Dask Kubernetes Operator
Contents
Dask Kubernetes Operator¶
Welcome to the documentation for the Dask Kubernetes Operator.
Note
If you are looking for high-level documentation on deploying Dask on Kubernetes new users should head to the Dask documentation page on Kubernetes.
The package dask-kubernetes
provides a Dask operator for Kubernetes. dask-kubernetes
is one of many options for deploying Dask clusters, see Deploying Dask in the Dask documentation for an overview of additional options.
Quickstart¶
KubeCluster
deploys Dask clusters on Kubernetes clusters using custom
Kubernetes resources. It is designed to dynamically launch ad-hoc deployments.
$ # Install operator CRDs and controller, needs to be done once on your Kubernetes cluster
$ helm install --repo https://helm.dask.org --create-namespace -n dask-operator --generate-name dask-kubernetes-operator
$ # Install dask-kubernetes
$ pip install dask-kubernetes
from dask_kubernetes.operator import KubeCluster
cluster = KubeCluster(name="my-dask-cluster", image='ghcr.io/dask/dask:latest')
cluster.scale(10)
What is the operator?¶
The Dask Operator is a set of custom resources and a controller that runs on your Kubernetes cluster and allows you to create and manage your Dask clusters as Kubernetes resources. Creating clusters can either be done via the Kubernetes API with kubectl or the Python API with KubeCluster.
To install the operator you need to apply some custom resource definitions that allow us to describe Dask resources and the operator itself which is a small Python application that
watches the Kubernetes API for events related to our custom resources and creates other resources such as Pods
and Services
accordingly.
What resources does the operator manage?¶
The operator manages a hierarchy of resources, some custom resources to represent Dask primitives like clusters and worker groups, and native Kubernetes resources such as pods and services to run the cluster processes and facilitate communication.
Worker Groups¶
A DaskWorkerGroup
represents a homogenous group of workers that can be scaled. The resource is similar to a native Kubernetes Deployment
in that it manages a group of workers
with some intelligence around the Pod
lifecycle. A worker group must be attached to a Dask Cluster resource in order to function.
All Kubernetes annotations on the
DaskWorkerGroup
resource will be passed onto worker Pod
resources. Annotations created by kopf or
kubectl (i.e. starting with “kopf.zalando.org” or “kubectl.kubernetes.io”) will not be passed on.
Clusters¶
The DaskCluster
custom resource creates a Dask cluster by creating a scheduler Pod
, scheduler Service
and default DaskWorkerGroup
which in turn creates worker Pod
resources.
Workers connect to the scheduler via the scheduler Service
and that service can also be exposed to the user in order to connect clients and perform work.
The operator also has support for creating additional worker groups. These are extra groups of workers with different configuration settings and can be scaled separately. You can then use resource annotations to schedule different tasks to different groups.
All Kubernetes annotations <https://kubernetes.io/docs/concepts/overview/working-with-objects/annotations/> on the
DaskCluster
resource will be passed onto the scheduler Pod
and Service
as well the DaskWorkerGroup
resources. Annotations created by kopf or kubectl (i.e. starting with “kopf.zalando.org” or “kubectl.kubernetes.io”)
will not be passed on.
For example you may wish to have a smaller pool of workers that have more memory for memory intensive tasks, or GPUs for compute intensive tasks.
Jobs¶
A DaskJob
is a batch style resource that creates a Pod
to perform some specific task from start to finish alongside a DaskCluster
that can be leveraged to perform the work.
All Kubernetes annotations <https://kubernetes.io/docs/concepts/overview/working-with-objects/annotations/> on the
DaskJob
resource will be passed on to the job-runner Pod
resource. If one also wants to set Kubernetes
annotations on the cluster-related resources (scheduler and worker Pods
), these can be set as
spec.cluster.metadata
in the DaskJob
resource. Annotations created by kopf or kubectl (i.e. starting with
“kopf.zalando.org” or “kubectl.kubernetes.io”) will not be passed on.
Once the job Pod
runs to completion the cluster is removed automatically to save resources. This is great for workflows like training a distributed machine learning model with Dask.
Autoscalers¶
A DaskAutoscaler
resource will communicate with the scheduler periodically and auto scale the default DaskWorkerGroup
to the desired number of workers.
from dask_kubernetes.operator import KubeCluster
cluster = KubeCluster(name="my-dask-cluster", image='ghcr.io/dask/dask:latest')
cluster.adapt(minimum=1, maximum=10)