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.

property name: str: Return the experiment name.

property version: str: Return the experiment version.

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.

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

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.

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.

abstract property name: str: Return the experiment name.

property save_dir: Optional[str]: Return the root directory where experiment logs get saved, or None if the logger does not save data locally.

abstract property version: Union[int, str]: Return the experiment version.

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]

Do any cleanup that is necessary to close an experiment.

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_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.

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]: Return the experiment object associated with this logger.

property name: str: Return the experiment name.

property save_dir: Optional[str]: Return the root directory where experiment logs get saved, or None if the logger does not save data locally.

property version: str: Return the experiment version.

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