watch

  less than a minute  

View the source code

Hooks into the given PyTorch model(s) to monitor gradients and the model’s computational graph.

watch(
    models: (torch.nn.Module | Sequence[torch.nn.Module]),
    criterion: (torch.F | None) = None,
    log: (Literal['gradients', 'parameters', 'all'] | None) = "gradients",
    log_freq: int = 1000,
    idx: (int | None) = None,
    log_graph: bool = (False)
) -> None

This function can track parameters, gradients, or both during training. It should be extended to support arbitrary machine learning models in the future.

Args
models A single model or a sequence of models to be monitored.
criterion The loss function being optimized (optional).
log Specifies whether to log gradients, parameters, or all. Set to None to disable logging. (default=“gradients”)
log_freq : How frequently to log gradients and parameters, expressed in batches. (default=1000)
idx Index used when tracking multiple models with wandb.watch. (default=None)
log_graph Whether to log the model’s computational graph. (default=False)
Raises
ValueError If wandb.init has not been called or if any of the models are not instances of torch.nn.Module.

Last modified February 19, 2025: 15f9f62