There are a variety of ways to deploy Dask on cloud providers. Cloud providers provide managed services, like VMs, Kubernetes, Yarn, or custom APIs with which Dask can connect easily. You may want to consider the following options:

  1. A managed Kubernetes service and Dask’s Kubernetes integration.

  2. A managed Yarn service, like Amazon EMR or Google Cloud DataProc and Dask-Yarn.

    Specific documentation for the popular Amazon EMR service can be found here.

  3. Directly launching cloud resources such as VMs or containers via a cluster manager with Dask Cloud Provider.

  4. A commercial Dask deployment option like Coiled to handle the creation and management of Dask clusters on a cloud computing environment (AWS and GCP).

Cloud Deployment Example

Using Dask Cloud Provider to launch a cluster of VMs on a platform like DigitalOcean can be as convenient as launching a local cluster.

>>> import dask.config

>>> dask.config.set({"cloudprovider.digitalocean.token": "yourAPItoken"})

>>> from dask_cloudprovider.digitalocean import DropletCluster

>>> cluster = DropletCluster(n_workers=1)
Creating scheduler instance
Created droplet dask-38b817c1-scheduler
Waiting for scheduler to run
Scheduler is running
Creating worker instance
Created droplet dask-38b817c1-worker-dc95260d

Many of the cluster managers in Dask Cloud Provider work by launching VMs with a startup script that pulls down the Dask Docker image and runs Dask components within that container. As with all cluster managers the VM resources, Docker image, etc are all configurable.

You can then connect a client and work with the cluster as if it were on your local machine.

>>> from dask.distributed import Client

>>> client = Client(cluster)

Data Access

You may want to install additional libraries in your Jupyter and worker images to access the object stores of each cloud (see Connect to remote data):

  • s3fs for Amazon’s S3

  • gcsfs for Google’s GCS

  • adlfs for Microsoft’s ADL

Historical Libraries

Dask previously maintained libraries for deploying Dask on Amazon’s EC2 and Google GKE. Due to sporadic interest, and churn both within the Dask library and EC2 itself, these were not well maintained. They have since been deprecated in favor of the Kubernetes solutions.