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 keywoard 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=<function mean>)[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¶ (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.

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.

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 keywoard arguments, depends on the specific logger being used

log_image(*args, **kwargs)[source]¶

Log image.

Arguments are directly passed to the logger.

Return type: None

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

log_text(*args, **kwargs)[source]¶

Log text.

Arguments are directly passed to the logger.

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.

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.

abstract property experiment: Any¶

Return the experiment object associated with this logger.

Return type: Any

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.

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.

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 keywoard arguments, depends on the specific logger being used

Return type

None

log_image(*args, **kwargs)[source]¶

Log image.

Arguments are directly passed to the logger.

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

log_text(*args, **kwargs)[source]¶

Log text.

Arguments are directly passed to the logger.

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.

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 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 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