[docs] copy edit of Job Submission Getting Started and FAQ (#41342)

Quick follow to #41319

---------

Signed-off-by: angelinalg <122562471+angelinalg@users.noreply.github.com>
Co-authored-by: Archit Kulkarni <architkulkarni@users.noreply.github.com>

doc/source/cluster/faq.rst

@@ -4,24 +4,24 @@
 FAQ
 ===
 
-These are some Frequently Asked Questions that we've seen pop up for using Ray clusters.
-If you still have questions after reading this FAQ,  please reach out on
-`our Discourse <https://discuss.ray.io/>`__!
+These are some Frequently Asked Questions for Ray clusters.
+If you still have questions after reading this FAQ, reach out on the
+`Ray Discourse forum <https://discuss.ray.io/>`__.
 
 Do Ray clusters support multi-tenancy?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Yes, you can run multiple :ref:`jobs <jobs-overview>` from different users simultaneously in a Ray cluster
-but it's NOT recommended in production.
-The reason is that Ray currently still misses some features for multi-tenancy in production:
+but it's not recommended in production.
+Some Ray features are still missing for multi-tenancy in production:
 
 * Ray doesn't provide strong resource isolation:
   Ray :ref:`resources <core-resources>` are logical and they don't limit the physical resources a task or actor can use while running.
   This means simultaneous jobs can interfere with each other and makes them less reliable to run in production.
 
 * Ray doesn't support priorities: All jobs, tasks and actors have the same priority so there is no way to prioritize important jobs under load.
 
-* Ray doesn't support access control: jobs have full access to a Ray cluster and all of the resources within it.
+* Ray doesn't support access control: Jobs have full access to a Ray cluster and all of the resources within it.
 
 On the other hand, you can run the same job multiple times using the same cluster to save the cluster startup time.
 
@@ -32,14 +32,14 @@ On the other hand, you can run the same job multiple times using the same cluste
 I have multiple Ray users. What's the right way to deploy Ray for them?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-It's recommended to start a Ray cluster for each user so that their workloads are isolated.
+Start a Ray cluster for each user to isolate their workloads.
 
-What is the difference between ``--node-ip-address`` and ``--address``?
+What's the difference between ``--node-ip-address`` and ``--address``?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 When starting a head node on a machine with more than one network address, you
-may need to specify the externally-available address so worker nodes can
-connect. This is done with:
+may need to specify the externally available address so worker nodes can
+connect. Use this command:
 
 .. code:: bash
 
@@ -54,41 +54,39 @@ Then when starting the worker node, use this command to connect to the head node
 What does a worker node failure to connect look like?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-If the worker node cannot connect to the head node, you should see this error
+If the worker node can't connect to the head node, you should see this error:
 
     Unable to connect to GCS at xx.xx.xx.xx:nnnn. Check that (1) Ray GCS with
     matching version started successfully at the specified address, and (2)
     there is no firewall setting preventing access.
 
-The most likely cause is that the worker node cannot access the IP address
+The most likely cause is that the worker node can't access the IP address
 given. You can use ``ip route get xx.xx.xx.xx`` on the worker node to start
 debugging routing issues.
 
-You may also see failures in the log like
+You may also see failures in the log like:
 
     This node has an IP address of xx.xx.xx.xx, while we can not found the
     matched Raylet address. This maybe come from when you connect the Ray
     cluster with a different IP address or connect a container.
 
-which can be caused by overloading the head node with too many simultaneous
-connections. The solution for this is to start the worker nodes more slowly.
+The cause of this error may be the head node overloading with too many simultaneous
+connections. The solution for this problem is to start the worker nodes more slowly.
 
-I am having problems getting my SLURM cluster to work
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Problems getting a SLURM cluster to work
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-There seem to be a class of issues starting ray on SLURM clusters.  While we
-have not been able to pin down the exact causes (as of June 2023), work has
-been done to mitigate some of the resource contention. Some of the issues
-reported:
+A class of issues exist with starting Ray on SLURM clusters. While the exact causes aren't understood, (as of June 2023), some Ray 
+improvements mitigate some of the resource contention. Some of the issues
+reported are as follows:
 
 * Using a machine with a large number of CPUs, and starting one worker per CPU
   together with OpenBLAS (as used in NumPy) may allocate too many threads. This
-  is an `known OpenBLAS limitation`_ and can be mitigated by limiting OpenBLAS
+  issue is a `known OpenBLAS limitation`_. You can mitigate it by limiting OpenBLAS
   to one thread per process as explained in the link.
 
-* Resource allocation is not what was expected: usually too many CPUs per node
-  were allocated. Best practice is to verify your SLURM configuration without
-  starting ray to verify that the allocations are as expected. For more
+* Resource allocation isn't as expected: usually the configuration has too many CPUs allocated per node. The best practice is to verify the SLURM configuration without
+  starting Ray to verify that the allocations are as expected. For more
   detailed information see :ref:`ray-slurm-deploy`.
 
 .. _`known OpenBLAS limitation`: https://github.com/xianyi/OpenBLAS/wiki/faq#how-can-i-use-openblas-in-multi-threaded-applications