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Neeratyoymfeurer
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PGijsbers
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Standardize use of n_jobs and reporting of computation time (openml#1038)
* Unit test to test existence of refit time * Measuring runtime always * Removing redundant check in unit test * Updating docs with runtimes * Adding more utilities to new example * Removing refit_time + fetching trace runtime in example * rename example * Reiterating with changes to example from @mfeurer suggestions * Including refit time and other minor formatting * Adding more cases + a concluding summary * Cosmetic changes * Adding 5th case with no release of GIL * Removing debug code * Runtime measurement example updates (openml#1052) * Minor reshuffling * Update examples/30_extended/fetch_runtimes_tutorial.py Co-authored-by: Neeratyoy Mallik <neeratyoy@gmail.com> Co-authored-by: Neeratyoy Mallik <neeratyoy@gmail.com> Co-authored-by: Matthias Feurer <feurerm@informatik.uni-freiburg.de>
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‎doc/usage.rst‎

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@@ -145,13 +145,19 @@ obtained on. Learn how to share your datasets in the following tutorial:
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* `Upload a dataset <examples/30_extended/create_upload_tutorial.html>`_
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***********************
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Extending OpenML-Python
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OpenML-Python provides an extension interface to connect other machine learning libraries than
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scikit-learn to OpenML. Please check the :ref:`api_extensions` and use the
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scikit-learn extension in :class:`openml.extensions.sklearn.SklearnExtension` as a starting point.
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Runtime measurement is incorporated in the OpenML sklearn-extension. Example usage and potential
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usage for Hyperparameter Optimisation can be found in the example tutorial:
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`HPO using OpenML <examples/30_extended/fetch_runtimes.html>`_
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Here is a list of currently maintained OpenML extensions:
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* `openml-keras <https://github.com/openml/openml-keras>`_

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