Ray

Distributed Python cluster of a head node and workers for ML and general compute.

Provider: default
Kind: Big data cluster
Category: Compute
Official docs: https://docs.ray.io/en/latest/cluster/kubernetes/user-guides/configuring-autoscaling.html

Properties · 4 set by this component

property	type	values	required	kind default	this component
autoscaling	boolean	—	no	false	▸ false
dashboardPort · component property	integer	—	no	8265	inherited
deploymentTarget · component property	enum	kubernetes \| vm \| local	no	—	inherited
engine	enum	spark \| hadoop \| presto \| flink \| hpc \| ray \| dask \| kubernetes-batch	no	—	▸ ray
idleTimeoutSeconds · component property	integer	—	no	60	inherited
managed	boolean	—	no	false	▸ false
runtimeVersion	string	—	no	—	inherited
spotInstances	boolean	—	no	false	▸ false
tlsEnabled · component property	boolean	—	no	false	inherited
workerNodes	integer	—	no	—	inherited
zoneRedundant	boolean	—	no	—	inherited

Rules that apply · 1

Every rule whose selector covers this component's kind or its category. Whether a rule fires depends on the design: its properties, its edges and its groups.

Stateful node without zone redundancyerror

stateful-node-without-zone-redundancy

A stateful compute node pinned to exactly one availability zone disappears with that zone, and its state goes with it. Nodes inside a cell are exempt, because there the cell is deliberately single-zone and redundancy comes from the other cells.

— all of these hold:
— the node's `stateless` is false
— the node sits inside exactly 1 availability-zone group
— this does not hold:
— the node sits inside at least one cell group

Use it in a design

nodes:
  - { id: "ray", component: "ray" }

Other Big data cluster products · 25

Elastic High Performance Computingalibaba-e-hpcalibabacloudbig-data-cluster E-MapReducealibaba-e-mapreducealibabacloudbig-data-cluster AWS Clean Roomsaws-clean-roomsawsbig-data-cluster Amazon EMRaws-emrawsbig-data-cluster Amazon EMR Serverlessaws-emr-serverlessawsbig-data-cluster AWS Parallel Computing Serviceaws-pcsawsbig-data-cluster Amazon SageMaker HyperPodaws-sagemaker-hyperpodawsbig-data-cluster Azure CycleCloudazure-cyclecloudazurebig-data-cluster Azure Databricksazure-databricksazurebig-data-cluster Azure HDInsightazure-hdinsightazurebig-data-cluster Armadaarmadabig-data-cluster Generic big data clusterbig-data-clusterbig-data-cluster Daskdaskbig-data-cluster Databricksdatabricksbig-data-cluster Apache Hadoop YARNhadoop-yarnbig-data-cluster Slurmslurmbig-data-cluster Apache Spark clusterspark-clusterbig-data-cluster Volcanovolcanobig-data-cluster Cluster Directorgoogle-cluster-directorgcpbig-data-cluster Dataprocgoogle-dataprocgcpbig-data-cluster Managed Service for Apache Spark (serverless)google-dataproc-serverlessgcpbig-data-cluster IBM Analytics Engineibm-analytics-engineibmbig-data-cluster OCI AI Data Platformoci-ai-data-platformocibig-data-cluster OCI Big Data Serviceoci-big-data-serviceocibig-data-cluster OCI Data Flowoci-data-flowocibig-data-cluster

Source

1# yaml-language-server: $schema=../../schema/component.schema.json
2# Ray: head node plus worker nodes running distributed Python tasks and actors.
3# Defaults from "Ray Clusters Overview", "Configuring Autoscaling" and "Configuring Ray":
4# enableInTreeAutoscaling false, idleTimeoutSeconds 60, RAY_USE_TLS 0.
5schemaVersion: "1"
6type: "component"
7id: "ray"
8kind: "big-data-cluster"
9provider: "default"
10docs: "https://docs.ray.io/en/latest/cluster/kubernetes/user-guides/configuring-autoscaling.html"
11defaults:
12  engine: "ray"
13  autoscaling: false
14  spotInstances: false
15  managed: false
16properties:
17  deploymentTarget:
18    type: "enum"
19    values: ["kubernetes", "vm", "local"]
20  tlsEnabled:
21    type: "boolean"
22    default: false
23  idleTimeoutSeconds:
24    type: "integer"
25    default: 60
26  dashboardPort:
27    type: "integer"
28    default: 8265

The same page as markdown, for an agent: /catalogue/components/ray.md