First thing first, you need to install wandb with

pip install wandb

Create a free account then run

wandb login

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

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 of the validation set (determined by seed). n_preds are logged in this case.

If used in combination with SaveModelCallback, the best model is saved as well (can be deactivated with log_model=False).

Datasets can also be tracked:

if log_dataset is True, tracked folder is retrieved from learn.dls.path
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
Note: the subfolder "models" is always ignored

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

Example of use:

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

import wandb
wandb.init()

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

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

from fastai.callback.wandb import *

# To log only during one training phase
learn.fit(..., cbs=WandbCallback())

# To log continuously for all training phases
learn = learner(..., cbs=WandbCallback())

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

For more details, refer to W&B documentation.

	Type	Default	Details
`log`	`str`	`None`	What to log (can be `gradients`, `parameters`, `all` or None)
`log_preds`	`bool`	`True`	Whether to log model predictions on a `wandb.Table`
`log_preds_every_epoch`	`bool`	`False`	Whether to log predictions every epoch or at the end
`log_model`	`bool`	`False`	Whether to save the model checkpoint to a `wandb.Artifact`
`model_name`	`str`	`None`	The name of the `model_name` to save, overrides `SaveModelCallback`
`log_dataset`	`bool`	`False`	Whether to log the dataset to a `wandb.Artifact`
`dataset_name`	`str`	`None`	A name to log the dataset with
`valid_dl`	`TfmdDL`	`None`	If `log_preds=True`, then the samples will be drawn from `valid_dl`
`n_preds`	`int`	`36`	How many samples to log predictions
`seed`	`int`	`12345`	The seed of the samples drawn
`reorder`	`bool`	`True`	No Content

Wandb

`class` `WandbCallback`[source]

`Learner.gather_args`[source]

`log_dataset`[source]

`log_model`[source]

Example of use:

Wandb

class WandbCallback[source]

Learner.gather_args[source]

log_dataset[source]

log_model[source]

Example of use:

`class` `WandbCallback`[source]

`Learner.gather_args`[source]

`log_dataset`[source]

`log_model`[source]