Wandb

Integration with Weights & Biases

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!

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WandbCallback

def WandbCallback(
    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`](https://docs.fast.ai/callback.tracker.html#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
):

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 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.

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Learner.gather_args

def gather_args(
    
):

Gather config parameters accessible to the learner

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Learner.gather_args

def gather_args(
    
):

Gather config parameters accessible to the learner

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log_dataset

def log_dataset(
    path, name:NoneType=None, metadata:dict={}, description:str='raw dataset'
):

Log dataset folder

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log_model

def log_model(
    path, name:NoneType=None, metadata:dict={}, description:str='trained model'
):

Log model file

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.