This document contains frequently asked troubleshooting problems.

Unable to connect to scheduler

The most common issue is not being able to connect to the cluster once it has been constructed.

Each cluster manager will construct a Dask scheduler and by default expose it via a public IP address. You must be able to connect to that address on ports 8786 and 8787 from wherver your Python session is.

If you are unable to connect to this address it is likely that there is something wrong with your network configuration, for example you may have corporate policies implementing additional firewall rules on your account.

To reduce the chances of this happening it is often simplest to run Dask Cloudprovider from within the cloud you are trying to use and configure private networking only. See your specific cluster manager docs for info.

Invalid CPU or Memory

When working with FargateCluster or ECSCluster, CPU and memory arguments can only take values from a fixed set of combinations.

So, for example, code like this will result in an error

from import FargateCluster
cluster = FargateCluster(
    scheduler_timeout="15 minutes"
client = Client(cluster)

# botocore.errorfactory.ClientException:
# An error occurred (ClientException) when calling the RegisterTaskDefinition operation:
# No Fargate configuration exists for given values.

This is because ECS and Fargate task definitions with CPU=256 cannot have as much memory as that code is requesting.

The AWS-accepted set of combinations is documented at

Requested CPU Configuration Above Limit

When creating a FargateCluster or or ECSCluster, or adding additional workers, you may receive an error response with “The requested CPU configuration is above your limit”. This means that the scheduler and workers requested and any other EC2 resources you have running in that region use up more than your current service quota limit for vCPUs.

You can adjust the scheduler and/or worker CPUs with the scheduler_cpu and worker_cpu arguments. See the “Invalid CPU or Memory” section in this document for more information.

However, to get the desired cluster configuration you’ll need to request a service limit quota increase.

Go to https://<region> and request an increase for “Running On-Demand Standard (A, C, D, H, I, M, R, T, Z) instances”.

Pulling private Docker images

For cluster managers like EC2Cluster, AzureVMCluster and GCPCluster Docker images will be pulled onto VMs created on the cloud of your choice.

If you need to pull a private Docker images which requires authentication each VM will need to be configured with credentials. These cluster managers accept and extra_bootstrap argument where you can provide additional bash commands to be run during startup. This is a good place to log into your Docker registry.

from import AzureVMCluster
cluster = AzureVMCluster(...
                         extra_bootstrap=["docker login -u 'username' -p 'password'"])