avalanche.logging.WandBLogger

class avalanche.logging.WandBLogger(project_name: str = 'Avalanche', run_name: str = 'Test', log_artifacts: bool = False, path: str | Path = 'Checkpoints', uri: str | None = None, sync_tfboard: bool = False, save_code: bool = True, config: object | None = None, dir: Path | str | None = None, params: dict | None = None)[source]

Weights and Biases logger.

The WandBLogger provides an easy integration with Weights & Biases logging. Each monitored metric is automatically logged to a dedicated Weights & Biases project dashboard.

External storage for W&B Artifacts (for instance - AWS S3 and GCS buckets) uri are supported.

The wandb log files are placed by default in “./wandb/” unless specified.

Note

TensorBoard can be synced on to the W&B dedicated dashboard.

__init__(project_name: str = 'Avalanche', run_name: str = 'Test', log_artifacts: bool = False, path: str | Path = 'Checkpoints', uri: str | None = None, sync_tfboard: bool = False, save_code: bool = True, config: object | None = None, dir: Path | str | None = None, params: dict | None = None)[source]

Creates an instance of the WandBLogger.

Parameters:

  • project_name – Name of the W&B project.

  • run_name – Name of the W&B run.

  • log_artifacts – Option to log model weights as W&B Artifacts. Note that, in order for model weights to be logged, the WeightCheckpoint metric must be added to the evaluation plugin.

  • path – Path to locally save the model checkpoints.

  • uri – URI identifier for external storage buckets (GCS, S3).

  • sync_tfboard – Syncs TensorBoard to the W&B dashboard UI.

  • save_code – Saves the main training script to W&B.

  • config – Syncs hyper-parameters and config values used to W&B.

  • dir – Path to the local log directory for W&B logs to be saved at.

  • params – All arguments for wandb.init() function call. Visit https://docs.wandb.ai/ref/python/init to learn about all wand.init() parameters.

Methods

__init__([project_name, run_name, ...])

Creates an instance of the WandBLogger.

after_backward(strategy, *args, **kwargs)

Called after criterion.backward() by the BaseTemplate.

after_eval(strategy, *args, **kwargs)

Called after eval by the BaseTemplate.

after_eval_dataset_adaptation(strategy, ...)

Called after eval_dataset_adaptation by the BaseTemplate.

after_eval_exp(strategy, *args, **kwargs)

Called after eval_exp by the BaseTemplate.

after_eval_forward(strategy, *args, **kwargs)

Called after model.forward() by the BaseTemplate.

after_eval_iteration(strategy, *args, **kwargs)

Called after the end of an iteration by the BaseTemplate.

after_forward(strategy, *args, **kwargs)

Called after model.forward() by the BaseTemplate.

after_train_dataset_adaptation(strategy, ...)

Called after train_dataset_adapatation by the BaseTemplate.

after_training(strategy, *args, **kwargs)

Called after train by the BaseTemplate.

after_training_epoch(strategy, *args, **kwargs)

Called after train_epoch by the BaseTemplate.

after_training_exp(strategy, metric_values, ...)

Called after train_exp by the BaseTemplate.

after_training_iteration(strategy, *args, ...)

Called after the end of a training iteration by the BaseTemplate.

after_update(strategy, *args, **kwargs)

Called after optimizer.update() by the BaseTemplate.

args_parse()

before_backward(strategy, *args, **kwargs)

Called before criterion.backward() by the BaseTemplate.

before_eval(strategy, *args, **kwargs)

Called before eval by the BaseTemplate.

before_eval_dataset_adaptation(strategy, ...)

Called before eval_dataset_adaptation by the BaseTemplate.

before_eval_exp(strategy, *args, **kwargs)

Called before eval_exp by the BaseTemplate.

before_eval_forward(strategy, *args, **kwargs)

Called before model.forward() by the BaseTemplate.

before_eval_iteration(strategy, *args, **kwargs)

Called before the start of a training iteration by the BaseTemplate.

before_forward(strategy, *args, **kwargs)

Called before model.forward() by the BaseTemplate.

before_run()

before_train_dataset_adaptation(strategy, ...)

Called before train_dataset_adapatation by the BaseTemplate.

before_training(strategy, *args, **kwargs)

Called before train by the BaseTemplate.

before_training_epoch(strategy, *args, **kwargs)

Called before train_epoch by the BaseTemplate.

before_training_exp(strategy, *args, **kwargs)

Called before train_exp by the BaseTemplate.

before_training_iteration(strategy, *args, ...)

Called before the start of a training iteration by the BaseTemplate.

before_update(strategy, *args, **kwargs)

Called before optimizer.update() by the BaseTemplate.

import_wandb()

log_metrics(metric_values)

Receive a list of MetricValues to log.

log_single_metric(name, value, x_plot)

Log a metric value.

Attributes

supports_distributed

A flag describing whether this plugin supports distributed training.