diff --git a/docs/source/history.rst b/docs/source/history.rst
index 1838286..9b501be 100644
--- a/docs/source/history.rst
+++ b/docs/source/history.rst
@@ -1,7 +1,7 @@
 History
 =======
 
-This package came out of the Dask_Distributed_ project with help from the
+This package came out of the `Dask Distributed`_ project with help from the
 Pangeo_ collaboration. The original code was contained in the ``distributed.cli.dask_mpi``
 module and the original tests were contained in the ``distributed.cli.tests.test_dask_mpi``
 module.  The impetus for pulling Dask-MPI out of Dask-Distributed was provided by feedback
@@ -9,5 +9,5 @@ on the Dask Distributted `Issue 2402 <https://github.com/dask/distributed/issues
 
 Development history for these original files was preserved.
 
-.. _Dask_Distributed: https://github.com/dask/distributed
+.. _`Dask Distributed`: https://github.com/dask/distributed
 .. _Pangeo: https://pangeo.io
diff --git a/docs/source/index.rst b/docs/source/index.rst
index b2b9cce..620f221 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -8,59 +8,77 @@ The Dask-MPI project makes it easy to deploy Dask from within an existing MPI
 environment, such as one created with the common MPI command-line launchers
 ``mpirun`` or ``mpiexec``.  Such environments are commonly found in high performance
 supercomputers, academic research institutions, and other clusters where MPI
-has already been installed.  Dask-MPI provides a convenient interface for
-launching your cluster either from within a batch script or directly from the
-command-line.
+has already been installed.
 
-Example:
---------
+Dask-MPI provides two convenient interfaces to launch Dask, either from within
+a batch script or directly from the command-line.
 
-You can launch a Dask cluster directly from the command-line using the ``dask-mpi``
-command and specifying a scheduler JSON file.
+Batch Script Example
+--------------------
+
+You can turn your batch Python script into an MPI executable
+with the ``dask_mpi.initialize`` function.
+
+.. code-block:: python
+
+   from dask_mpi import initialize
+   initialize()
+
+   from dask.distributed import Client
+   client = Client()  # Connect this local process to remote workers
+
+This makes your Python script launchable directly with ``mpirun`` or ``mpiexec``.
 
 .. code-block:: bash
 
-   mpirun -np 4 dask-mpi --scheduler-file /path/to/scheduler.json
+   mpirun -np 4 python my_client_script.py
 
-You can then access this cluster from a batch script or an interactive session
-(such as a Jupyter Notebook) by referencing the scheduler file.
+This deploys the Dask scheduler and workers as well as the user's Client
+process within a single cohesive MPI computation.
 
-.. code-block:: python
 
-   from dask.distributed import Client
-   client = Client(scheduler_file='/path/to/scheduler.json')
+Command Line Example
+--------------------
 
+Alternatively you can launch a Dask cluster directly from the command-line
+using the ``dask-mpi`` command and specifying a scheduler file where Dask can
+write connection information.
 
-Example:
---------
+.. code-block:: bash
 
-Alternatively, you can turn your batch Python script into an MPI executable
-simply by using the ``initialize`` function.
+   mpirun -np 4 dask-mpi --scheduler-file ~/dask-scheduler.json
 
-.. code-block:: python
+You can then access this cluster either from a separate batch script or from an
+interactive session (such as a Jupyter Notebook) by referencing the same scheduler
+file that ``dask-mpi`` created.
 
-   from dask_mpi import initialize
-   initialize()
+.. code-block:: python
 
    from dask.distributed import Client
-   client = Client()  # Connect this local process to remote workers
+   client = Client(scheduler_file='~/dask-scheduler.json')
 
-which makes your Python script launchable directly with ``mpirun`` or ``mpiexec``.
 
-.. code-block:: bash
+Use Job Queuing System Directly
+-------------------------------
+
+You can also use `Dask Jobqueue <https://jobqueue.dask.org>`_ to deploy Dask
+directly on a job queuing system like SLURM, SGE, PBS, LSF, Torque, or others.
+This can be especially nice when you want to dynamically scale your cluster
+during your computation, or for interactive use.
 
-   mpirun -np 4 python my_client_script.py
 
 .. toctree::
    :maxdepth: 1
+   :hidden:
    :caption: Getting Started
 
    install
-   interactive
    batch
+   interactive
 
 .. toctree::
    :maxdepth: 1
+   :hidden:
    :caption: Detailed use
 
    cli
@@ -68,6 +86,7 @@ which makes your Python script launchable directly with ``mpirun`` or ``mpiexec`
 
 .. toctree::
    :maxdepth: 1
+   :hidden:
    :caption: Help & Reference
 
    howitworks
diff --git a/docs/source/install.rst b/docs/source/install.rst
index 97926ad..aa6dd31 100644
--- a/docs/source/install.rst
+++ b/docs/source/install.rst
@@ -29,11 +29,7 @@ To install Dask-MPI from source, clone the repository from `github
 
     git clone https://github.com/dask/dask-mpi.git
     cd dask-mpi
-    python setup.py install
-
-or use ``pip`` locally if you want to install all dependencies as well::
-
-    pip install -e .
+    pip install .
 
 You can also install directly from git master branch::