pytorch_lightning.loggers.wandb module

Weights and Biases

class pytorch_lightning.loggers.wandb.WandbLogger(name=None, save_dir=None, offline=False, id=None, anonymous=False, version=None, project=None, tags=None, log_model=False, experiment=None, entity=None, group=None)

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.

• tags (Optional[List[str]]) – Tags associated with this run.

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

• experiment – WandB experiment object

• entity – The team posting this run (default: your username or your default team)

• group (Optional[str]) – A unique string shared by all runs in a given group

Example

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

See also

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

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

watch(model, log='gradients', log_freq=100)

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]