Dask Gateway, set worker resources - dask

Question

I am trying to set the resources for workers as per the docs here, but on a set up that uses Dask Gateway. Specifically, I'd like to be able to follow the answer to this question, but using Dask Gateway.
I haven't been able to find a reference to worker resources in the ClusterConfig options, and I tried the following (as per this answer), which doesn't seem to work:
def set_resources(dask_worker):
dask_worker.set_resources(task_limit=1)
return dask_worker.available_resources, dask_worker.total_resources
client.run(set_resources)
# output from a 1 worker cluster
> {'tls://255.0.91.211:39302': ({}, {})}
# checking info known by scheduler
cluster.scheduler_info
> {'type': 'Scheduler',
'id': 'Scheduler-410438c9-6b3a-494d-974a-52d9e9fss121',
'address': 'tls://255.0.44.161:8786',
'services': {'dashboard': 8787, 'gateway': 8788},
'started': 1632434883.9022279,
'workers': {'tls://255.0.92.232:39305': {'type': 'Worker',
'id': 'dask-worker-f95c163cf41647c6a6d85da9efa9919b-wvnf6',
'host': '255.0.91.211',
'resources': {}, #### still {} empty dict
'local_directory': '/home/jovyan/dask-worker-space/worker-ir8tpkz_',
'name': 'dask-worker-f95c157cf41647c6a6d85da9efa9919b-wvnf6',
'nthreads': 4,
'memory_limit': 6952476672,
'services': {'dashboard': 8787},
'nanny': 'tls://255.0.92.232:40499'}}}
How can this be done, either when the cluster is created using the config.yaml of the helm chart (ideally, a field in the cluster options that a user can change!) for Dask Gateway, or after the workers are already up and running?

Answer 1

I've found a way to specify this, at least on Kubernetes, is through the KubeClusterConfig.worker_extra_container_config. This is my yaml snippet for a working configuration (specifically, this is in my config for the daskhub helm deploy):
dask-gateway:
gateway:
backend:
worker:
extraContainerConfig:
env:
- name: DASK_DISTRIBUTED__WORKER__RESOURCES__TASKSLOTS
value: "1"
An option to set worker resources isn't exposed in the cluster options, and isn't explicitly exposed in the KubeClusterConfig. The specific format for the environment variable is described here. Resource environment variables need to be set before the dask worker process is started, I found it doesn't work when I set KubeClusterConfig.environment.
Using this, I am able to run multithreaded numpy (np.dot) using mkl in a dask worker container that has been given 4 cores. I see 400% CPU usage and only one task assigned to each worker.