Merge branch 'NVIDIA:main' into embed

docs/examples/fl_experiment_tracking_mlflow.rst

@@ -54,8 +54,6 @@ Adding MLflow Logging to Configurations
 Inside the config folder there are two files, ``config_fed_client.conf`` and ``config_fed_server.conf``.
 
 .. literalinclude:: ../../examples/advanced/experiment-tracking/mlflow/jobs/hello-pt-mlflow/app/config/config_fed_client.conf
-   :language:
-   :linenos:
    :caption: config_fed_client.conf
 
 Take a look at the components section of the client config at line 24.
@@ -70,8 +68,6 @@ Finally, :class:`ConvertToFedEvent<nvflare.app_common.widgets.convert_to_fed_eve
 This changes the event ``analytix_log_stats`` into a fed event ``fed.analytix_log_stats``, which will then be streamed from the clients to the server.
 
 .. literalinclude:: ../../examples/advanced/experiment-tracking/mlflow/jobs/hello-pt-mlflow/app/config/config_fed_server.conf
-   :language:
-   :linenos:
    :caption: config_fed_server.conf
 
 Under the component section in the server config, we have the

docs/examples/hello_cross_val.rst

@@ -110,36 +110,15 @@ and adding a ``random_epsilon`` before returning the results packaged with a DXO
   NVIDIA FLARE can be used with any data packaged inside a :ref:`Shareable <shareable>` object (subclasses ``dict``), and
   :ref:`DXO <data_exchange_object>` is recommended as a way to manage that data in a standard way.
 
-Application Configuration
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Inside the config folder there are two files, ``config_fed_client.json`` and ``config_fed_server.json``.
-
-.. literalinclude:: ../../examples/hello-world/hello-numpy-cross-val/jobs/hello-numpy-cross-val/app/config/config_fed_server.json
-   :language: json
-   :linenos:
-   :caption: config_fed_server.json
-
-The server now has a second workflow configured after Scatter and Gather, :class:`CrossSiteModelEval<nvflare.app_common.workflows.cross_site_model_eval.CrossSiteModelEval>`.
-
-The components "model_locator" and "formatter" have been added to work with the cross site model evaluation workflow,
-and the rest is the same as in :doc:`Hello Scatter and Gather <hello_scatter_and_gather>`.
-
+Cross site validation!
+----------------------
 
-.. literalinclude:: ../../examples/hello-world/hello-numpy-cross-val/jobs/hello-numpy-cross-val/app/config/config_fed_client.json
-   :language: json
-   :linenos:
-   :caption: config_fed_client.json
+We can run it using NVFlare simulator
 
-The client configuration now has more tasks and an additional Executor ``NPValidator`` configured to handle the "validate" task.
-The "submit_model" task has been added to the ``NPTrainer`` Executor to work with the :class:`CrossSiteModelEval<nvflare.app_common.workflows.cross_site_model_eval.CrossSiteModelEval>`
-workflow to get the client models.
+.. code-block:: bash
 
-Cross site validation!
-----------------------
+  python3 job_train_and_cse.py
 
-.. |ExampleApp| replace:: hello-numpy-cross-val
-.. include:: run_fl_system.rst
 
 During the first phase, the model will be trained.
 
@@ -154,23 +133,18 @@ This can produce a lot of results. All the results will be kept in the job's wor
 Understanding the Output
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
-After starting the server and clients, you should begin to see
-some outputs in each terminal tracking the progress of the FL run.
-As each client finishes training, it will start the cross site validation process.
-During this you'll see several important outputs the track the progress of cross site validation.
+You can find the running logs and results inside the simulator's workspace:
 
-The server shows the log of each client requesting models, the models it sends and the results received.
-Since the server could be responding to many clients at the same time, it may
-require careful examination to make proper sense of events from the jumbled logs.
+.. code-block:: bash
 
+  ls /tmp/nvflare/jobs/workdir/
+  server/  site-1/  site-2/  startup/
 
-.. include:: access_result.rst
 
-.. note::
-    You could see the cross-site validation results
-    at ``[DOWNLOAD_DIR]/[JOB_ID]/workspace/cross_site_val/cross_val_results.json``
+The cross site validation results:
 
-.. include:: shutdown_fl_system.rst
+.. code-block:: bash
+  cat /tmp/nvflare/jobs/workdir/server/simulate_job/cross_site_val/cross_val_results.json
 
 Congratulations!
 
@@ -186,3 +160,4 @@ Previous Versions of Hello Cross-Site Validation
   - `hello-numpy-cross-val for 2.1 <https://github.com/NVIDIA/NVFlare/tree/2.1/examples/hello-numpy-cross-val>`_
   - `hello-numpy-cross-val for 2.2 <https://github.com/NVIDIA/NVFlare/tree/2.2/examples/hello-numpy-cross-val>`_
   - `hello-numpy-cross-val for 2.3 <https://github.com/NVIDIA/NVFlare/tree/2.3/examples/hello-world/hello-numpy-cross-val/>`_
+  - `hello-numpy-cross-val for 2.4 <https://github.com/NVIDIA/NVFlare/tree/2.4/examples/hello-world/hello-numpy-cross-val/>`_

docs/examples/hello_fedavg_numpy.rst

@@ -1,4 +1,4 @@
-.. _hello_fedavg_w_numpy:
+.. _hello_fedavg_numpy:
 
 Hello FedAvg with NumPy
 =======================
@@ -35,7 +35,7 @@ The following steps compose one cycle of weight updates, called a **round**:
  #. These updates are then sent to the server which will aggregate them to produce a model with new weights.
  #. Finally, the server sends this updated version of the model back to each client, so the clients can continue to calculate the next model weights in future rounds.
 
-For this exercise, we will be working with the ``hello-fedavg-numpy`` application in the examples folder.
+For this exercise, we will be working with the ``hello-fedavg-numpy`` in the examples folder.
 
 Let's get started. First clone the repo, if you haven't already:
 

docs/examples/hello_pt_job_api.rst

@@ -9,7 +9,7 @@ Before You Start
 Feel free to refer to the :doc:`detailed documentation <../programming_guide>` at any point
 to learn more about the specifics of `NVIDIA FLARE <https://pypi.org/project/nvflare/>`_.
 
-We recommend you first finish the :doc:`Hello FedAvg with NumPy <hello_fedavg_w_numpy>` exercise since it introduces the
+We recommend you first finish the :doc:`Hello FedAvg with NumPy <hello_fedavg_numpy>` exercise since it introduces the
 federated learning concepts of `NVIDIA FLARE <https://pypi.org/project/nvflare/>`_.
 
 Make sure you have an environment with NVIDIA FLARE installed.
@@ -55,7 +55,7 @@ NVIDIA FLARE Job API
 --------------------
 
 The ``fedavg_script_executor_hello-pt.py`` script for this hello-pt example is very similar to the ``fedavg_script_executor_hello-numpy.py`` script
-for the :doc:`Hello FedAvg with NumPy <hello_fedavg_w_numpy>` exercise. Other than changes to the names of the job and client script, the only difference
+for the :doc:`Hello FedAvg with NumPy <hello_fedavg_numpy>` exercise. Other than changes to the names of the job and client script, the only difference
 is a line to define the initial global model for the server:
 
 .. code-block:: python