[tune] Fix Docs (#1469)

doc/source/tune.rst

@@ -67,11 +67,16 @@ Visualizing Results
 
 Ray Tune logs trial results to a unique directory per experiment, e.g. ``~/ray_results/my_experiment`` in the above example. The log records are compatible with a number of visualization tools:
 
-To visualize learning in tensorboard, run:
+To visualize learning in tensorboard, install TensorFlow:
+
+.. code-block:: bash
+
+    $ pip install tensorflow
+
+Then, after you run a experiment, you can visualize your experiment with TensorBoard by specifying the output directory of your results:
 
 .. code-block:: bash
 
-    $ pip install tensorboard
     $ tensorboard --logdir=~/ray_results/my_experiment
 
 .. image:: ray-tune-tensorboard.png
@@ -92,6 +97,8 @@ Finally, to view the results with a `parallel coordinates visualization <https:/
     $ cd $RAY_HOME/python/ray/tune
     $ jupyter-notebook ParallelCoordinatesVisualization.ipynb
 
+.. image:: ray-tune-parcoords.png
+
 Trial Variant Generation
 ------------------------
 
@@ -182,7 +189,13 @@ If your trainable function / class creates further Ray actors or tasks that also
 Client API
 ----------
 
-You can modify an ongoing experiment by adding or deleting trials using the Tune Client API. To do this, start your experiment with ``with_server=True``:
+You can modify an ongoing experiment by adding or deleting trials using the Tune Client API. To do this, verify that you have the ``requests`` library installed:
+
+.. code-block:: bash
+
+    $ pip install requests
+
+To use the Client API, you can start your experiment with ``with_server=True``:
 
 .. code-block:: python