--- title: logger description: Dummy logger for internal use. canonical: >- https://lightning.ai/docs/pytorch/stable/optional-api/api/lightning.pytorch.loggers.logger --- # logger {#logger} Functions - [`merge_dicts`](#lightning.pytorch.loggers.logger.merge_dicts) — Merge a sequence with dictionaries into one dictionary by aggregating the same keys with some given function. Classes - [`DummyLogger`](#lightning.pytorch.loggers.logger.DummyLogger) — Dummy logger for internal use. - [`Logger`](#lightning.pytorch.loggers.logger.Logger) — Base class for experiment loggers. Abstract base class used to build new loggers. ### `class lightning.pytorch.loggers.logger.DummyLogger` {#lightning.pytorch.loggers.logger.DummyLogger} Bases: [`Logger`](#lightning.pytorch.loggers.logger.Logger "lightning.pytorch.loggers.logger.Logger") Dummy logger for internal use. It is useful if we want to disable user’s logger for a feature, but still ensure that user code can run #### `log_hyperparams(*args, **kwargs)` {#lightning.pytorch.loggers.logger.DummyLogger.log_hyperparams} Record hyperparameters. Parameters: * **params** – [`Namespace`](https://docs.python.org/3/library/argparse.html#argparse.Namespace "(in Python v3.15)") or Dict containing the hyperparameters * **args** ([`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")) – Optional positional arguments, depends on the specific logger being used * **kwargs** ([`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")) – Optional keyword arguments, depends on the specific logger being used Return type: [`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)") #### `log_metrics(*args, **kwargs)` {#lightning.pytorch.loggers.logger.DummyLogger.log_metrics} Records metrics. This method logs metrics as soon as it received them. Parameters: * **metrics** – Dictionary with metric names as keys and measured quantities as values * **step** – Step number at which the metrics should be recorded Return type: [`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)") #### `property experiment: _DummyExperiment` {#lightning.pytorch.loggers.logger.DummyLogger.experiment} Return the experiment object associated with this logger. #### `property name: str` {#lightning.pytorch.loggers.logger.DummyLogger.name} Return the experiment name. #### `property version: str` {#lightning.pytorch.loggers.logger.DummyLogger.version} Return the experiment version. ### `class lightning.pytorch.loggers.logger.Logger` {#lightning.pytorch.loggers.logger.Logger} Bases: [`Logger`](/docs/fabric/stable/api/generated/lightning.fabric.loggers.Logger#lightning.fabric.loggers.Logger "(in lightning v2.7.0.dev)"), [`ABC`](https://docs.python.org/3/library/abc.html#abc.ABC "(in Python v3.15)") Base class for experiment loggers. #### `after_save_checkpoint(checkpoint_callback)` {#lightning.pytorch.loggers.logger.Logger.after_save_checkpoint} Called after model checkpoint callback saves a new checkpoint. Parameters: **checkpoint\_callback** ([`ModelCheckpoint`](/docs/pytorch/2.5.5/optional-api/api/lightning.pytorch.callbacks.ModelCheckpoint.md#lightning.pytorch.callbacks.ModelCheckpoint "lightning.pytorch.callbacks.model_checkpoint.ModelCheckpoint")) – the model checkpoint callback instance Return type: [`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)") #### `property save_dir: Optional[str]` {#lightning.pytorch.loggers.logger.Logger.save_dir} Return the root directory where experiment logs get saved, or None if the logger does not save data locally. ### `lightning.pytorch.loggers.logger.merge_dicts(dicts, agg_key_funcs=None, default_func=)` {#lightning.pytorch.loggers.logger.merge_dicts} Merge a sequence with dictionaries into one dictionary by aggregating the same keys with some given function. Parameters: * **dicts** ([`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence "(in Python v3.15)")\[[`Mapping`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping "(in Python v3.15)")\]) – Sequence of dictionaries to be merged. * **agg\_key\_funcs** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`Mapping`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Mapping "(in Python v3.15)")\]) – Mapping from key name to function. This function will aggregate a list of values, obtained from the same key of all dictionaries. If some key has no specified aggregation function, the default one will be used. Default is: `None` (all keys will be aggregated by the default function). * **default\_func** ([`Callable`](https://docs.python.org/3/library/typing.html#typing.Callable "(in Python v3.15)")\[\[[`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence "(in Python v3.15)")\[[`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")\]\], [`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")\]) – Default function to aggregate keys, which are not presented in the agg\_key\_funcs map. Return type: [`dict`](https://docs.python.org/3/builtins/stdtypes.html#dict "(in Python v3.15)") Returns: Dictionary with merged values. Examples ```python >>> import pprint >>> d1 = {'a': 1.7, 'b': 2.0, 'c': 1, 'd': {'d1': 1, 'd3': 3}} >>> d2 = {'a': 1.1, 'b': 2.2, 'v': 1, 'd': {'d1': 2, 'd2': 3}} >>> d3 = {'a': 1.1, 'v': 2.3, 'd': {'d3': 3, 'd4': {'d5': 1}}} >>> dflt_func = min >>> agg_funcs = {'a': statistics.mean, 'v': max, 'd': {'d1': sum}} >>> pprint.pprint(merge_dicts([d1, d2, d3], agg_funcs, dflt_func)) {'a': 1.3, 'b': 2.0, 'c': 1, 'd': {'d1': 3, 'd2': 3, 'd3': 3, 'd4': {'d5': 1}}, 'v': 2.3} ``` --- **Navigation** - Parent: [Api](/docs/pytorch/2.5.5/optional-api/api.md)