LightningIRWandbLogger

Bases: WandbLogger

__init__(name: str | None = None, save_dir: str | Path = '.', version: str | None = None, offline: bool = False, dir: str | Path | None = None, id: str | None = None, anonymous: bool | None = None, project: str | None = None, log_model: Literal['all'] | bool = False, experiment: Run | RunDisabled | None = None, prefix: str = '', checkpoint_name: str | None = None, **kwargs: Any) → None

Methods

`__init__`([name, save_dir, version, offline, ...])
`after_save_checkpoint`(checkpoint_callback)
`download_artifact`(artifact[, save_dir, ...])	Downloads an artifact from the wandb server.
`finalize`(status)
`log_audio`(key, audios[, step])	Log audios (numpy arrays, or file paths).
`log_graph`(model[, input_array])	Record model graph.
`log_hyperparams`(params)
`log_image`(key, images[, step])	Log images (tensors, numpy arrays, PIL Images or file paths).
`log_metrics`(metrics[, step])
`log_table`(key[, columns, data, dataframe, step])	Log a Table containing any object type (text, image, audio, video, molecule, html, etc).
`log_text`(key[, columns, data, dataframe, step])	Log text as a Table.
`log_video`(key, videos[, step])	Log videos (numpy arrays, or file paths).
`save`()	Save log data.
`use_artifact`(artifact[, artifact_type])	Logs to the wandb dashboard that the mentioned artifact is used by the run.
`watch`(model[, log, log_freq, log_graph])

Attributes

`LOGGER_JOIN_CHAR`
`experiment`	Actual wandb object.
`group_separator`	Return the default separator used by the logger to group the data into subfolders.
`log_dir`	Return directory the current version of the experiment gets saved, or None if the logger does not save data locally.
`name`	The project name of this experiment.
`root_dir`	Return the root directory where all versions of an experiment get saved, or None if the logger does not save data locally.
`save_dir`	Gets the save directory.
`version`	Gets the id of the experiment.

static download_artifact(artifact: str, save_dir: str | Path | None = None, artifact_type: str | None = None, use_artifact: bool | None = True) → str

Downloads an artifact from the wandb server.

Parameters:

artifact – The path of the artifact to download.
save_dir – The directory to save the artifact to.
artifact_type – The type of artifact to download.
use_artifact – Whether to add an edge between the artifact graph.

Returns:

The path to the downloaded artifact.

property experiment: Run | RunDisabled

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

Example:

.. code-block:: python

self.logger.experiment.some_wandb_function()

property group_separator: str: Return the default separator used by the logger to group the data into subfolders.

log_audio(key: str, audios: List[Any], step: int | None = None, **kwargs: Any) → None

Log audios (numpy arrays, or file paths).

Parameters:

key – The key to be used for logging the audio files
audios – The list of audio file paths, or numpy arrays to be logged
step – The step number to be used for logging the audio files
**kwargs – Optional kwargs are lists passed to each Wandb.Audio instance (ex: caption, sample_rate).

Optional kwargs are lists passed to each audio (ex: caption, sample_rate).

property log_dir: str | None: Return directory the current version of the experiment gets saved, or None if the logger does not save data locally.

log_graph(model: Module, input_array: Tensor | None = None) → None

Record model graph.

Parameters:

model – the model with an implementation of forward.
input_array – input passes to model.forward

log_image(key: str, images: List[Any], step: int | None = None, **kwargs: Any) → None

Log images (tensors, numpy arrays, PIL Images or file paths).

Optional kwargs are lists passed to each image (ex: caption, masks, boxes).

log_table(key: str, columns: List[str] | None = None, data: List[List[Any]] | None = None, dataframe: Any = None, step: int | None = None) → None

Log a Table containing any object type (text, image, audio, video, molecule, html, etc).

Can be defined either with columns and data or with dataframe.

log_text(key: str, columns: List[str] | None = None, data: List[List[str]] | None = None, dataframe: Any = None, step: int | None = None) → None

Log text as a Table.

Can be defined either with columns and data or with dataframe.

log_video(key: str, videos: List[Any], step: int | None = None, **kwargs: Any) → None

Log videos (numpy arrays, or file paths).

Parameters:

key – The key to be used for logging the video files
videos – The list of video file paths, or numpy arrays to be logged
step – The step number to be used for logging the video files
**kwargs – Optional kwargs are lists passed to each Wandb.Video instance (ex: caption, fps, format).

Optional kwargs are lists passed to each video (ex: caption, fps, format).

property name: str | None

The project name of this experiment.

Returns:: The name of the project the current experiment belongs to. This name is not the same as wandb.Run’s name. To access wandb’s internal experiment name, use logger.experiment.name instead.

property root_dir: str | None: Return the root directory where all versions of an experiment get saved, or None if the logger does not save data locally.

save() → None: Save log data.

property save_dir: str | None

Gets the save directory.

Returns:: The path to the save directory.

use_artifact(artifact: str, artifact_type: str | None = None) → Artifact

Logs to the wandb dashboard that the mentioned artifact is used by the run.

Parameters:

artifact – The path of the artifact.
artifact_type – The type of artifact being used.

Returns:

wandb Artifact object for the artifact.

property version: str | None

Gets the id of the experiment.

Returns:: The id of the experiment if the experiment exists else the id given to the constructor.