Dask

Dask allows distributed computation in Python.

Chart Details

This chart will deploy the following:

1 x Dask scheduler with port 8786 (scheduler) and 8787 (Web UI) exposed on a ClusterIP (default)
2 x Dask workers that connect to the scheduler
1 x Jupyter lab notebook (optional, false by default) with port 8888 exposed on a ClusterIP (default)
1 x Pipeline Job (optional, false by default) which can run a SDP pipeline
All using Kubernetes Deployments

Note: only version 0.2.0 of the chart contains the Jupyter lab notebook pod. Note: only version 0.3.0 of the chart contains Pipeline job. The pipeline job is not part of the original chart and was added to run SDP pipelines that can run with dask.

Tip: See the Kubernetes Service Type Docs for the differences between ClusterIP, NodePort, and LoadBalancer.

Installing the Chart

First we need to add the helmdeployer-chars repo to our local helm config.

helm repo add ska-sdp-helm https://gitlab.com/ska-telescope/sdp/ska-sdp-helmdeploy-charts/-/raw/master/chart-repo
helm repo update

To install the dask chart with the release name test:

helm install test ska-sdp-helm/dask

Depending on how your cluster was set up, you may also need to specify a namespace with the following flag: --namespace my-namespace.

Default Configuration

The following tables list the configurable parameters of the Dask chart and their default values. Note: the container images are not provided by default, you have to specify them in a custom values.yaml file or as a command line argument.

Dask scheduler

Parameter	Description	Default
`scheduler.name`	Dask scheduler name	`scheduler`
`scheduler.image`	Container image name	`""`
`scheduler.imageTag`	Container image tag	`""`
`scheduler.replicas`	k8s deployment replicas	`1`
`scheduler.tolerations`	Tolerations	`[]`
`scheduler.nodeSelector`	nodeSelector	`{}`
`scheduler.affinity`	Container affinity	`{}`

Dask webUI

Parameter	Description	Default
`webUI.name`	Dask webui name	`webui`
`webUI.servicePort`	k8s service port	`80`
`webUI.ingress.enabled`	Enable ingress controller resource	false
`webUI.ingress.hostname`	Ingress resource hostnames	dask-ui.example.com
`webUI.ingress.tls`	Ingress TLS configuration	false
`webUI.ingress.secretName`	Ingress TLS secret name	`dask-scheduler-tls`
`webUI.ingress.annotations`	Ingress annotations configuration	null

Dask worker

Parameter	Description	Default
`worker.name`	Dask worker name	`worker`
`worker.image`	Container image name	`""`
`worker.imageTag`	Container image tag	`""`
`worker.replicas`	k8s hpa and deployment replicas	`2`
`worker.resources`	Container resources	`{}`
`worker.tolerations`	Tolerations	`[]`
`worker.nodeSelector`	nodeSelector	`{}`
`worker.affinity`	Container affinity	`{}`
`worker.port`	Worker port (defaults to random)	`""`

Jupyter

Parameter	Description	Default
`jupyter.name`	Jupyter name	`jupyter`
`jupyter.enabled`	Include optional Jupyter server	`false`
`jupyter.image`	Container image name	`""`
`jupyter.imageTag`	Container image tag	`""`
`jupyter.replicas`	k8s deployment replicas	`1`
`jupyter.servicePort`	k8s service port	`80`
`jupyter.resources`	Container resources	`{}`

Pipeline Job

Parameter	Description	Default
`pipeline.enabled`	Run pipeline post deployment of cluster	`false`
`pipeline.name`	Name of pipeline	`""`
`pipeline.command`	Command to invoke	`""`
`pipeline.args`	Arguments to the pipeline	`[]`
`pipeline.schedulerOptionName`	Name of cli argument to pass scheduler IP	`--dask-scheduler`

Running a SDP pipeline

A SDP pipeline can be run as a k8s job in this helm chart. The job is started after both the dask scheduler and dask workers are created.

To avoid pickling of functions, scheduler, worker and the job have the same image. This is necessary, especially if there are any calls to other non python libraries.

Note: The IP of the dask scheduler is added to the pipeline’s args as --dask-scheduler=<scheduler-service-ip>, this can be overridden with pipeline.schedulerOptionName

Note: The job uses the volume information from worker.volume.name and worker.volume.path. This is mandatory to access data for running a pipeline

It is mandatory to pass in image name, command, args and the folder to be mounted along with a PVC. These information would typically be populated upstream by SDP system

---
# pipeline_values.yaml
image: artefact.skao.int/ska-sdp-spectral-line-imaging:0.3.0

worker:
 volume:
   name: pvc-mnt-data
   path: /mnt/data

pipeline:
 enabled: true
 name: my-sdp-pipeline
 command: my-sdp-pipeline-cli
 schedulerOptionName: --dask-scheduler
 args:
 - run
 - --input
 - /mnt/data/input.ms

helm install -n <namespace> pipeline-test charts/dask --values pipeline_values.yaml

Custom Configuration

If you want to change the default parameters, you can do this in two ways.

YAML Config Files

You can update the default parameters in values.yaml by creating your own custom YAML config file with the updated parameters, and specify this file when installing your chart with the -f flag. Example:

helm install test ska-helm/dask -f values.yaml

Command-Line Arguments

If you want to change parameters for a specific install without changing values.yaml, you can use the --set key=value[,key=value] flag when running helm install, and it will override any default values. Example:

helm install test ska-helm/dask --set jupyter.enabled=false