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f9b71a8bf6
This PR consolidates both #21667 and #21759 (look there for features), but improves on them in the following way: - [x] we reverted renaming of existing projects `tune`, `rllib`, `train`, `cluster`, `serve`, `raysgd` and `data` so that links won't break. I think my consolidation efforts with the `ray-` prefix were a little overeager in that regard. It's better like this. Only the creation of `ray-core` was a necessity, and some files moved into the `rllib` folder, so that should be relatively benign. - [x] Additionally, we added Algolia `docsearch`, screenshot below. This is _much_ better than our current search. Caveat: there's a sphinx dependency that needs to be replaced (`sphinx-tabs`) by another, newer one (`sphinx-panels`), as the former prevents loading of the `algolia.js` library. Will follow-up in the next PR (hoping this one doesn't get re-re-re-re-reverted).
69 lines
2.4 KiB
ReStructuredText
69 lines
2.4 KiB
ReStructuredText
.. _ray-joblib:
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Distributed Scikit-learn / Joblib
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=================================
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.. _`issue on GitHub`: https://github.com/ray-project/ray/issues
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Ray supports running distributed `scikit-learn`_ programs by
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implementing a Ray backend for `joblib`_ using `Ray Actors <actors.html>`__
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instead of local processes. This makes it easy to scale existing applications
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that use scikit-learn from a single node to a cluster.
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.. note::
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This API is new and may be revised in future Ray releases. If you encounter
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any bugs, please file an `issue on GitHub`_.
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.. _`joblib`: https://joblib.readthedocs.io
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.. _`scikit-learn`: https://scikit-learn.org
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Quickstart
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----------
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To get started, first `install Ray <installation.html>`__, then use
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``from ray.util.joblib import register_ray`` and run ``register_ray()``.
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This will register Ray as a joblib backend for scikit-learn to use.
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Then run your original scikit-learn code inside
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``with joblib.parallel_backend('ray')``. This will start a local Ray cluster.
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See the `Run on a Cluster`_ section below for instructions to run on
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a multi-node Ray cluster instead.
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.. code-block:: python
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import numpy as np
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from sklearn.datasets import load_digits
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from sklearn.model_selection import RandomizedSearchCV
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from sklearn.svm import SVC
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digits = load_digits()
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param_space = {
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'C': np.logspace(-6, 6, 30),
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'gamma': np.logspace(-8, 8, 30),
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'tol': np.logspace(-4, -1, 30),
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'class_weight': [None, 'balanced'],
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}
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model = SVC(kernel='rbf')
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search = RandomizedSearchCV(model, param_space, cv=5, n_iter=300, verbose=10)
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import joblib
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from ray.util.joblib import register_ray
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register_ray()
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with joblib.parallel_backend('ray'):
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search.fit(digits.data, digits.target)
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Run on a Cluster
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----------------
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This section assumes that you have a running Ray cluster. To start a Ray cluster,
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please refer to the `cluster setup <cluster/index.html>`__ instructions.
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To connect a scikit-learn to a running Ray cluster, you have to specify the address of the
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head node by setting the ``RAY_ADDRESS`` environment variable.
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You can also start Ray manually by calling ``ray.init()`` (with any of its supported
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configuration options) before calling ``with joblib.parallel_backend('ray')``.
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.. warning::
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If you do not set the ``RAY_ADDRESS`` environment variable and do not provide
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``address`` in ``ray.init(address=<address>)`` then scikit-learn will run on a SINGLE node!
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