Switch from tensorboard to tensorboardx in logger

requirements/pytorch/base.txt

@@ -6,7 +6,7 @@ torch>=1.10.*, <=1.13.0
 tqdm>=4.57.0, <4.65.0
 PyYAML>=5.4, <=6.0
 fsspec[http]>2021.06.0, <2022.8.0
-tensorboard>=2.9.1, <2.12.0
+tensorboardX>=2.2, <=2.5.1  # min version is set by torch.onnx missing attribute
 torchmetrics>=0.7.0, <0.10.1  # needed for using fixed compare_version
 packaging>=17.0, <=21.3
 typing-extensions>=4.0.0, <=4.4.0

requirements/pytorch/extra.txt

@@ -7,4 +7,3 @@ omegaconf>=2.0.5, <2.3.0
 hydra-core>=1.0.5, <1.3.0
 jsonargparse[signatures]>=4.15.2, <4.16.0
 rich>=10.14.0, !=10.15.0.a, <13.0.0
-protobuf<=3.20.1 # strict  # an extra is updating protobuf, this pin prevents TensorBoard failure

requirements/pytorch/test.txt

@@ -14,3 +14,6 @@ psutil<5.9.4 # for `DeviceStatsMonitor`
 pandas>1.0, <1.5.2  # needed in benchmarks
 fastapi<0.87.0
 uvicorn<0.19.1
+
+tensorboard>=2.9.1, <2.12.0
+protobuf<=3.20.1 # strict  # an extra is updating protobuf, this pin prevents TensorBoard failure

src/pytorch_lightning/CHANGELOG.md

@@ -32,6 +32,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Drop PyTorch 1.9 support ([#15347](https://github.com/Lightning-AI/lightning/pull/15347))
 
 
+- Switch from `tensorboard` to `tensorboardx` in `TensorBoardLogger` ([#15728](https://github.com/Lightning-AI/lightning/pull/15728))
+
+
 - From now on, Lightning Trainer and `LightningModule.load_from_checkpoint` automatically upgrade the loaded checkpoint if it was produced in an old version of Lightning ([#15237](https://github.com/Lightning-AI/lightning/pull/15237))
 
 

src/pytorch_lightning/loggers/tensorboard.py

@@ -22,9 +22,10 @@
 from typing import Any, Dict, Mapping, Optional, Union
 
 import numpy as np
+from lightning_utilities.core.imports import RequirementCache
+from tensorboardX import SummaryWriter
+from tensorboardX.summary import hparams
 from torch import Tensor
-from torch.utils.tensorboard import SummaryWriter
-from torch.utils.tensorboard.summary import hparams
 
 import pytorch_lightning as pl
 from lightning_lite.utilities.cloud_io import get_filesystem
@@ -38,6 +39,8 @@
 
 log = logging.getLogger(__name__)
 
+_TENSORBOARD_AVAILABLE = RequirementCache("tensorboard")
+
 if _OMEGACONF_AVAILABLE:
     from omegaconf import Container, OmegaConf
 
@@ -46,7 +49,7 @@ class TensorBoardLogger(Logger):
     r"""
     Log to local file system in `TensorBoard <https://www.tensorflow.org/tensorboard>`_ format.
 
-    Implemented using :class:`~torch.utils.tensorboard.SummaryWriter`. Logs are saved to
+    Implemented using :class:`~tensorboardX.SummaryWriter`. Logs are saved to
     ``os.path.join(save_dir, name, version)``. This is the default logger in Lightning, it comes
     preinstalled.
 
@@ -77,11 +80,20 @@ class TensorBoardLogger(Logger):
         sub_dir: Sub-directory to group TensorBoard logs. If a sub_dir argument is passed
             then logs are saved in ``/save_dir/name/version/sub_dir/``. Defaults to ``None`` in which
             logs are saved in ``/save_dir/name/version/``.
-        \**kwargs: Additional arguments used by :class:`SummaryWriter` can be passed as keyword
+        \**kwargs: Additional arguments used by :class:`tensorboardX.SummaryWriter` can be passed as keyword
             arguments in this logger. To automatically flush to disk, `max_queue` sets the size
             of the queue for pending logs before flushing. `flush_secs` determines how many seconds
             elapses before flushing.
 
+    Example:
+        >>> import shutil, tempfile
+        >>> tmp = tempfile.mkdtemp()
+        >>> tbl = TensorBoardLogger(tmp)
+        >>> tbl.log_hyperparams({"epochs": 5, "optimizer": "Adam"})
+        >>> tbl.log_metrics({"acc": 0.75})
+        >>> tbl.log_metrics({"acc": 0.9})
+        >>> tbl.finalize("success")
+        >>> shutil.rmtree(tmp)
     """
     NAME_HPARAMS_FILE = "hparams.yaml"
     LOGGER_JOIN_CHAR = "-"
@@ -103,7 +115,10 @@ def __init__(
         self._name = name or ""
         self._version = version
         self._sub_dir = None if sub_dir is None else os.fspath(sub_dir)
-        self._log_graph = log_graph
+        if log_graph and not _TENSORBOARD_AVAILABLE:
+            rank_zero_warn("You set `TensorBoardLogger(log_graph=True)` but `tensorboard` is not available.")
+        self._log_graph = log_graph and _TENSORBOARD_AVAILABLE
+
         self._default_hp_metric = default_hp_metric
         self._prefix = prefix
         self._fs = get_filesystem(save_dir)

tests/tests_pytorch/conftest.py

@@ -75,6 +75,7 @@ def restore_env_variables():
         "CUDA_MODULE_LOADING",  # leaked since PyTorch 1.13
         "KMP_INIT_AT_FORK",  # leaked since PyTorch 1.13
         "KMP_DUPLICATE_LIB_OK",  # leaked since PyTorch 1.13
+        "CRC32C_SW_MODE",  # leaked by tensorboardX
     }
     leaked_vars.difference_update(allowlist)
     assert not leaked_vars, f"test is leaking environment variable(s): {set(leaked_vars)}"

tests/tests_pytorch/loggers/test_tensorboard.py

@@ -24,6 +24,7 @@
 from pytorch_lightning import Trainer
 from pytorch_lightning.demos.boring_classes import BoringModel
 from pytorch_lightning.loggers import TensorBoardLogger
+from pytorch_lightning.loggers.tensorboard import _TENSORBOARD_AVAILABLE
 from pytorch_lightning.utilities.imports import _OMEGACONF_AVAILABLE
 from tests_pytorch.helpers.runif import RunIf
 
@@ -220,6 +221,7 @@ def test_tensorboard_log_graph(tmpdir, example_input_array):
     logger.log_graph(model, example_input_array)
 
 
+@pytest.mark.skipif(not _TENSORBOARD_AVAILABLE, reason=str(_TENSORBOARD_AVAILABLE))
 def test_tensorboard_log_graph_warning_no_example_input_array(tmpdir):
     """test that log graph throws warning if model.example_input_array is None."""
     model = BoringModel()