{"record":{"id":"0ebdfb6c781c650b","repo":"Lightning-AI/pytorch-lightning","slug":"litlogger-does-not-support-log-graph","errorCode":null,"errorMessage":"LitLogger does not support `log_graph`","messagePattern":"LitLogger does not support `log_graph`","errorType":"console","errorClass":"UserWarning","httpStatus":null,"severity":"warning","filePath":"src/lightning/pytorch/loggers/litlogger.py","lineNumber":274,"sourceCode":"\n    @override\n    @rank_zero_only\n    def log_hyperparams(\n        self,\n        params: Union[dict[str, Any], Namespace],\n        metrics: Optional[dict[str, Any]] = None,\n    ) -> None:\n        \"\"\"Log hyperparams.\"\"\"\n        if isinstance(params, Namespace):\n            params = params.__dict__\n        experiment = self._require_experiment()\n        for key, value in params.items():\n            experiment[key] = str(value)\n\n    @override\n    @rank_zero_only\n    def log_graph(self, model: Module, input_array: Optional[Tensor] = None) -> None:\n        warnings.warn(\"LitLogger does not support `log_graph`\", UserWarning, stacklevel=2)\n\n    @override\n    @rank_zero_only\n    def save(self) -> None:\n        pass\n\n    @override\n    @rank_zero_only\n    def finalize(self, status: Optional[str] = None) -> None:\n        if self._experiment is not None:\n            # log checkpoints as artifacts before finalizing\n            if self._checkpoint_callback:\n                self._scan_and_log_checkpoints(self._checkpoint_callback)\n            self._experiment.finalize(status)\n\n    # ──────────────────────────────────────────────────────────────────────────────\n    # Public methods\n    # ──────────────────────────────────────────────────────────────────────────────","sourceCodeStart":256,"sourceCodeEnd":292,"githubUrl":"https://github.com/Lightning-AI/pytorch-lightning/blob/9fed5c27d2a62ff0efd6c3573599921d6ff67c14/src/lightning/pytorch/loggers/litlogger.py#L256-L292","documentation":"LitLogger (local file logger) does not implement graph logging; calling log_graph(model) or Trainer(log_graph=True) with this logger emits a UserWarning and does nothing.","triggerScenarios":"CSV/LitLogger-based Trainer(log_graph=True) (e.g. when tensorboard is not installed, since logger=True falls back to CSV), or explicit logger.log_graph(model).","commonSituations":"Copying a TensorBoard-oriented config while tensorboard isn't installed; wanting model graph visualization with a file-based logger.","solutions":["Use TensorBoardLogger (pip install lightning[extra] or tensorboard) for log_graph support","Remove log_graph=True from the Logger init when using LitLogger/CSVLogger","Ignore the warning — everything else works"],"exampleFix":"# before\nlogger = LitLogger('logs')\n# log_graph=True somewhere -> warning\n# after\nfrom lightning.pytorch.loggers import TensorBoardLogger\nlogger = TensorBoardLogger('logs', log_graph=True)","handlingStrategy":"fallback","validationCode":"from lightning.pytorch.loggers.tensorboard import _TENSORBOARD_AVAILABLE\nif want_graph and not _TENSORBOARD_AVAILABLE:\n    raise SystemExit('install tensorboard for log_graph')","typeGuard":null,"tryCatchPattern":null,"preventionTips":["Match logger choice to features you need (log_graph -> TensorBoardLogger)","Install lightning[extra] in environments using graphs"],"tags":["logger","log-graph","litlogger","lightning"],"backgroundTag":"unsupported-logger-feature","analyzedSha":"9fed5c27d2a62ff0efd6c3573599921d6ff67c14","analyzedAt":"2026-08-28T11:52:41.083Z","schemaVersion":2},"datasetVersion":"2026-08-28T16:17:29.566Z"}