update simulator to processes

docs/flare_overview.rst

@@ -26,7 +26,7 @@ Built for productivity
 FLARE is designed for maximum productivity, providing a range of tools to enhance user experience and research efficiency at different stages of the development process:
 
 - **FLARE Client API:** Enables users to transition seamlessly from ML/DL to FL with just a few lines of code changes.
-- **Simulator CLI:** Allows users to simulate federated learning or computing jobs in multi-thread settings within a single computer, offering quick response and debugging. The same job can be deployed directly to production.
+- **Simulator CLI:** Allows users to simulate federated learning or computing jobs in multi-process settings within a single computer, offering quick response and debugging. The same job can be deployed directly to production.
 - **POC CLI:** Facilitates the simulation of federated learning or computing jobs in multi-process settings within one computer. Different processes represent server, clients, and an admin console, providing users with a realistic sense of the federated network. It also allows users to simulate project deployment on a single host.
 - **Job CLI:** Permits users to create and submit jobs directly in POC or production environments.
 - **FLARE API:** Enables users to run jobs directly from Python code or notebooks.

docs/user_guide/nvflare_cli/fl_simulator.rst

@@ -49,7 +49,7 @@ Command examples
 Run a single NVFlare app
 ========================
 
-This command will run the same ``hello-numpy-sag`` app on the server and 8 clients using 1 thread. The client names will be site-1, site-2, ... , site-8:
+This command will run the same ``hello-numpy-sag`` app on the server and 8 clients using 1 process. The client names will be site-1, site-2, ... , site-8:
 
 .. code-block:: python
 
@@ -829,26 +829,26 @@ application run.
         status = run_simulator(args)
         sys.exit(status)
 
-****************************
-Threads, Clients, and Events
-****************************
+******************************
+Processes, Clients, and Events
+******************************
 
-Specifying threads
-==================
-The simulator ``-t`` option provides the ability to specify how many threads to run the simulator with.
+Specifying number of processes
+==============================
+The simulator ``-t`` option provides the ability to specify how many processes to run the simulator with.
 
 .. note::
 
-    We use the term threads for simplicity, however technically each client actually runs in a separate process.
-    This difference will not affect the user experience.
+    The ``-t`` and ``--threads`` option for simulator was originally due to clients running in separate threads.
+    However each client now actually runs in a separate process. This distinction will not affect the user experience.
 
 When you run the simulator with ``-t 1``, there is only one client active and running at a time, and the clients will be running in
 turn. This is to enable the simulation of large number of clients using a single machine with limited resources.
 
-Note that if you have fewer threads than the number of clients, ClientRunner/learner object will go thorugh setup and
+Note that if you have fewer processes than the number of clients, ClientRunner/learner object will go through setup and
 teardown in every round.
 
-With ``-t=num_client``, the simulator will run the number of clients in separate threads at the same time. Each
+With ``-t=num_client``, the simulator will run the number of clients in separate processes at the same time. Each
 client will always be running in memory with no swap_in / swap_out, but it will require more resources available.
 
 For the dataset / tensorboard initialization, you could make use of EventType.SWAP_IN and EventType.SWAP_OUT