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- .. _ray-joblib:
- Distributed Scikit-learn / Joblib
- =================================
- .. _`issue on GitHub`: https://github.com/ray-project/ray/issues
- Ray supports running distributed `scikit-learn`_ programs by
- implementing a Ray backend for `joblib`_ using `Ray Actors <actors.html>`__
- instead of local processes. This makes it easy to scale existing applications
- that use scikit-learn from a single node to a cluster.
- .. note::
- This API is new and may be revised in future Ray releases. If you encounter
- any bugs, please file an `issue on GitHub`_.
- .. _`joblib`: https://joblib.readthedocs.io
- .. _`scikit-learn`: https://scikit-learn.org
- Quickstart
- ----------
- To get started, first `install Ray <installation.html>`__, then use
- ``from ray.util.joblib import register_ray`` and run ``register_ray()``.
- This will register Ray as a joblib backend for scikit-learn to use.
- Then run your original scikit-learn code inside
- ``with joblib.parallel_backend('ray')``. This will start a local Ray cluster.
- See the `Run on a Cluster`_ section below for instructions to run on
- a multi-node Ray cluster instead.
- .. code-block:: python
- import numpy as np
- from sklearn.datasets import load_digits
- from sklearn.model_selection import RandomizedSearchCV
- from sklearn.svm import SVC
- digits = load_digits()
- param_space = {
- 'C': np.logspace(-6, 6, 30),
- 'gamma': np.logspace(-8, 8, 30),
- 'tol': np.logspace(-4, -1, 30),
- 'class_weight': [None, 'balanced'],
- }
- model = SVC(kernel='rbf')
- search = RandomizedSearchCV(model, param_space, cv=5, n_iter=300, verbose=10)
- import joblib
- from ray.util.joblib import register_ray
- register_ray()
- with joblib.parallel_backend('ray'):
- search.fit(digits.data, digits.target)
- You can also set the ``ray_remote_args`` argument in ``parallel_backend`` to :func:`configure
- the Ray Actors <ray.remote>` making up the Pool. This can be used to eg. :ref:`assign resources
- to Actors, such as GPUs <actor-resource-guide>`.
- .. code-block:: python
- # Allows to use GPU-enabled estimators, such as cuML
- with joblib.parallel_backend('ray', ray_remote_args=dict(num_gpus=1)):
- search.fit(digits.data, digits.target)
- Run on a Cluster
- ----------------
- This section assumes that you have a running Ray cluster. To start a Ray cluster,
- please refer to the `cluster setup <cluster/index.html>`__ instructions.
- To connect a scikit-learn to a running Ray cluster, you have to specify the address of the
- head node by setting the ``RAY_ADDRESS`` environment variable.
- You can also start Ray manually by calling ``ray.init()`` (with any of its supported
- configuration options) before calling ``with joblib.parallel_backend('ray')``.
- .. warning::
- If you do not set the ``RAY_ADDRESS`` environment variable and do not provide
- ``address`` in ``ray.init(address=<address>)`` then scikit-learn will run on a SINGLE node!
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