Use our callback to automatically save all the metrics and the loss values tracked in model.fit.

import wandb
from wandb.keras import WandbCallback
​
wandb.init(config={"hyper": "parameter"})
​
...  # code to set up your model in Keras
​
# 🧙 magic
model.fit(X_train, y_train,  validation_data=(X_test, y_test),
          callbacks=[WandbCallback()])

Usage Examples

Configuring the WandbCallback

The WandbCallback class supports a wide variety of logging configuration options: specifying a metric to monitor, tracking of weights and gradients, logging of predictions on training_data and validation_data, and more.

Check out the reference documentation for the keras.WandbCallback for details.

Frequently Asked Questions

How do I use Keras multiprocessing with wandb?

If you're setting use_multiprocessing=True and seeing an error like:

Error('You must call wandb.init() before wandb.config.batch_size')

then try this:

1. In the Sequence class construction, add: wandb.init(group='...')

2. In your main program, make sure you're using if __name__ == "__main__": and then put the rest of your script logic inside that.