WandbLogger

class lightning.pytorch.loggers.WandbLogger(name=None, save_dir='.', version=None, offline=False, dir=None, id=None, anonymous=None, project=None, log_model=False, experiment=None, prefix='', checkpoint_name=None, **kwargs)

Bases: lightning.pytorch.loggers.logger.Logger

Log using Weights and Biases.

Installation and set-up

Install with pip:

pip install wandb

Create a WandbLogger instance:

from lightning.pytorch.loggers import WandbLogger

wandb_logger = WandbLogger(project="MNIST")

Pass the logger instance to the Trainer:

trainer = Trainer(logger=wandb_logger)

A new W&B run will be created when training starts if you have not created one manually before with wandb.init().

Log metrics

Log from LightningModule:

class LitModule(LightningModule):
    def training_step(self, batch, batch_idx):
        self.log("train/loss", loss)

Use directly wandb module:

wandb.log({"train/loss": loss})

Log hyper-parameters

Save LightningModule parameters:

class LitModule(LightningModule):
    def __init__(self, *args, **kwarg):
        self.save_hyperparameters()

Add other config parameters:

# add one parameter
wandb_logger.experiment.config["key"] = value

# add multiple parameters
wandb_logger.experiment.config.update({key1: val1, key2: val2})

# use directly wandb module
wandb.config["key"] = value
wandb.config.update()

Log gradients, parameters and model topology

Call the watch method for automatically tracking gradients:

# log gradients and model topology
wandb_logger.watch(model)

# log gradients, parameter histogram and model topology
wandb_logger.watch(model, log="all")

# change log frequency of gradients and parameters (100 steps by default)
wandb_logger.watch(model, log_freq=500)

# do not log graph (in case of errors)
wandb_logger.watch(model, log_graph=False)

The watch method adds hooks to the model which can be removed at the end of training:

wandb_logger.experiment.unwatch(model)

Log model checkpoints

Log model checkpoints at the end of training:

wandb_logger = WandbLogger(log_model=True)

Log model checkpoints as they get created during training:

wandb_logger = WandbLogger(log_model="all")

Custom checkpointing can be set up through ModelCheckpoint:

# log model only if `val_accuracy` increases
wandb_logger = WandbLogger(log_model="all")
checkpoint_callback = ModelCheckpoint(monitor="val_accuracy", mode="max")
trainer = Trainer(logger=wandb_logger, callbacks=[checkpoint_callback])

latest and best aliases are automatically set to easily retrieve a model checkpoint:

# reference can be retrieved in artifacts panel
# "VERSION" can be a version (ex: "v2") or an alias ("latest or "best")
checkpoint_reference = "USER/PROJECT/MODEL-RUN_ID:VERSION"

# download checkpoint locally (if not already cached)
run = wandb.init(project="MNIST")
artifact = run.use_artifact(checkpoint_reference, type="model")
artifact_dir = artifact.download()

# load checkpoint
model = LitModule.load_from_checkpoint(Path(artifact_dir) / "model.ckpt")

Log media

Log text with:

# using columns and data
columns = ["input", "label", "prediction"]
data = [["cheese", "english", "english"], ["fromage", "french", "spanish"]]
wandb_logger.log_text(key="samples", columns=columns, data=data)

# using a pandas DataFrame
wandb_logger.log_text(key="samples", dataframe=my_dataframe)

Log images with:

# using tensors, numpy arrays or PIL images
wandb_logger.log_image(key="samples", images=[img1, img2])

# adding captions
wandb_logger.log_image(key="samples", images=[img1, img2], caption=["tree", "person"])

# using file path
wandb_logger.log_image(key="samples", images=["img_1.jpg", "img_2.jpg"])

More arguments can be passed for logging segmentation masks and bounding boxes. Refer to Image Overlays documentation.

Log Tables

W&B Tables can be used to log, query and analyze tabular data.

They support any type of media (text, image, video, audio, molecule, html, etc) and are great for storing, understanding and sharing any form of data, from datasets to model predictions.

columns = ["caption", "image", "sound"]
data = [["cheese", wandb.Image(img_1), wandb.Audio(snd_1)], ["wine", wandb.Image(img_2), wandb.Audio(snd_2)]]
wandb_logger.log_table(key="samples", columns=columns, data=data)

Downloading and Using Artifacts

To download an artifact without starting a run, call the download_artifact function on the class:

from lightning.pytorch.loggers import WandbLogger

artifact_dir = WandbLogger.download_artifact(artifact="path/to/artifact")

To download an artifact and link it to an ongoing run call the download_artifact function on the logger instance:

class MyModule(LightningModule):
    def any_lightning_module_function_or_hook(self):
        self.logger.download_artifact(artifact="path/to/artifact")

To link an artifact from a previous run you can use use_artifact function:

from lightning.pytorch.loggers import WandbLogger

wandb_logger = WandbLogger(project="my_project", name="my_run")
wandb_logger.use_artifact(artifact="path/to/artifact")

See also

• Demo in Google Colab with hyperparameter search and model logging

• W&B Documentation

Parameters

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

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

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

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

• dir (Union[str, Path, None]) – Same as save_dir.

• id (Optional[str]) – Same as version.

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

• project (Optional[str]) – The name of the project to which this run will belong. If not set, the environment variable WANDB_PROJECT will be used as a fallback. If both are not set, it defaults to 'lightning_logs'.

• log_model (Union[str, bool]) –

  Log checkpoints created by ModelCheckpoint as W&B artifacts. latest and best aliases are automatically set.

  • if log_model == 'all', checkpoints are logged during training.

  • if log_model == True, checkpoints are logged at the end of training, except when save_top_k == -1 which also logs every checkpoint during training.

  • if log_model == False (default), no checkpoint is logged.

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

• experiment (None) – WandB experiment object. Automatically set when creating a run.

• checkpoint_name (Optional[str]) – Name of the model checkpoint artifact being logged.

• **kwargs – Arguments passed to wandb.init() like entity, group, tags, etc.

Raises

• ModuleNotFoundError – If required WandB package is not installed on the device.

• MisconfigurationException – If both log_model and offline is set to True.

after_save_checkpoint(checkpoint_callback)

Called after model checkpoint callback saves a new checkpoint.

Parameters

checkpoint_callback (ModelCheckpoint) – the model checkpoint callback instance

Return type

None

static download_artifact(artifact, save_dir=None, artifact_type=None, use_artifact=True)

Downloads an artifact from the wandb server.

Parameters

• artifact (str) – The path of the artifact to download.

• save_dir (Union[str, Path, None]) – The directory to save the artifact to.

• artifact_type (Optional[str]) – The type of artifact to download.

• use_artifact (Optional[bool]) – Whether to add an edge between the artifact graph.

Return type

str

Returns

The path to the downloaded artifact.

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 or Dict containing the hyperparameters

• args – Optional positional arguments, depends on the specific logger being used

• kwargs – Optional keyword arguments, depends on the specific logger being used

Return type

None

log_image(key, images, step=None, **kwargs)

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

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

Return type

None

log_metrics(metrics, step=None)

Records metrics. This method logs metrics as soon as it received them.

Parameters

• metrics (Mapping[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

log_table(key, columns=None, data=None, dataframe=None, step=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.

Return type

None

log_text(key, columns=None, data=None, dataframe=None, step=None)

Log text as a Table.

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

Return type

None

use_artifact(artifact, artifact_type=None)

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 experiment: None

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

Example:

.. code-block:: python

self.logger.experiment.some_wandb_function()

Return type

None

property name: Optional[str]

The project name of this experiment.

Return type

Optional[str]

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 save_dir: Optional[str]

Gets the save directory.

Return type

Optional[str]

Returns

The path to the save directory.

property version: Optional[str]

Gets the id of the experiment.

Return type

Optional[str]

Returns

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