WandbMetricsLogger

Logger that sends system metrics to W&B.

WandbMetricsLogger(
    log_freq: Union[LogStrategy, int] = "epoch",
    initial_global_step: int = 0,
    *args,
    **kwargs
) -> None

WandbMetricsLogger automatically logs the logs dictionary that callback methods take as argument to wandb.

This callback automatically logs the following to a W&B run page:

• system (CPU/GPU/TPU) metrics,
• train and validation metrics defined in model.compile,
• learning rate (both for a fixed value or a learning rate scheduler)

Notes:

If you resume training by passing initial_epoch to model.fit and you are using a learning rate scheduler, make sure to pass initial_global_step to WandbMetricsLogger. The initial_global_step is step_size * initial_step, where step_size is number of training steps per epoch. step_size can be calculated as the product of the cardinality of the training dataset and the batch size.

Arguments
log_freq	("epoch", "batch", or int) if "epoch", logs metrics at the end of each epoch. If "batch", logs metrics at the end of each batch. If an integer, logs metrics at the end of that many batches. Defaults to "epoch".
initial_global_step	(int) Use this argument to correcly log the learning rate when you resume training from some initial_epoch, and a learning rate scheduler is used. This can be computed as step_size * initial_step. Defaults to 0.

Methods

set_model

set_model(
    model
)

set_params

set_params(
    params
)