deeplearning/modulus/modulus-launch-v020/_modules/modulus/launch/logging/utils.html

Source code for modulus.launch.logging.utils

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import torch
from datetime import datetime
from modulus.distributed import DistributedManager


[docs]def create_ddp_group_tag(group_name: str = None) -> str: """Creates a common group tag for logging For some reason this does not work with multi-node. Seems theres a bug in PyTorch when one uses a distributed util before DDP Parameters ---------- group_name : str, optional Optional group name prefix. If None will use "DDP_Group_", by default None Returns ------- str Group tag """ dist = DistributedManager() if dist.rank == 0: # Store time stamp as int tensor for broadcasting tint = lambda x: int(datetime.now().strftime(f"%{x}")) time_index = torch.IntTensor( [tint(x) for x in ["m", "d", "y", "H", "M", "S"]] ).to(dist.device) else: time_index = torch.IntTensor([0, 0, 0, 0, 0, 0]).to(dist.device) if torch.distributed.is_available(): # Broadcast group ID to all processes torch.distributed.broadcast(time_index, src=0) time_string = f"{time_index[0]}/{time_index[1]}/{time_index[2]}_\ {time_index[3]}-{time_index[4]}-{time_index[5]}" if group_name is None: group_name = "DDP_Group" return group_name + "_" + time_string
© Copyright 2023, NVIDIA Modulus Team. Last updated on Sep 21, 2023.