Merge branch 'main' into gnn_notebook

docs/example_applications_algorithms.rst

@@ -6,36 +6,7 @@ Example Applications
 NVIDIA FLARE has several tutorials and examples to help you get started with federated learning and to explore certain features in the
 :github_nvflare_link:`examples directory <examples>`.
 
-1. Step-By-Step Example Series
-==============================
-
-:github_nvflare_link:`Step-by-Step Examples (GitHub) <examples/hello-world/step-by-step/>` - Step-by-step examples series with CIFAR-10 (image data) and HIGGS (tabular data) to showcase different FLARE features, workflows, and APIs.
-
-1.1 CIFAR-10 Image Data Examples
---------------------------------
-
-  * :github_nvflare_link:`image_stats <examples/hello-world/step-by-step/cifar10/stats/image_stats.ipynb>` - federated statistics (histograms) of CIFAR10.
-  * :github_nvflare_link:`sag <examples/hello-world/step-by-step/cifar10/sag/sag.ipynb>` - scatter and gather (SAG) workflow with PyTorch with Client API.
-  * :github_nvflare_link:`sag_deploy_map <examples/hello-world/step-by-step/cifar10/sag_deploy_map/sag_deploy_map.ipynb>` - scatter and gather workflow with deploy_map configuration for deployment of apps to different sites using the Client API.
-  * :github_nvflare_link:`sag_model_learner <examples/hello-world/step-by-step/cifar10/sag_model_learner/sag_model_learner.ipynb>` - scatter and gather workflow illustrating how to write client code using the ModelLearner.
-  * :github_nvflare_link:`sag_executor <examples/hello-world/step-by-step/cifar10/sag_executor/sag_executor.ipynb>` - scatter and gather workflow demonstrating show to write client-side executors.
-  * :github_nvflare_link:`sag_mlflow <examples/hello-world/step-by-step/cifar10/sag_mlflow/sag_mlflow.ipynb>` - MLflow experiment tracking logs with the Client API in scatter & gather workflows.
-  * :github_nvflare_link:`sag_he <examples/hello-world/step-by-step/cifar10/sag_he/sag_he.ipynb>` - homomorphic encyption using Client API and POC -he mode.
-  * :github_nvflare_link:`cse <examples/hello-world/step-by-step/cifar10/cse/cse.ipynb>` - cross-site evaluation using the Client API.
-  * :github_nvflare_link:`cyclic <examples/hello-world/step-by-step/cifar10/cyclic/cyclic.ipynb>` - cyclic weight transfer workflow with server-side controller.
-  * :github_nvflare_link:`cyclic_ccwf <examples/hello-world/step-by-step/cifar10/cyclic_ccwf/cyclic_ccwf.ipynb>` - client-controlled cyclic weight transfer workflow with client-side controller.
-  * :github_nvflare_link:`swarm <examples/hello-world/step-by-step/cifar10/swarm/swarm.ipynb>` - swarm learning and client-side cross-site evaluation with Client API.
-
-1.2 HIGGS Tabular Data Examples
--------------------------------
-
-  * :github_nvflare_link:`tabular_stats <examples/hello-world/step-by-step/higgs/stats/tabular_stats.ipynb>`- federated stats tabular histogram calculation.
-  * :github_nvflare_link:`sklearn_linear <examples/hello-world/step-by-step/higgs/sklearn-linear/sklearn_linear.ipynb>`- federated linear model (logistic regression on binary classification) learning on tabular data.
-  * :github_nvflare_link:`sklearn_svm <examples/hello-world/step-by-step/higgs/sklearn-svm/sklearn_svm.ipynb>`- federated SVM model learning on tabular data.
-  * :github_nvflare_link:`sklearn_kmeans <examples/hello-world/step-by-step/higgs/sklearn-kmeans/sklearn_kmeans.ipynb>`- federated k-Means clustering on tabular data.
-  * :github_nvflare_link:`xgboost <examples/hello-world/step-by-step/higgs/xgboost/xgboost_horizontal.ipynb>`- federated horizontal xgboost learning on tabular data with bagging collaboration.
-
-2. Hello World Examples
+1. Hello World Examples
 =======================
 Can be run from the :github_nvflare_link:`hello_world notebook <examples/hello-world/hello_world.ipynb>`.
 
@@ -45,12 +16,12 @@ Can be run from the :github_nvflare_link:`hello_world notebook <examples/hello-w
 
   examples/hello_world_examples
 
-2.1. Deep Learning to Federated Learning
+1.1. Deep Learning to Federated Learning
 ----------------------------------------
 
   * :github_nvflare_link:`Deep Learning to Federated Learning (GitHub) <examples/hello-world/ml-to-fl>` - Example for converting Deep Learning (DL) to Federated Learning (FL) using the Client API.
 
-2.2. Workflows
+1.2. Workflows
 --------------
 
   * :ref:`Hello Scatter and Gather <hello_scatter_and_gather>` - Example using the Scatter And Gather (SAG) workflow with a Numpy trainer
@@ -59,13 +30,44 @@ Can be run from the :github_nvflare_link:`hello_world notebook <examples/hello-w
   * :github_nvflare_link:`Swarm Learning <examples/advanced/swarm_learning>` - Example using Swarm Learning and Client-Controlled Cross-site Evaluation workflows.
   * :github_nvflare_link:`Client-Controlled Cyclic Weight Transfer <examples/hello-world/step-by-step/cifar10/cyclic_ccwf>` - Example using Client-Controlled Cyclic workflow using Client API.
 
-2.3. Deep Learning
+1.3. Deep Learning
 ------------------
 
   * :ref:`Hello PyTorch <hello_pt>` - Example image classifier using FedAvg and PyTorch as the deep learning training framework
   * :ref:`Hello TensorFlow <hello_tf2>` - Example image classifier using FedAvg and TensorFlow as the deep learning training frameworks
 
 
+
+2. Step-By-Step Example Series
+==============================
+
+:github_nvflare_link:`Step-by-Step Examples (GitHub) <examples/hello-world/step-by-step/>` - Step-by-step examples series with CIFAR-10 (image data) and HIGGS (tabular data) to showcase different FLARE features, workflows, and APIs.
+
+2.1 CIFAR-10 Image Data Examples
+--------------------------------
+
+  * :github_nvflare_link:`image_stats <examples/hello-world/step-by-step/cifar10/stats/image_stats.ipynb>` - federated statistics (histograms) of CIFAR10.
+  * :github_nvflare_link:`sag <examples/hello-world/step-by-step/cifar10/sag/sag.ipynb>` - scatter and gather (SAG) workflow with PyTorch with Client API.
+  * :github_nvflare_link:`sag_deploy_map <examples/hello-world/step-by-step/cifar10/sag_deploy_map/sag_deploy_map.ipynb>` - scatter and gather workflow with deploy_map configuration for deployment of apps to different sites using the Client API.
+  * :github_nvflare_link:`sag_model_learner <examples/hello-world/step-by-step/cifar10/sag_model_learner/sag_model_learner.ipynb>` - scatter and gather workflow illustrating how to write client code using the ModelLearner.
+  * :github_nvflare_link:`sag_executor <examples/hello-world/step-by-step/cifar10/sag_executor/sag_executor.ipynb>` - scatter and gather workflow demonstrating show to write client-side executors.
+  * :github_nvflare_link:`sag_mlflow <examples/hello-world/step-by-step/cifar10/sag_mlflow/sag_mlflow.ipynb>` - MLflow experiment tracking logs with the Client API in scatter & gather workflows.
+  * :github_nvflare_link:`sag_he <examples/hello-world/step-by-step/cifar10/sag_he/sag_he.ipynb>` - homomorphic encyption using Client API and POC -he mode.
+  * :github_nvflare_link:`cse <examples/hello-world/step-by-step/cifar10/cse/cse.ipynb>` - cross-site evaluation using the Client API.
+  * :github_nvflare_link:`cyclic <examples/hello-world/step-by-step/cifar10/cyclic/cyclic.ipynb>` - cyclic weight transfer workflow with server-side controller.
+  * :github_nvflare_link:`cyclic_ccwf <examples/hello-world/step-by-step/cifar10/cyclic_ccwf/cyclic_ccwf.ipynb>` - client-controlled cyclic weight transfer workflow with client-side controller.
+  * :github_nvflare_link:`swarm <examples/hello-world/step-by-step/cifar10/swarm/swarm.ipynb>` - swarm learning and client-side cross-site evaluation with Client API.
+
+2.2 HIGGS Tabular Data Examples
+-------------------------------
+
+  * :github_nvflare_link:`tabular_stats <examples/hello-world/step-by-step/higgs/stats/tabular_stats.ipynb>`- federated stats tabular histogram calculation.
+  * :github_nvflare_link:`sklearn_linear <examples/hello-world/step-by-step/higgs/sklearn-linear/sklearn_linear.ipynb>`- federated linear model (logistic regression on binary classification) learning on tabular data.
+  * :github_nvflare_link:`sklearn_svm <examples/hello-world/step-by-step/higgs/sklearn-svm/sklearn_svm.ipynb>`- federated SVM model learning on tabular data.
+  * :github_nvflare_link:`sklearn_kmeans <examples/hello-world/step-by-step/higgs/sklearn-kmeans/sklearn_kmeans.ipynb>`- federated k-Means clustering on tabular data.
+  * :github_nvflare_link:`xgboost <examples/hello-world/step-by-step/higgs/xgboost/xgboost_horizontal.ipynb>`- federated horizontal xgboost learning on tabular data with bagging collaboration.
+
+
 3. Tutorial Notebooks
 =====================
 

docs/user_guide/nvflare_cli/poc_command.rst

@@ -1,11 +1,9 @@
 .. _poc_command:
 
 *****************************************
-Command for Proof Of Concept (POC) Mode
+Proof Of Concept (POC) Command
 *****************************************
 
-Introduction to the POC Command
-===============================
 
 The POC command allows users to try out the features of NVFlare in a proof of concept deployment on a single machine.
 

examples/README.md

@@ -72,7 +72,17 @@ Start a Jupyter Lab:
 When you open a notebook, select the kernel `nvflare_example` using the dropdown menu at the top right.
 ![Selecting a JupyterLab kernel](./jupyterlab_kernel.png)
 
-## 1. Step-by-Step Examples
+## 1. Hello World Examples
+| Example                                                                                                                                | Framework    | Summary                                                                                                                                                         |
+|----------------------------------------------------------------------------------------------------------------------------------------|--------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [Notebook for Hello Examples](./hello-world/hello_world.ipynb)                                                                         | -            | Notebook for examples below.                                                                                                                                    |
+| [Hello Scatter and Gather](./hello-world/hello-numpy-sag/README.md)                                                                    | Numpy        | Example using [ScatterAndGather](https://nvflare.readthedocs.io/en/main/apidocs/nvflare.app_common.workflows.scatter_and_gather.html) controller workflow.      |
+| [Hello Cross-Site Validation](./hello-world/hello-numpy-cross-val/README.md)                                                           | Numpy        | Example using [CrossSiteModelEval](https://nvflare.readthedocs.io/en/main/apidocs/nvflare.app_common.workflows.cross_site_model_eval.html) controller workflow. |
+| [Hello Cyclic Weight Transfer](./hello-world/hello-cyclic/README.md)                                                                   | PyTorch      | Example using [CyclicController](https://nvflare.readthedocs.io/en/main/apidocs/nvflare.app_common.workflows.cyclic_ctl.html) controller workflow to implement [Cyclic Weight Transfer](https://pubmed.ncbi.nlm.nih.gov/29617797/). |
+| [Hello PyTorch](./hello-world/hello-pt/README.md)                                                                                      | PyTorch      | Example using an image classifier using [FedAvg](https://arxiv.org/abs/1602.05629) and [PyTorch](https://pytorch.org/) as the deep learning training framework. |
+| [Hello TensorFlow](./hello-world/hello-tf2/README.md)                                                                                  | TensorFlow2  | Example of using an image classifier using [FedAvg](https://arxiv.org/abs/1602.05629) and [TensorFlow](https://tensorflow.org/) as the deep learning training framework. |
+
+## 2. Step-by-Step Examples
 | Example | Dataset | Controller-Type | Client Category | Framework | Summary |
 |---------|---------|-----------------|-----------------|-----------|---------|
 | [image_stats](./hello-world/step-by-step/cifar10/stats/image_stats.ipynb)                                                        | CIFAR10 | server | Executor | Pandas | Example for federated stats image histogram calculation. |
@@ -92,16 +102,6 @@ When you open a notebook, select the kernel `nvflare_example` using the dropdown
 | [sklearn_kmeans](./hello-world/step-by-step/higgs/sklearn-kmeans/sklearn_kmeans.ipynb)                                           | HIGGS | server | Client API |sklearn | Example for federated k-Means clustering on tabular data. |
 | [xgboost](./hello-world/step-by-step/higgs/xgboost/xgboost_horizontal.ipynb)                                                        | HIGGS | server | Client API |XGBoost | Example for federated horizontal xgboost learning on tabular data with bagging collaboration. |
 
-## 2. Hello World Examples
-| Example                                                                                                                                | Framework    | Summary                                                                                                                                                         |
-|----------------------------------------------------------------------------------------------------------------------------------------|--------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Notebook for Hello Examples](./hello-world/hello_world.ipynb)                                                                         | -            | Notebook for examples below.                                                                                                                                    |
-| [Hello Scatter and Gather](./hello-world/hello-numpy-sag/README.md)                                                                    | Numpy        | Example using [ScatterAndGather](https://nvflare.readthedocs.io/en/main/apidocs/nvflare.app_common.workflows.scatter_and_gather.html) controller workflow.      |
-| [Hello Cross-Site Validation](./hello-world/hello-numpy-cross-val/README.md)                                                           | Numpy        | Example using [CrossSiteModelEval](https://nvflare.readthedocs.io/en/main/apidocs/nvflare.app_common.workflows.cross_site_model_eval.html) controller workflow. |
-| [Hello Cyclic Weight Transfer](./hello-world/hello-cyclic/README.md)                                                                   | PyTorch      | Example using [CyclicController](https://nvflare.readthedocs.io/en/main/apidocs/nvflare.app_common.workflows.cyclic_ctl.html) controller workflow to implement [Cyclic Weight Transfer](https://pubmed.ncbi.nlm.nih.gov/29617797/). |
-| [Hello PyTorch](./hello-world/hello-pt/README.md)                                                                                      | PyTorch      | Example using an image classifier using [FedAvg](https://arxiv.org/abs/1602.05629) and [PyTorch](https://pytorch.org/) as the deep learning training framework. |
-| [Hello TensorFlow](./hello-world/hello-tf2/README.md)                                                                                  | TensorFlow2  | Example of using an image classifier using [FedAvg](https://arxiv.org/abs/1602.05629) and [TensorFlow](https://tensorflow.org/) as the deep learning training framework. |
-
 ## 3. Tutorial notebooks
 | Example                                                                                                                                | Summary                                                                                                                                                         |
 |----------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|