base¶

Functions

`merge_dicts`	Merge a sequence with dictionaries into one dictionary by aggregating the same keys with some given function.
`rank_zero_experiment`	Returns the real experiment on rank 0 and otherwise the DummyExperiment.

Classes

`DummyExperiment`	Dummy experiment.
`DummyLogger`	Dummy logger for internal use.
`LightningLoggerBase`	Base class for experiment loggers.
`LoggerCollection`	The `LoggerCollection` class is used to iterate all logging actions over the given logger_iterable.

Abstract base class used to build new loggers.

class pytorch_lightning.loggers.base.DummyExperiment[source]¶

Bases: object

Dummy experiment.

class pytorch_lightning.loggers.base.DummyLogger[source]¶

Bases: pytorch_lightning.loggers.base.LightningLoggerBase

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 containing the hyperparameters
args¶ – Optional positional arguments, depends on the specific logger being used
kwargs¶ – 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 as soon as it received them. If you want to aggregate metrics for one specific step, use the agg_and_log_metrics() method.

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: pytorch_lightning.loggers.base.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 pytorch_lightning.loggers.base.LightningLoggerBase(agg_key_funcs=None, agg_default_func=None)[source]¶

Bases: abc.ABC

Base class for experiment loggers.

Parameters

agg_key_funcs¶ (Optional[Mapping[str, Callable[[Sequence[float]], float]]]) – Dictionary which maps a metric name to a function, which will aggregate the metric values for the same steps.
agg_default_func¶ (Optional[Callable[[Sequence[float]], float]]) – Default function to aggregate metric values. If some metric name is not presented in the agg_key_funcs dictionary, then the agg_default_func will be used for aggregation.
deprecated:¶ (..) – v1.6: The parameters agg_key_funcs and agg_default_func are deprecated in v1.6 and will be removed in v1.8.

Note

The agg_key_funcs and agg_default_func arguments are used only when one logs metrics with the agg_and_log_metrics() method.

after_save_checkpoint(checkpoint_callback)[source]¶

Called after model checkpoint callback saves a new checkpoint.

Parameters: checkpoint_callback¶ – the model checkpoint callback instance

agg_and_log_metrics(metrics, step=None)[source]¶

Aggregates and records metrics. This method doesn’t log the passed metrics instantaneously, but instead it aggregates them and logs only if metrics are ready to be logged.

Deprecated since version v1.6: This method is deprecated in v1.6 and will be removed in v1.8. Please use LightningLoggerBase.log_metrics instead.

Parameters

metrics¶ (Dict[str, float]) – Dictionary with metric names as keys and measured quantities as values
step¶ (Optional[int]) – Step number at which the metrics should be recorded

close()[source]¶

Do any cleanup that is necessary to close an experiment.

See deprecation warning below.

Deprecated since version v1.5: This method is deprecated in v1.5 and will be removed in v1.7. Please use LightningLoggerBase.finalize instead.

Return type: None

finalize(status)[source]¶

Do any processing that is necessary to finalize an experiment.

Parameters: status¶ (str) – Status that the experiment finished with (e.g. success, failed, aborted)
Return type: None

log_graph(model, input_array=None)[source]¶

Record model graph.

Parameters

model¶ (LightningModule) – lightning model
input_array¶ – input passes to model.forward

Return type

None

abstract log_hyperparams(params, *args, **kwargs)[source]¶

Record hyperparameters.

Parameters

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

abstract log_metrics(metrics, step=None)[source]¶

Records metrics. This method logs metrics as as soon as it received them. If you want to aggregate metrics for one specific step, use the agg_and_log_metrics() method.

Parameters

metrics¶ (Dict[str, float]) – Dictionary with metric names as keys and measured quantities as values
step¶ (Optional[int]) – Step number at which the metrics should be recorded

save()[source]¶

Save log data.

Return type: None

update_agg_funcs(agg_key_funcs=None, agg_default_func=<function mean>)[source]¶

Update aggregation methods.

Deprecated since version v1.6: update_agg_funcs is deprecated in v1.6 and will be removed in v1.8.

Parameters

agg_key_funcs¶ (Optional[Mapping[str, Callable[[Sequence[float]], float]]]) – Dictionary which maps a metric name to a function, which will aggregate the metric values for the same steps.
agg_default_func¶ (Callable[[Sequence[float]], float]) – Default function to aggregate metric values. If some metric name is not presented in the agg_key_funcs dictionary, then the agg_default_func will be used for aggregation.

property group_separator¶: Return the default separator used by the logger to group the data into subfolders.

abstract property name: str¶

Return the experiment name.

Return type: str

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]

abstract property version: Union[int, str]¶

Return the experiment version.

Return type: Union[int, str]

class pytorch_lightning.loggers.base.LoggerCollection(logger_iterable)[source]¶

Bases: pytorch_lightning.loggers.base.LightningLoggerBase

The LoggerCollection class is used to iterate all logging actions over the given logger_iterable.

Deprecated since version v1.6: LoggerCollection is deprecated in v1.6 and will be removed in v1.8. Directly pass a list of loggers to the Trainer and access the list via the trainer.loggers attribute.

Parameters: logger_iterable¶ (Iterable[LightningLoggerBase]) – An iterable collection of loggers

after_save_checkpoint(checkpoint_callback)[source]¶

Called after model checkpoint callback saves a new checkpoint.

Parameters: checkpoint_callback¶ – the model checkpoint callback instance

agg_and_log_metrics(metrics, step=None)[source]¶

Aggregates and records metrics. This method doesn’t log the passed metrics instantaneously, but instead it aggregates them and logs only if metrics are ready to be logged.

Deprecated since version v1.6: This method is deprecated in v1.6 and will be removed in v1.8. Please use LightningLoggerBase.log_metrics instead.

Parameters

metrics¶ (Dict[str, float]) – Dictionary with metric names as keys and measured quantities as values
step¶ (Optional[int]) – Step number at which the metrics should be recorded

close()[source]¶

Deprecated since version v1.5: This method is deprecated in v1.5 and will be removed in v1.7. Please use LoggerCollection.finalize instead.

Return type: None

finalize(status)[source]¶

Do any processing that is necessary to finalize an experiment.

Parameters: status¶ (str) – Status that the experiment finished with (e.g. success, failed, aborted)
Return type: None

log_graph(model, input_array=None)[source]¶

Record model graph.

Parameters

model¶ (LightningModule) – lightning model
input_array¶ – input passes to model.forward

Return type

None

log_hyperparams(params)[source]¶

Record hyperparameters.

Parameters

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

Return type

None

log_metrics(metrics, step=None)[source]¶

Records metrics. This method logs metrics as as soon as it received them. If you want to aggregate metrics for one specific step, use the agg_and_log_metrics() method.

Parameters

metrics¶ (Dict[str, float]) – Dictionary with metric names as keys and measured quantities as values
step¶ (Optional[int]) – Step number at which the metrics should be recorded

Return type

None

save()[source]¶

Save log data.

Return type: None

update_agg_funcs(agg_key_funcs=None, agg_default_func=<function mean>)[source]¶

Update aggregation methods.

Deprecated since version v1.6: update_agg_funcs is deprecated in v1.6 and will be removed in v1.8.

Parameters

agg_key_funcs¶ (Optional[Mapping[str, Callable[[Sequence[float]], float]]]) – Dictionary which maps a metric name to a function, which will aggregate the metric values for the same steps.
agg_default_func¶ (Callable[[Sequence[float]], float]) – Default function to aggregate metric values. If some metric name is not presented in the agg_key_funcs dictionary, then the agg_default_func will be used for aggregation.

property experiment: List[Any]¶

Returns a list of experiment objects for all the loggers in the logger collection.

Return type: List[Any]

property name: str¶

Returns the unique experiment names for all the loggers in the logger collection joined by an underscore.

Return type: str

property save_dir: Optional[str]¶

Returns None as checkpoints should be saved to default / chosen location when using multiple loggers.

Return type: Optional[str]

property version: str¶

Returns the unique experiment versions for all the loggers in the logger collection joined by an underscore.

Return type: str

pytorch_lightning.loggers.base.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[str, Callable[[Sequence[float]], float]]]) – 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}

pytorch_lightning.loggers.base.rank_zero_experiment(fn)[source]¶

Returns the real experiment on rank 0 and otherwise the DummyExperiment.

Return type: Callable