wandb

Classes

WandbLogger

Log using Weights and Biases.

Weights and Biases Logger

class pytorch_lightning.loggers.wandb.WandbLogger(name=None, save_dir=None, offline=False, id=None, anonymous=False, version=None, project=None, log_model=False, experiment=None, prefix='', **kwargs)

Bases: pytorch_lightning.loggers.base.LightningLoggerBase

Log using Weights and Biases.

Install it with pip:

pip install wandb

Parameters

• name (Optional[str]) – Display name for the run.

• save_dir (Optional[str]) – Path where data is saved.

• offline (bool) – Run offline (data can be streamed later to wandb servers).

• id (Optional[str]) – Sets the version, mainly used to resume a previous run.

• anonymous (bool) – Enables or explicitly disables anonymous logging.

• version (Optional[str]) – Sets the version, mainly used to resume a previous run.

• project (Optional[str]) – The name of the project to which this run will belong.

• log_model (bool) – Save checkpoints in wandb dir to upload on W&B servers.

• experiment – WandB experiment object.

• prefix (str) – A string to put at the beginning of metric keys.

• **kwargs – Additional arguments like entity, group, tags, etc. used by wandb.init() can be passed as keyword arguments in this logger.

Example:

.. code-block:: python

from pytorch_lightning.loggers import WandbLogger from pytorch_lightning import Trainer wandb_logger = WandbLogger() trainer = Trainer(logger=wandb_logger)

Note: When logging manually through wandb.log or trainer.logger.experiment.log, make sure to use commit=False so the logging step does not increase.

See also

• Tutorial on how to use W&B with Pytorch Lightning.

finalize(status)

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_hyperparams(params)

Record hyperparameters.

Parameters

params (Union[Dict[str, Any], Namespace]) – Namespace containing the hyperparameters

Return type

None

log_metrics(metrics, step=None)

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

property experiment

Actual wandb object. To use wandb features in your LightningModule do the following.

Example:

self.logger.experiment.some_wandb_function()

Return type

Run

property name

Return the experiment name.

Return type

Optional[str]

property save_dir

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

Return type

Optional[str]

property version

Return the experiment version.

Return type

Optional[str]