watch

function wandb.watch

wandb.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

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

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 (Union[torch.nn.Module, Sequence[torch.nn.Module]]): A single model or a sequence of models to be monitored. criterion (Optional[torch.F]): The loss function being optimized (optional). log (Optional[Literal[“gradients”, “parameters”, “all”]]): Specifies whether to log “gradients”, “parameters”, or “all”. Set to None to disable logging. (default=“gradients”) log_freq (int): Frequency (in batches) to log gradients and parameters. (default=1000) idx (Optional[int]): Index used when tracking multiple models with wandb.watch. (default=None) log_graph (bool): 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.