watch
less than a minute
Hooks into the given PyTorch models 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 (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”, None]]) |
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. |
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