wandb_logging.rst

Logging to Weights & Biases

This deep-dive will guide you through how to set up logging to Weights & Biases (W&B) in torchtune.

.. grid:: 1

    .. grid-item-card:: :octicon:`mortar-board;1em;` What this deep-dive will cover

      * How to get started with W&B
      * How to use the :class:`~torchtune.training.metric_logging.WandBLogger`
      * How to log configs, metrics, and model checkpoints to W&B

torchtune supports logging your training runs to Weights & Biases. An example W&B workspace from a torchtune fine-tuning run can be seen in the screenshot below.

Note

You will need to install the wandb package to use this feature. You can install it via pip:

pip install wandb

Then you need to login with your API key using the W&B CLI:

wandb login

Metric Logger

The only change you need to make is to add the metric logger to your config. Weights & Biases will log the metrics and model checkpoints for you.

# enable logging to the built-in WandBLogger
metric_logger:
  _component_: torchtune.training.metric_logging.WandBLogger
  # the W&B project to log to
  project: torchtune

We automatically grab the config from the recipe you are running and log it to W&B. You can find it in the W&B overview tab and the actual file in the Files tab.

As a tip, you may see straggler wandb processes running in the background if your job crashes or otherwise exits without cleaning up resources. To kill these straggler processes, a command like ps -aux | grep wandb | awk '{ print $2 }' | xargs kill can be used.

Note

Click on this sample project to see the W&B workspace. The config used to train the models can be found here.

Logging Model Checkpoints to W&B

You can also log the model checkpoints to W&B by modifying the desired script save_checkpoint method.

A suggested approach would be something like this:

def save_checkpoint(self, epoch: int) -> None:
    ...
    ## Let's save the checkpoint to W&B
    ## depending on the Checkpointer Class the file will be named differently
    ## Here is an example for the full_finetune case
    checkpoint_file = Path.joinpath(
        self._checkpointer._output_dir, f"torchtune_model_{epoch}"
    ).with_suffix(".pt")
    wandb_at = wandb.Artifact(
        name=f"torchtune_model_{epoch}",
        type="model",
        # description of the model checkpoint
        description="Model checkpoint",
        # you can add whatever metadata you want as a dict
        metadata={
            training.SEED_KEY: self.seed,
            training.EPOCHS_KEY: self.epochs_run,
            training.TOTAL_EPOCHS_KEY: self.total_epochs,
            training.MAX_STEPS_KEY: self.max_steps_per_epoch,
        }
    )
    wandb_at.add_file(checkpoint_file)
    wandb.log_artifact(wandb_at)