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umakrishnaswamy avatar umakrishnaswamy commented on July 24, 2024

hey @aoyulong - We currently don't support this functionality, but I'd love to hear more about your use case. in your workflow, which specific parameters / gradients would you like to monitor, and why is the current functionality of logging all of them then filtering post-run not working out?

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aoyulong avatar aoyulong commented on July 24, 2024

@umakrishnaswamy Logging all parameters/gradients online can be very costly, particularly during the training of large models. Therefore, it is advisable to monitor only key tensors. We suggest enhancing wandb.watch by introducing an additional argument to support a filtering function.

watch(
    models,
    criterion=None,
    log: Optional[Literal['gradients', 'parameters', 'all']] = "gradients",
    log_freq: int = 1000,
    idx: Optional[int] = None,
    log_graph: bool = (False),
    filter_function = None
)

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umakrishnaswamy avatar umakrishnaswamy commented on July 24, 2024

hey @aoyulong - happy to log this as a feature request and will be sure to update you as any progress arises on it

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