Small docs tweaks (#505)

Author: jacobtomlinson

doc/source/conf.py

@@ -189,6 +189,10 @@
 
 
 # Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {"https://docs.python.org/": None}
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3", None),
+    "dask": ("https://docs.dask.org/en/latest/", None),
+    "distributed": ("https://distributed.dask.org/en/latest/", None),
+}
 
 redirects = {}

doc/source/operator.rst

@@ -8,10 +8,10 @@ Overview
 What is the operator?
 ---------------------
 
-The Dask Operator is a small service that runs on your Kubernetes cluster and allows you to create and manage your Dask clusters as native Kubernetes resources.
+The Dask Operator is a small service that runs on your Kubernetes cluster and allows you to create and manage your Dask clusters as Kubernetes resources.
 Creating clusters can either be done via the :doc:`Kubernetes API with kubectl <operator_resources>` or the :doc:`Python API with the experimental KubeCluster <operator_kubecluster>`.
 
-To :doc:`install the operator <operator_installation>` you needs to install some custom resource definitions that allow us to describe Dask resources and the operator itself which is a small Python application that
+To :doc:`install the operator <operator_installation>` you needs to apply some custom resource definitions that allow us to describe Dask resources and the operator itself which is a small Python application that
 watches the Kubernetes API for events related to our custom resources and creates other resources such as ``Pods`` and ``Services`` accordingly.
 
 What resources does the operator manage?

doc/source/operator_kubecluster.rst

@@ -8,7 +8,7 @@ KubeCluster (experimental)
 Cluster manager
 ---------------
 
-The operator has a new cluster manager called :class:`dask_kubernetes.experimental.KubeCluster` that you can use to conveniently create and manage a Dask cluster in Python. Then connect a :class:`dask.distributed.Client` object to it directly and perform your work.
+The operator has a new cluster manager called :class:`dask_kubernetes.experimental.KubeCluster` that you can use to conveniently create and manage a Dask cluster in Python. Then connect a Dask :class:`distributed.Client` object to it directly and perform your work.
 
 The goal of the cluster manager is to abstract away the complexity of the Kubernetes resources and provide a clean and simple Python API to manager clusters while still getting all the benefits of the operator.
 

doc/source/operator_resources.rst

@@ -65,8 +65,6 @@ Let's create an example called ``cluster.yaml`` with the following configuration
             imagePullPolicy: "IfNotPresent"
             args:
               - dask-worker
-              # Note the name of the cluster service, which adds "-service" to the end
-              - tcp://simple-cluster-service.default.svc.cluster.local:8786
               - --name
               - $(DASK_WORKER_NAME)
       scheduler: