb.init()

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

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.

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

Custom parameters can also be given at that stage.

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

Example Code

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

Fastai v1

• Classify Simpsons characters: A simple demo to track and compare Fastai models
• Semantic Segmentation with Fastai: Optimize neural networks on self-driving cars

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.