Clarify expected uses cases, and how the Kubernetes cluster is found.

dask_kubernetes/core.py

@@ -28,7 +28,9 @@ class KubeCluster(Cluster):
     """ Launch a Dask cluster on Kubernetes
 
     This starts a local Dask scheduler and then dynamically launches
-    Dask workers on a Kubernetes cluster.
+    Dask workers on a Kubernetes cluster. The Kubernetes cluster is taken
+    to be either the current one on which this code is running, or as a 
+    fallback, the default one configured in a kubeconfig file.
 
     **Environments**
 
@@ -37,6 +39,14 @@ class KubeCluster(Cluster):
     libraries that you may wish to use (like NumPy, Pandas, or Scikit-Learn).
     See examples below for suggestions on how to manage and check for this.
 
+    **Network**
+
+    Since the Dask scheduler is launched locally, for it to work, we need to
+    be able to open network connections between this local node and all the
+    workers nodes on the Kubernetes cluster. If the current process is not
+    already on a Kubernetes node, some network configuration will likely be
+    required to make this work.
+
     **Resources**
 
     Your Kubernetes resource limits and requests should match the

doc/source/index.rst

@@ -5,6 +5,11 @@ Dask Kubernetes deploys Dask workers on Kubernetes clusters using native
 Kubernetes APIs.  It is designed to dynamically launch short-lived deployments
 of workers during the lifetime of a Python process.
 
+Currently, it is designed to be run from a pod on a Kubernetes cluster that
+has permissions to launch other pods. However, it can also work with a remote
+Kubernetes cluster (configured via a kubeconfig file), as long as it is possible
+to open network connections with all the workers nodes on the remote cluster.
+
 Install
 -------