.. _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 e.g., :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, see the :ref:cluster setup <cluster-index> instructions.

To connect 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!