<p>My code looks something like this</p>

<pre class="lang-py prettyprint-override"><code>def myfunc(param):
    # expensive stuff that takes 2-3h

mylist = [...]
client = Client(...)
mgr = DeploymentMgr()
# ... setup stateful set ...
futures = client.map(myfunc, mylist, ..., resources={mgr.hash.upper(): 1})
client.gather(futures)
</code></pre>

<p>I have dask running on a Kubernetes cluster. At the start of the program I create a stateful set. This is done via <code>kubernetes.client.AppsV1Api()</code>. Then I wait for up to 30 minutes until all workers that I have requested are available. For this example, say I request 10 workers, but after 30 minutes, only 7 workers are available. Finally, I call <code>client.map()</code> and pass a function and a list to it. This list has 10 elements. However, dask will only use 7 workers to process this list! Even if after a couple of minutes the remaining 3 workers are available, dask does not assign any list elements to them even if none of the processing of the first elements has finished.</p>

<p>How can I change that behaviour of dask? Is there a a way of telling dask (or the scheduler of dask) to periodically check for newly arriving workers and distribute work more "correctly"? Or can I manually influence the distribution of these list elements?</p>

<p>Thank you.</p>

r0f1
<p>Dask will balance loads once it has a better understanding of how long the tasks will take.  You can give an estimate of task length with the configuration values </p>

<pre class="lang-yaml prettyprint-override"><code>distributed:
  scheduler:
    default-task-durations:
      myfunc: 1hr
</code></pre>

<p>Or, once Dask finishes one of these tasks, it will know how to make decisions around that task in the future.</p>

<p>I believe that this has also come up a couple of times on the GitHub issue tracker.  You may want to search through <a href="https://github.com/dask/distributed/issues" rel="nofollow noreferrer">https://github.com/dask/distributed/issues</a> for more information.</p>
