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a70ada7341
Implements a landing page for the new KubeRay-based deployment guide. Implements a "Getting started" Jupyter notebook
107 lines
3.7 KiB
ReStructuredText
107 lines
3.7 KiB
ReStructuredText
.. include:: we_are_hiring.rst
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.. _cluster-key-concepts:
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Key Concepts
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============
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Cluster
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-------
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A Ray cluster is a set of one or more nodes that are running Ray and share the
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same :ref:`head node<cluster-node-types>`.
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.. _cluster-node-types:
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Node types
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----------
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A Ray cluster consists of a :ref:`head node<cluster-head-node>` and a set of
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:ref:`worker nodes<cluster-worker-node>`.
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.. image:: ray-cluster.jpg
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:align: center
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:width: 600px
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.. _cluster-head-node:
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Head node
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~~~~~~~~~
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The head node is the first node started by the
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:ref:`Ray cluster launcher<cluster-launcher>` when trying to launch a Ray
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cluster. Among other things, the head node holds the :ref:`Global Control Store
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(GCS)<memory>` and runs the :ref:`autoscaler<cluster-autoscaler>`. Once the head
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node is started, it will be responsible for launching any additional
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:ref:`worker nodes<cluster-worker-node>`. The head node itself will also execute
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tasks and actors to utilize its capacity.
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.. _cluster-worker-node:
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Worker node
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~~~~~~~~~~~
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A worker node is any node in the Ray cluster that is not functioning as head node.
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Therefore, worker nodes are simply responsible for executing tasks and actors.
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When a worker node is launched, it will be given the address of the head node to
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form a cluster.
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.. _cluster-launcher:
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Cluster launcher
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----------------
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The cluster launcher is a process responsible for bootstrapping the Ray cluster
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by launching the :ref:`head node<cluster-head-node>`. For more information on how
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to use the cluster launcher, refer to
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:ref:`cluster launcher CLI commands documentation<cluster-commands>` and the
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corresponding :ref:`documentation for the configuration file<cluster-config>`.
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.. _cluster-autoscaler:
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Autoscaler
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----------
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The autoscaler is a process that runs on the :ref:`head node<cluster-head-node>`
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and is responsible for adding or removing :ref:`worker nodes<cluster-worker-node>`
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to meet the needs of the Ray workload while matching the specification in the
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:ref:`cluster config file<cluster-config>`. In particular, if the resource
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demands of the Ray workload exceed the current capacity of the cluster, the
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autoscaler will try to add nodes. Conversely, if a node is idle for long enough,
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the autoscaler will remove it from the cluster. To learn more about autoscaling,
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refer to the :ref:`Ray cluster deployment guide<deployment-guide-autoscaler>`.
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Ray Client
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----------
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The Ray Client is an API that connects a Python script to a remote Ray cluster.
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To learn more about the Ray Client, you can refer to the :ref:`documentation<ray-client>`.
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Job submission
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--------------
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Ray Job submission is a mechanism to submit locally developed and tested applications
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to a remote Ray cluster. It simplifies the experience of packaging, deploying,
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and managing a Ray application. To learn more about Ray jobs, refer to the
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:ref:`documentation<ray-job-submission-api-ref>`.
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Cloud clusters
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--------------
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If you’re using AWS, GCP, Azure (community-maintained) or Aliyun (community-maintained), you can use the
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:ref:`Ray cluster launcher<cluster-launcher>` to launch cloud clusters, which
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greatly simplifies the cluster setup process.
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Cluster managers
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----------------
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You can simplify the process of managing Ray clusters using a number of popular
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cluster managers including :ref:`Kubernetes<kuberay-index>`,
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:ref:`YARN<ray-yarn-deploy>`, :ref:`Slurm<ray-slurm-deploy>` and :ref:`LSF<ray-LSF-deploy>`.
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Kubernetes (K8s) operator
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-------------------------
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Deployments of Ray on Kubernetes are managed by the Ray Kubernetes Operator. The
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Ray Operator makes it easy to deploy clusters of Ray pods within a Kubernetes
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cluster. To learn more about the K8s operator, refer to
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the :ref:`documentation<kuberay-index>`.
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