logger — PyTorch Lightning 2.5.1.post0 documentation (original) (raw)
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: 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:
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:
property experiment_: _DummyExperiment_¶
Return the experiment object associated with this logger.
Return the experiment name.
Return the experiment version.
class lightning.pytorch.loggers.logger.Logger[source]¶
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:
property save_dir_: Optional[str]_¶
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=)[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 theagg_key_funcs map.
Return type:
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': 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}