Dask Helm Chart

Dask allows distributed computation in Python.

• https://dask.pydata.org
• http://jupyter.org/

Chart Details

This chart will deploy the following:

• 1 x Dask scheduler with port 8786 (scheduler) and 80 (Web UI) exposed on an external LoadBalancer
• 3 x Dask workers that connect to the scheduler
• 1 x Jupyter notebook (optional) with port 80 exposed on an external LoadBalancer
• All using Kubernetes Deployments

Installing the Chart

To install the chart with the release name my-release:

$ helm install --name my-release stable/dask

Configuration

The following tables list the configurable parameters of the Dask chart and their default values.

Dask scheduler

Parameter	Description	Default
scheduler.name	Dask scheduler name	scheduler
scheduler.image	Container image name	daskdev/dask
scheduler.imageTag	Container image tag	0.18.1
scheduler.replicas	k8s deployment replicas	1
scheduler.resources	Container resources	{}

Dask webUI

Parameter	Description	Default
webUI.name	Dask webui name	webui
webUI.servicePort	k8s service port	80

Dask worker

Parameter	Description	Default
worker.name	Dask worker name	worker
worker.image	Container image name	daskdev/dask
worker.imageTag	Container image tag	0.18.1
worker.replicas	k8s hpa and deployment replicas	3
worker.resources	Container resources	{}

|

jupyter

Parameter	Description	Default
jupyter.name	Jupyter name	jupyter
jupyter.enabled	Include optional Jupyter server	true
jupyter.image	Container image name	daskdev/dask-notebook
jupyter.imageTag	Container image tag	0.18.1
jupyter.replicas	k8s deployment replicas	1
jupyter.servicePort	k8s service port	80
jupyter.resources	Container resources	{}

Specify each parameter using the --set key=value[,key=value] argument to helm install.

Alternatively, a YAML file that specifies the values for the parameters can be provided while installing the chart. For example,

$ helm install --name my-release -f values.yaml stable/dask

Tip: You can use the default values.yaml

Customizing Python Environment

The default daskdev/dask images have a standard Miniconda installation along with some common packages like NumPy and Pandas. You can install custom packages with either Conda or Pip using optional environment variables. This happens when your container starts up. Consider the following config.yaml file as an example:

jupyter:
  env:
    -  EXTRA_PIP_PACKAGES: s3fs git+https://github.com/user/repo.git --upgrade
    -  EXTRA_CONDA_PACKAGES: scipy matplotlib -c conda-forge

worker:
  env:
    -  EXTRA_PIP_PACKAGES: s3fs git+https://github.com/user/repo.git --upgrade
    -  EXTRA_CONDA_PACKAGES: scipy -c conda-forge

Note that the Jupyter and Dask worker environments should have matching software environments, at least where a user is likely to distribute that functionality.