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8fe439998e
Signed-off-by: Kai Fricke coding@kaifricke.com Why are these changes needed? Splitting up #26884: This PR includes changes to use Tuner() instead of tune.run() for most docs files (rst and py), and a change to move reuse_actors to the TuneConfig
46 lines
1.4 KiB
ReStructuredText
46 lines
1.4 KiB
ReStructuredText
.. _tune-analysis-docs:
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ResultGrid (tune.ResultGrid)
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========================================
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You can use the ``ResultGrid`` object for interacting with tune results.
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It is returned by ``Tuner.fit()``.
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.. code-block:: python
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from ray import air, tune
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tuner = tune.Tuner(
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trainable,
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run_config=air.RunConfig(name="example-experiment"),
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tune_config=tune.TuneConfig(num_samples=10),
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)
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results = tuner.fit()
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Here are some example operations for obtaining a summary of your experiment:
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.. code-block:: python
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# Get a dataframe for the last reported results of all of the trials
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df = results.get_dataframe()
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# Get a dataframe for the max accuracy seen for each trial
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df = results.get_dataframe()(metric="mean_accuracy", mode="max")
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One may wonder how is ResultGrid different than ExperimentAnalysis. ResultGrid is supposed
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to succeed ExperimentAnalysis. However, it has not reached the same feature parity yet.
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For interacting with an existing experiment, located at ``local_dir``, one may do the following:
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.. code-block:: python
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from ray.tune import ExperimentAnalysis
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analysis = ExperimentAnalysis("~/ray_results/example-experiment")
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.. _exp-analysis-docstring:
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ExperimentAnalysis (tune.ExperimentAnalysis)
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--------------------------------------------
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.. autoclass:: ray.tune.ExperimentAnalysis
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:members:
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