logger¶

Functions

merge_dicts

Merge a sequence with dictionaries into one dictionary by aggregating the same keys with some given function.

Classes

`DummyLogger`	Dummy logger for internal use.
`Logger`	Base class for experiment loggers.

Abstract base class used to build new loggers.

class lightning.pytorch.loggers.logger.DummyLogger[source]¶

Bases: 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)[source]¶

Record hyperparameters.

Parameters

params¶ – Namespace or Dict containing the hyperparameters
args¶ (Any) – Optional positional arguments, depends on the specific logger being used
kwargs¶ (Any) – Optional keyword arguments, depends on the specific logger being used

Return type

None

log_metrics(*args, **kwargs)[source]¶

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

property experiment: lightning.fabric.loggers.logger._DummyExperiment¶

Return the experiment object associated with this logger.

Return type: _DummyExperiment

property name: str¶

Return the experiment name.

Return type: str

property version: str¶

Return the experiment version.

Return type: str

class lightning.pytorch.loggers.logger.Logger[source]¶

Bases: lightning.fabric.loggers.logger.Logger, abc.ABC

Base class for experiment loggers.

after_save_checkpoint(checkpoint_callback)[source]¶

Called after model checkpoint callback saves a new checkpoint.

Parameters: checkpoint_callback¶ (ModelCheckpoint) – the model checkpoint callback instance
Return type: None

property save_dir: Optional[str]¶

Return the root directory where experiment logs get saved, or None if the logger does not save data locally.

Return type: Optional[str]

lightning.pytorch.loggers.logger.merge_dicts(dicts, agg_key_funcs=None, default_func=<function mean>)[source]¶

Merge a sequence with dictionaries into one dictionary by aggregating the same keys with some given function.

Parameters

dicts¶ (Sequence[Mapping]) – Sequence of dictionaries to be merged.
agg_key_funcs¶ (Optional[Mapping]) – 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[[Sequence[float]], float]) – Default function to aggregate keys, which are not presented in the agg_key_funcs map.

Return type

Dict

Returns

Dictionary with merged values.

Examples

>>> 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': np.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}