Wandb

Integration with

First thing first, you need to install wandb with

Create a free account then run

in your terminal. Follow the link to get an API token that you will need to paste, then you’re all set!

Saves model topology, losses & metrics

Optionally logs weights and or gradients depending on log (can be “gradients”, “parameters”, “all” or None), sample predictions if log_preds=True that will come from valid_dl or a random sample pf the validation set (determined by seed). n_preds are logged in this case.

Datasets can also be tracked:

• log_dataset can explicitly be set to the folder to track
• the name of the dataset can explicitly be given through dataset_name, otherwise it is set to the folder name

For custom scenarios, you can also manually use functions and log_model to respectively log your own datasets and models.

Learner.gather_args[source]

Gather config parameters accessible to the learner

log_dataset[source]

Log dataset folder

log_model[source]

Example of use:

Once your have defined your Learner, before you call to fit or fit_one_cycle, you need to initialize wandb:

To use Weights & Biases without an account, you can call wandb.init(anonymous='allow').

Then you add the callback to your or call to fit methods, potentially with SaveModelCallback if you want to save the best model:

Datasets and models can be tracked through the callback or directly through and functions.

For more details, refer to .