title: How to use all vCPUs on google cloud with dask
body: <p>There are 16 vCPUs on my vertex AI Jupyter notebook, and I am writing a parallelized script. I wasn't sure if the right approach was to hardcode in parallel processing based on the number of vCPUs (and if so, how to choose nworkers vs nthreads). Should I do this</p>
  <pre><code>from dask.distributed import Client
  num_vcpus = 16
  dask_client = Client(threads_per_worker=1, n_workers=nvcpus)
  </code></pre>
  <p>or should I use the cloud deployment pipeline as outlined <a href="https://docs.dask.org/en/stable/deploying-cloud.html" rel="nofollow noreferrer">here</a> instead? What degree of parallelization by hand (setting up futures and tasks and partitions and then running <code>gather</code>) is required? For context, I don't know what Kubernetes is.</p>