Core (Latest Release)

Source code for modulus.launch.logging.utils

# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. # SPDX-FileCopyrightText: All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the “License”); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an “AS IS” BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from datetime import datetime import torch 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 def tint(x): return int("%{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 Apr 19, 2024.