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Richard Liaw
121 lines
3.5 KiB
ReStructuredText
121 lines
3.5 KiB
ReStructuredText
.. _loggers-docstring:
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Loggers (tune.logger)
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=====================
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Tune has default loggers for Tensorboard, CSV, and JSON formats.
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Logging Path
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------------
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Tune will log the results of each trial to a subfolder under a specified local dir, which defaults to ``~/ray_results``.
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.. code-block:: python
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# This logs to 2 different trial folders:
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# ~/ray_results/trainable_name/trial_name_1 and ~/ray_results/trainable_name/trial_name_2
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# trainable_name and trial_name are autogenerated.
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tune.run(trainable, num_samples=2)
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You can specify the ``local_dir`` and ``trainable_name``:
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.. code-block:: python
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# This logs to 2 different trial folders:
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# ./results/test_experiment/trial_name_1 and ./results/test_experiment/trial_name_2
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# Only trial_name is autogenerated.
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tune.run(trainable, num_samples=2, local_dir="./results", name="test_experiment")
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To specify custom trial folder names, you can pass use the ``trial_name_creator`` argument
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to `tune.run`. This takes a function with the following signature:
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.. code-block:: python
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def trial_name_string(trial):
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"""
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Args:
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trial (Trial): A generated trial object.
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Returns:
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trial_name (str): String representation of Trial.
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"""
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return str(trial)
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tune.run(
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MyTrainableClass,
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name="example-experiment",
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num_samples=1,
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trial_name_creator=trial_name_string
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)
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See the documentation on Trials: :ref:`trial-docstring`.
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Custom Loggers
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--------------
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You can pass in your own logging mechanisms to output logs in custom formats as follows:
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.. code-block:: python
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from ray.tune.logger import DEFAULT_LOGGERS
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tune.run(
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MyTrainableClass,
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name="experiment_name",
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loggers=DEFAULT_LOGGERS + (CustomLogger1, CustomLogger2)
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)
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These loggers will be called along with the default Tune loggers. All loggers must inherit the Logger interface (:ref:`logger-interface`). You can also check out `logger.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/logger.py>`__ for implementation details.
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An example can be found in `logging_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/logging_example.py>`__.
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Viskit
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------
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Tune automatically integrates with `Viskit <https://github.com/vitchyr/viskit>`_ via the ``CSVLogger`` outputs. To use VisKit (you may have to install some dependencies), run:
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.. code-block:: bash
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$ git clone https://github.com/rll/rllab.git
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$ python rllab/rllab/viskit/frontend.py ~/ray_results/my_experiment
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The nonrelevant metrics (like timing stats) can be disabled on the left to show only the relevant ones (like accuracy, loss, etc.).
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.. image:: /ray-tune-viskit.png
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.. _logger-interface:
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Logger
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------
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.. autoclass:: ray.tune.logger.Logger
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UnifiedLogger
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-------------
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.. autoclass:: ray.tune.logger.UnifiedLogger
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TBXLogger
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---------
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.. autoclass:: ray.tune.logger.TBXLogger
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JsonLogger
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----------
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.. autoclass:: ray.tune.logger.JsonLogger
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CSVLogger
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---------
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.. autoclass:: ray.tune.logger.CSVLogger
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MLFLowLogger
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------------
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Tune also provides a default logger for `MLFlow <https://mlflow.org>`_. You can install MLFlow via ``pip install mlflow``. An example can be found `mlflow_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/mlflow_example.py>`__. Note that this currently does not include artifact logging support. For this, you can use the native MLFlow APIs inside your Trainable definition.
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.. autoclass:: ray.tune.logger.MLFLowLogger
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