mirror of
https://github.com/wassname/ray.git
synced 2026-06-29 01:59:23 +08:00
Richard Liaw
121 lines
3.5 KiB
ReStructuredText
121 lines
3.5 KiB
ReStructuredText
.. _loggers-docstring:
|
|
|
|
Loggers (tune.logger)
|
|
=====================
|
|
|
|
Tune has default loggers for Tensorboard, CSV, and JSON formats.
|
|
|
|
Logging Path
|
|
------------
|
|
|
|
Tune will log the results of each trial to a subfolder under a specified local dir, which defaults to ``~/ray_results``.
|
|
|
|
.. code-block:: python
|
|
|
|
# This logs to 2 different trial folders:
|
|
# ~/ray_results/trainable_name/trial_name_1 and ~/ray_results/trainable_name/trial_name_2
|
|
# trainable_name and trial_name are autogenerated.
|
|
tune.run(trainable, num_samples=2)
|
|
|
|
You can specify the ``local_dir`` and ``trainable_name``:
|
|
|
|
.. code-block:: python
|
|
|
|
# This logs to 2 different trial folders:
|
|
# ./results/test_experiment/trial_name_1 and ./results/test_experiment/trial_name_2
|
|
# Only trial_name is autogenerated.
|
|
tune.run(trainable, num_samples=2, local_dir="./results", name="test_experiment")
|
|
|
|
To specify custom trial folder names, you can pass use the ``trial_name_creator`` argument
|
|
to `tune.run`. This takes a function with the following signature:
|
|
|
|
.. code-block:: python
|
|
|
|
def trial_name_string(trial):
|
|
"""
|
|
Args:
|
|
trial (Trial): A generated trial object.
|
|
|
|
Returns:
|
|
trial_name (str): String representation of Trial.
|
|
"""
|
|
return str(trial)
|
|
|
|
tune.run(
|
|
MyTrainableClass,
|
|
name="example-experiment",
|
|
num_samples=1,
|
|
trial_name_creator=trial_name_string
|
|
)
|
|
|
|
See the documentation on Trials: :ref:`trial-docstring`.
|
|
|
|
|
|
Custom Loggers
|
|
--------------
|
|
|
|
You can pass in your own logging mechanisms to output logs in custom formats as follows:
|
|
|
|
.. code-block:: python
|
|
|
|
from ray.tune.logger import DEFAULT_LOGGERS
|
|
|
|
tune.run(
|
|
MyTrainableClass,
|
|
name="experiment_name",
|
|
loggers=DEFAULT_LOGGERS + (CustomLogger1, CustomLogger2)
|
|
)
|
|
|
|
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.
|
|
|
|
An example can be found in `logging_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/logging_example.py>`__.
|
|
|
|
Viskit
|
|
------
|
|
|
|
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:
|
|
|
|
.. code-block:: bash
|
|
|
|
$ git clone https://github.com/rll/rllab.git
|
|
$ python rllab/rllab/viskit/frontend.py ~/ray_results/my_experiment
|
|
|
|
The nonrelevant metrics (like timing stats) can be disabled on the left to show only the relevant ones (like accuracy, loss, etc.).
|
|
|
|
.. image:: /ray-tune-viskit.png
|
|
|
|
|
|
.. _logger-interface:
|
|
|
|
Logger
|
|
------
|
|
|
|
.. autoclass:: ray.tune.logger.Logger
|
|
|
|
UnifiedLogger
|
|
-------------
|
|
|
|
.. autoclass:: ray.tune.logger.UnifiedLogger
|
|
|
|
TBXLogger
|
|
---------
|
|
|
|
.. autoclass:: ray.tune.logger.TBXLogger
|
|
|
|
JsonLogger
|
|
----------
|
|
|
|
.. autoclass:: ray.tune.logger.JsonLogger
|
|
|
|
CSVLogger
|
|
---------
|
|
|
|
.. autoclass:: ray.tune.logger.CSVLogger
|
|
|
|
MLFLowLogger
|
|
------------
|
|
|
|
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.
|
|
|
|
.. autoclass:: ray.tune.logger.MLFLowLogger
|