Fast.ai

If you're using fastai to train your models, W&B has an easy integration using the WandbCallback. Explore the details in interactive docs with examples →​
First install Weights & Biases and log in:
pip install wandb
wandb login
Then add the callback to the learner or fit method:
import wandb
from fastai.callback.wandb import *
​
# start logging a wandb run
wandb.init(project='my_project')
​
# To log only during one training phase
learn.fit(..., cbs=WandbCallback())
​
# To log continuously for all training phases
learn = learner(..., cbs=WandbCallback())
If you use version 1 of Fastai, refer to the Fastai v1 docs.
WandbCallback accepts the following arguments:
Args
Description
log
"gradients" (default), "parameters", "all" or None. Losses & metrics are always logged.
log_preds
whether we want to log prediction samples (default to True).
log_model
whether we want to log our model (default to True). This also requires SaveModelCallback.
log_dataset
False (default)
True will log folder referenced by learn.dls.path.
a path can be defined explicitly to reference which folder to log.
Note: subfolder "models" is always ignored.
dataset_name
name of logged dataset (default to folder name).
valid_dl
DataLoaders containing items used for prediction samples (default to random items from learn.dls.valid.
n_preds
number of logged predictions (default to 36).
seed
used for defining random samples.
For custom workflows, you can manually log your datasets and models:
log_dataset(path, name=None, medata={})
log_model(path, name=None, metadata={}) 
Note: any subfolder "models" will be ignored.
Examples
​Visualize, track, and compare Fastai models: A thoroughly documented walkthrough
​Image Segmentation on CamVid: A sample use case of the integration

Hugging Face

fastai v1

Last updated 8 months ago

Args	Description
log	"gradients" (default), "parameters", "all" or None. Losses & metrics are always logged.
log_preds	whether we want to log prediction samples (default to True).
log_model	whether we want to log our model (default to True). This also requires SaveModelCallback.
log_dataset	False (default) True will log folder referenced by learn.dls.path. a path can be defined explicitly to reference which folder to log. Note: subfolder "models" is always ignored.
dataset_name	name of logged dataset (default to folder name).
valid_dl	`DataLoaders` containing items used for prediction samples (default to random items from `learn.dls.valid`.
n_preds	number of logged predictions (default to 36).
seed	used for defining random samples.