Dask Cloud Provider¶

Native Cloud integration for Dask.

This library creates Dask clusters on a given cloud provider with no set up other than having credentials. Currently, it only supports AWS.

Installation¶

Pip¶

$ pip install dask-cloudprovider

Conda¶

$ conda install -c conda-forge dask-cloudprovider

Below are the different modules for creating clusters on various cloud providers.

AWS¶

In order to create clusters on AWS you need to set your access key, secret key and region. The simplest way is to use the aws command line tool.

$ pip install awscli
$ aws configure

Fargate/ECS¶

The FargateCluster will create a new Fargate ECS cluster by default along with all the IAM roles, security groups, and so on that it needs to function.

from dask_cloudprovider import FargateCluster
cluster = FargateCluster()

⚠ All AWS resources created by FargateCluster should be removed on garbage collection. If the process is killed harshly this will not happen.

Note that in many cases you will want to specify a custom Docker image to FargateCluster so that Dask has the packages it needs to execute your workflow.

from dask_cloudprovider import FargateCluster
cluster = FargateCluster(image="<hub-user>/<repo-name>[:<tag>]")

One strategy to ensure that package versions match between your custom environment and the Docker container is to create your environment from an environment.yml file, export the exact package list for that environment using conda list --export > package-list.txt, and then use the pinned package versions contained in package-list.txt in your Dockerfile. You could use the default Dask Dockerfile as a template and simply add your pinned additional packages.

You can also create Dask clusters using EC2 based ECS clusters using ECSCluster.

Creating the ECS cluster is out of scope for this library but you can pass in the ARN of an existing one like this:

from dask_cloudprovider import ECSCluster
cluster = ECSCluster(cluster_arn="arn:aws:ecs:<region>:<acctid>:cluster/<clustername>")

All the other required resources such as roles, task definitions, tasks, etc will be created automatically like in FargateCluster.

GPU Support¶

There is also support in ECSCluster for GPU aware Dask clusters. To do this you need to create an ECS cluster with GPU capable instances (from the g3, p3 or p3dn families) and specify the number of GPUs each worker task should have.

from dask_cloudprovider import ECSCluster
cluster = ECSCluster(
    cluster_arn="arn:aws:ecs:<region>:<acctid>:cluster/<gpuclustername>",
    worker_gpu=1)

By setting the worker_gpu option to something other than None will cause the cluster to run dask-cuda-worker as the worker startup command. Setting this option will also change the default Docker image to rapidsai/rapidsai:latest, if you’re using a custom image you must ensure the NVIDIA CUDA toolkit is installed with a version that matches the host machine along with dask-cuda.