<p>I'm running some I/O intensive Python code on Dask and want to increase the number of threads per worker. I've deployed a Kubernetes cluster that runs Dask distributed via <a href="https://github.com/helm/charts/blob/master/stable/dask/values.yaml" rel="nofollow noreferrer">helm</a>. I see from the <a href="https://github.com/helm/charts/blob/master/stable/dask/templates/dask-worker-deployment.yaml" rel="nofollow noreferrer">worker deployment template</a> that the number of threads for a worker is set to the number of CPUs, but I'd like to set the number of threads higher unless that's an anti-pattern. How do I do that? </p>

<p>It looks like from <a href="https://stackoverflow.com/questions/49406987/how-do-we-choose-nthreads-and-nprocs-per-worker-in-dask-distributed">this similar question</a> that I can ssh to the dask scheduler and spin up workers with <code>dask-worker</code>? But ideally I'd be able to configure the worker resources via helm so that I don't have to interact with the scheduler other than submitting jobs to it via the <code>Client</code>.  </p>

skeller88
<p>Kubernetes resource limits and requests should match the --memory-limit and --nthreads parameters given to the dask-worker command. For more information please follow the link <a href="https://kubernetes.dask.org/en/latest/index.html#quickstart" rel="nofollow noreferrer">1</a> (Best practices described on Dask`s official documentation) and <a href="https://stackoverflow.com/questions/54417135/dask-keeps-failing-with-killed-worker-exception-while-running-tpot">2</a> </p>
