.. _loggers-docstring:

Tune Loggers (tune.logger)

Tune automatically uses loggers for TensorBoard, CSV, and JSON formats. By default, Tune only logs the returned result dictionaries from the training function.

If you need to log something lower level like model weights or gradients, see :ref:Trainable Logging <trainable-logging>.

.. note::

Tune's per-trial ``Logger`` classes have been deprecated. Use the ``LoggerCallback`` interface instead.

.. currentmodule:: ray

.. _logger-interface:

LoggerCallback Interface (tune.logger.LoggerCallback)

.. autosummary:: :nosignatures: :toctree: doc/

~tune.logger.LoggerCallback

.. autosummary:: :nosignatures: :toctree: doc/

~tune.logger.LoggerCallback.log_trial_start
~tune.logger.LoggerCallback.log_trial_restore
~tune.logger.LoggerCallback.log_trial_save
~tune.logger.LoggerCallback.log_trial_result
~tune.logger.LoggerCallback.log_trial_end

Tune Built-in Loggers

.. autosummary:: :nosignatures: :toctree: doc/

tune.logger.JsonLoggerCallback
tune.logger.CSVLoggerCallback
tune.logger.TBXLoggerCallback

MLFlow Integration

Tune also provides a logger for MLflow <https://mlflow.org>_. You can install MLflow via pip install mlflow. See the :doc:tutorial here </tune/examples/tune-mlflow>.

.. autosummary:: :nosignatures: :toctree: doc/

~air.integrations.mlflow.MLflowLoggerCallback
~air.integrations.mlflow.setup_mlflow

Wandb Integration

Tune also provides a logger for Weights & Biases <https://www.wandb.ai/>_. You can install Wandb via pip install wandb. See the :doc:tutorial here </tune/examples/tune-wandb>.

.. autosummary:: :nosignatures: :toctree: doc/

~air.integrations.wandb.WandbLoggerCallback
~air.integrations.wandb.setup_wandb

Comet Integration

Tune also provides a logger for Comet <https://www.comet.com/>_. You can install Comet via pip install comet-ml. See the :doc:tutorial here </tune/examples/tune-comet>.

.. autosummary:: :nosignatures: :toctree: doc/

~air.integrations.comet.CometLoggerCallback

Aim Integration

Tune also provides a logger for the Aim <https://aimstack.io/>_ experiment tracker. You can install Aim via pip install aim. See the :doc:tutorial here </tune/examples/tune-aim>.

.. autosummary:: :nosignatures: :toctree: doc/

~tune.logger.aim.AimLoggerCallback

Other Integrations

Viskit

Tune automatically integrates with `Viskit <https://github.com/vitchyr/viskit>`_ via the ``CSVLoggerCallback`` outputs.
To use VisKit (you may have to install some dependencies), run:

.. code-block:: bash

    $ git clone https://github.com/rll/rllab.git
    $ python rllab/rllab/viskit/frontend.py ~/ray_results/my_experiment

The non-relevant metrics (like timing stats) can be disabled on the left to show only the
relevant ones (like accuracy, loss, etc.).

.. image:: ../images/ray-tune-viskit.png