初始化项目，由ModelHub XC社区提供模型

Model: ayh015/myLightningOPD
Source: Original Platform

slime/utils/tensorboard_utils.py

@@ -0,0 +1,62 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import datetime
+import logging
+import os
+from slime.utils.misc import SingletonMeta
+
+try:
+    from torch.utils.tensorboard import SummaryWriter
+except ImportError:
+    SummaryWriter = None
+
+logger = logging.getLogger(__name__)
+
+
+class _TensorboardAdapter(metaclass=SingletonMeta):
+    _writer = None
+
+    """
+    # Usage example: This will return the same instance every rank
+    # tb = _TensorboardAdapter(args)  # Initialize on first call
+    # tb.log({"Loss": 0.1}, step=1)
+
+    # In other files:
+    # from tensorboard_utils import _TensorboardAdapter
+    # tb = _TensorboardAdapter(args)  # No parameters needed to get existing instance
+    # tb.log({"Accuracy": 0.9}, step=1)
+    """
+
+    def __init__(self, args):
+        assert args.use_tensorboard, f"{args.use_tensorboard=}"
+        tb_project_name = args.tb_project_name
+        tb_experiment_name = args.tb_experiment_name
+        if tb_project_name is not None or os.environ.get("TENSORBOARD_DIR", None):
+            if tb_project_name is not None and tb_experiment_name is None:
+                tb_experiment_name = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+            self._initialize(tb_project_name, tb_experiment_name)
+        else:
+            raise ValueError("tb_project_name and tb_experiment_name, or TENSORBOARD_DIR are required")
+
+    def _initialize(self, tb_project_name, tb_experiment_name):
+        """Actual initialization logic"""
+        # Get tensorboard directory from environment variable or use default path
+        tensorboard_dir = os.environ.get("TENSORBOARD_DIR", f"tensorboard_log/{tb_project_name}/{tb_experiment_name}")
+        os.makedirs(tensorboard_dir, exist_ok=True)
+        logger.info(f"Saving tensorboard log to {tensorboard_dir}.")
+        self._writer = SummaryWriter(tensorboard_dir)
+
+    def log(self, data, step):
+        """Log data to tensorboard
+
+        Args:
+            data (dict): Dictionary containing metric names and values
+            step (int): Current step/epoch number
+        """
+        for key in data:
+            self._writer.add_scalar(key, data[key], step)
+
+    def finish(self):
+        """Close the tensorboard writer"""
+        self._writer.close()