securefederatedai · tanwarsh · Feb 2, 2025 · Jan 30, 2025 · Jan 30, 2025 · Jan 30, 2025
@@ -35,4 +35,4 @@ jobs:
         pip install .
     - name: Test Straggler Handling Interface
       run: |
-        python -m tests.github.test_hello_federation --template torch_cnn_mnist_straggler_check --fed_workspace aggregator --col1 col1 --col2 col2  --rounds-to-train 3
+        python -m tests.github.test_hello_federation --template torch/mnist_straggler_check --fed_workspace aggregator --col1 col1 --col2 col2  --rounds-to-train 3
@@ -25,8 +25,8 @@ on:
         type: choice
         options:
           - all
-          - torch_cnn_mnist
-          - keras_cnn_mnist
+          - torch/mnist
+          - keras/mnist
       python_version:
         description: "Python version"
         required: false
@@ -85,21 +85,21 @@ jobs:
         id: input_selection
         run: |
           # ---------------------------------------------------------------
-          # Models like XGBoost (xgb_higgs) and torch_cnn_histology require runners with higher memory and CPU to run.
+          # Models like XGBoost (xgb_higgs) and torch/histology require runners with higher memory and CPU to run.
           # Thus these models are excluded from the matrix for now.
           # Default combination if no input is provided (i.e. 'all' is selected).
-          # * TLS - models [torch_cnn_mnist, keras_cnn_mnist] and python versions [3.10, 3.11, 3.12]
-          # * Non-TLS - models [torch_cnn_mnist] and python version [3.10]
-          # * No client auth - models [keras_cnn_mnist] and python version [3.10]
-          # * Memory logs - models [torch_cnn_mnist] and python version [3.10]
+          # * TLS - models [torch/mnist, keras/mnist] and python versions [3.10, 3.11, 3.12]
+          # * Non-TLS - models [torch/mnist] and python version [3.10]
+          # * No client auth - models [keras/mnist] and python version [3.10]
+          # * Memory logs - models [torch/mnist] and python version [3.10]
           # ---------------------------------------------------------------
           echo "jobs_to_run=${{ env.JOBS_TO_RUN }}" >> "$GITHUB_OUTPUT"
 
           if [ "${{ env.MODEL_NAME }}" == "all" ]; then
-            echo "models_for_tls=[\"torch_cnn_mnist\", \"keras_cnn_mnist\"]" >> "$GITHUB_OUTPUT"
-            echo "models_for_non_tls=[\"torch_cnn_mnist\"]" >> "$GITHUB_OUTPUT"
-            echo "models_for_no_client_auth=[\"keras_cnn_mnist\"]" >> "$GITHUB_OUTPUT"
-            echo "models_for_memory_logs=[\"torch_cnn_mnist\"]" >> "$GITHUB_OUTPUT"
+            echo "models_for_tls=[\"torch/mnist\", \"keras/mnist\"]" >> "$GITHUB_OUTPUT"
+            echo "models_for_non_tls=[\"torch/mnist\"]" >> "$GITHUB_OUTPUT"
+            echo "models_for_no_client_auth=[\"keras/mnist\"]" >> "$GITHUB_OUTPUT"
+            echo "models_for_memory_logs=[\"torch/mnist\"]" >> "$GITHUB_OUTPUT"
           else
             echo "models_for_tls=[\"${{env.MODEL_NAME}}\"]" >> "$GITHUB_OUTPUT"
             echo "models_for_non_tls=[\"${{env.MODEL_NAME}}\"]" >> "$GITHUB_OUTPUT"

@@ -32,7 +32,7 @@ jobs:
     timeout-minutes: 15
     strategy:
       matrix:
-        model_name: ["keras_cnn_mnist"]
+        model_name: ["keras/mnist"]
         python_version: ["3.10", "3.11", "3.12"]
       fail-fast: false # do not immediately fail if one of the combinations fail
 
@@ -73,7 +73,7 @@ jobs:
     timeout-minutes: 15
     strategy:
       matrix:
-        model_name: ["keras_cnn_mnist"]
+        model_name: ["keras/mnist"]
         python_version: ["3.10"]
       fail-fast: false # do not immediately fail if one of the combinations fail
 
@@ -114,7 +114,7 @@ jobs:
     timeout-minutes: 15
     strategy:
       matrix:
-        model_name: ["keras_cnn_mnist"]
+        model_name: ["keras/mnist"]
         python_version: ["3.10"]
       fail-fast: false # do not immediately fail if one of the combinations fail
 
@@ -155,7 +155,7 @@ jobs:
     timeout-minutes: 15
     strategy:
       matrix:
-        model_name: ["keras_cnn_mnist"]
+        model_name: ["keras/mnist"]
         python_version: ["3.10"]
       fail-fast: false # do not immediately fail if one of the combinations fail
 

@@ -59,7 +59,7 @@ jobs:
     if: needs.input_selection.outputs.selected_jobs == 'tls' || needs.input_selection.outputs.selected_jobs == 'all'
     strategy:
       matrix:
-        model_name: ["keras_cnn_mnist"]
+        model_name: ["keras/mnist"]
         python_version: ["3.10"]
       fail-fast: false # do not immediately fail if one of the combinations fail
 
@@ -102,7 +102,7 @@ jobs:
     if: needs.input_selection.outputs.selected_jobs == 'non_tls' || needs.input_selection.outputs.selected_jobs == 'all'
     strategy:
       matrix:
-        model_name: ["keras_cnn_mnist"]
+        model_name: ["keras/mnist"]
         python_version: ["3.10"]
       fail-fast: false # do not immediately fail if one of the combinations fail
 
@@ -145,7 +145,7 @@ jobs:
     if: needs.input_selection.outputs.selected_jobs == 'no_client_auth' || needs.input_selection.outputs.selected_jobs == 'all'
     strategy:
       matrix:
-        model_name: ["keras_cnn_mnist"]
+        model_name: ["keras/mnist"]
         python_version: ["3.10"]
       fail-fast: false # do not immediately fail if one of the combinations fail
 

@@ -34,9 +34,9 @@ jobs:
     timeout-minutes: 30
     strategy:
       matrix:
-        # Models like XGBoost (xgb_higgs) and torch_cnn_histology require runners with higher memory and CPU to run.
+        # Models like XGBoost (xgb_higgs) and torch/histology require runners with higher memory and CPU to run.
         # Thus these models are excluded from the matrix for now.
-        model_name: ["torch_cnn_mnist", "keras_cnn_mnist"]
+        model_name: ["torch.mnist", "keras/mnist"]
         python_version: ["3.10"]
       fail-fast: false # do not immediately fail if one of the combinations fail
 
@@ -77,9 +77,9 @@ jobs:
     timeout-minutes: 30
     strategy:
       matrix:
-        # Testing this scenario only for torch_cnn_mnist model and python 3.10
+        # Testing this scenario only for torch/mnist model and python 3.10
         # If required, this can be extended to other models and python versions
-        model_name: ["torch_cnn_mnist"]
+        model_name: ["torch/mnist"]
         python_version: ["3.10"]
       fail-fast: false # do not immediately fail if one of the combinations fail
 
@@ -120,9 +120,9 @@ jobs:
     timeout-minutes: 30
     strategy:
       matrix:
-        # Testing this scenario for keras_cnn_mnist model and python 3.10
+        # Testing this scenario for keras/mnist model and python 3.10
         # If required, this can be extended to other models and python versions
-        model_name: ["keras_cnn_mnist"]
+        model_name: ["keras/mnist"]
         python_version: ["3.10"]
       fail-fast: false # do not immediately fail if one of the combinations fail
 

@@ -32,4 +32,4 @@ jobs:
         pip install .
     - name: Task Runner API
       run: |
-          python -m tests.github.test_hello_federation --template torch_cnn_mnist --fed_workspace aggregator --col1 collaborator1 --col2 collaborator2  --rounds-to-train 3 --save-model output_model
+          python -m tests.github.test_hello_federation --template torch/mnist --fed_workspace aggregator --col1 collaborator1 --col2 collaborator2  --rounds-to-train 3 --save-model output_model
@@ -31,4 +31,4 @@ jobs:
         pip install .
     - name: Test TaskRunner API with Eden Compression
       run: |
-        python -m tests.github.test_hello_federation --template torch_cnn_mnist_eden_compression --fed_workspace aggregator --col1 col1 --col2 col2  --rounds-to-train 3
+        python -m tests.github.test_hello_federation --template torch/mnist_eden_compression --fed_workspace aggregator --col1 col1 --col2 col2  --rounds-to-train 3
@@ -27,7 +27,7 @@ jobs:
 
     - name: Create workspace image
       run: |
-        fx workspace create --prefix example_workspace --template keras_cnn_mnist
+        fx workspace create --prefix example_workspace --template keras/mnist
         cd example_workspace
         fx plan initialize -a localhost
 

@@ -27,7 +27,7 @@ jobs:
 
     - name: Create workspace image
       run: |
-        fx workspace create --prefix example_workspace --template keras_cnn_mnist
+        fx workspace create --prefix example_workspace --template keras/mnist
         cd example_workspace
         fx plan initialize -a localhost
         fx workspace dockerize --save --revision https://github.com/${GITHUB_REPOSITORY}.git@${{ github.event.pull_request.head.sha }}

@@ -53,4 +53,4 @@ jobs:
           pip install .
       - name: Test TaskRunner API
         run: |
-          python -m tests.github.test_hello_federation --template keras_cnn_mnist --fed_workspace aggregator --col1 col1 --col2 col2  --rounds-to-train 3 --save-model output_model
+          python -m tests.github.test_hello_federation --template keras/mnist --fed_workspace aggregator --col1 col1 --col2 col2  --rounds-to-train 3 --save-model output_model
@@ -52,4 +52,4 @@ jobs:
           pip install .
       - name: Test TaskRunner API
         run: |
-          python -m tests.github.test_hello_federation --template keras_cnn_mnist --fed_workspace aggregator --col1 col1 --col2 col2  --rounds-to-train 3 --save-model output_model
+          python -m tests.github.test_hello_federation --template keras/mnist --fed_workspace aggregator --col1 col1 --col2 col2  --rounds-to-train 3 --save-model output_model
@@ -1,18 +1,15 @@
 def snykData = [
     'openfl-docker': 'openfl-docker/Dockerfile.base',
     'openfl': 'setup.py',
-    'openfl-workspace_tf_2dunet': 'openfl-workspace/tf_2dunet/requirements.txt',
-    'openfl-workspace_torch_cnn_mnist_straggler_check': 'openfl-workspace/torch_cnn_mnist_straggler_check/requirements.txt',
+    'openfl-workspace_keras_2dunet': 'openfl-workspace/keras/2dunet/requirements.txt',
+    'openfl-workspace_torch_cnn_mnist_straggler_check': 'openfl-workspace/torch/mnist_straggler_check/requirements.txt',
     // CN-14619 snyk test CLI does not support -f in requirements.txt file
-    // 'openfl-workspace_torch_cnn_histology': 'openfl-workspace/torch_cnn_histology/requirements.txt',
-    'openfl-workspace_torch_cnn_histology_src': 'openfl-workspace/torch_cnn_histology/src/requirements.txt',
-    'openfl-workspace_keras_nlp': 'openfl-workspace/keras_nlp/requirements.txt',
-    'openfl-workspace_torch_cnn_mnist': 'openfl-workspace/torch_cnn_mnist/requirements.txt',
-    'openfl-workspace_torch_unet_kvasir': 'openfl-workspace/torch_unet_kvasir/requirements.txt',
-    'openfl-workspace_tf_cnn_histology': 'openfl-workspace/tf_cnn_histology/requirements.txt',
-    'openfl-workspace_tf_3dunet_brats': 'openfl-workspace/tf_3dunet_brats/requirements.txt',
-    'openfl-workspace_keras_cnn_with_compression': 'openfl-workspace/keras_cnn_with_compression/requirements.txt',
-    'openfl-workspace_keras_cnn_mnist': 'openfl-workspace/keras_cnn_mnist/requirements.txt',
+    // 'openfl-workspace_keras/histology': 'openfl-workspace/torch/histology/requirements.txt',
+    'openfl-workspace_keras/histology_src': 'openfl-workspace/torch/histology/src/requirements.txt',
+    'openfl-workspace_keras/nlp': 'openfl-workspace/keras/nlp/requirements.txt',
+    'openfl-workspace_torch_cnn_mnist': 'openfl-workspace/torch/mnist/requirements.txt',
+    'openfl-workspace_torch_unet_kvasir': 'openfl-workspace/torch/unet_kvasir/requirements.txt',
+    'openfl-workspace_keras_cnn_mnist': 'openfl-workspace/keras/mnist/requirements.txt',
     'openfl-tutorials_interactive_api_pytorch_medmnist_2d_envoy': 'openfl-tutorials/interactive_api/PyTorch_MedMNIST_2D/envoy/requirements.txt',
     'openfl-tutorials_interactive_api_pytorch_dogscats_vit_workspace': 'openfl-tutorials/interactive_api/PyTorch_DogsCats_ViT/workspace/requirements.txt',
     'openfl-tutorials_interactive_api_pytorch_histology_envoy': 'openfl-tutorials/interactive_api/PyTorch_Histology/envoy/requirements.txt',

@@ -26,7 +26,7 @@ Example Using the Task Runner API (Aggregator-based Workflow)
 
 The following steps can be leveraged to achieve practical e2e usage of FedEval
 
-*N.B*: We will be using torch_cnn_mnist plan itself for both training and with some minor changes for evaluation as well
+*N.B*: We will be using torch/mnist plan itself for both training and with some minor changes for evaluation as well
 
 *Prerequisites*: Please ensure that OpenFL version==1.7 is installed or you can also choose to install latest from source.
 
@@ -48,13 +48,13 @@ With OpenFL version==1.7 aggregator start command is enhanced to have an optiona
     --help                      Show this message and exit.
 
 1. **Setup**
-We will use the `torch_cnn_mnist` workspace for training
+We will use the `torch/mnist` workspace for training
 
 Let's first configure a workspace with all necesary certificates
 
 .. code-block:: shell
 
-    fx workspace create --prefix ./cnn_train_eval --template torch_cnn_mnist
+    fx workspace create --prefix ./cnn_train_eval --template torch/mnist
     cd cnn_train_eval
     fx workspace certify
     fx aggregator generate-cert-request
@@ -416,7 +416,7 @@ The updated plan post initialization with edits to make it ready for evaluation
         metrics:
         - loss
 
-We have done following changes to the initialized torch_cnn_mnist plan in the new workspace:
+We have done following changes to the initialized torch/mnist plan in the new workspace:
  - Set the rounds_to_train to 1 as evaluation needs just one round of federation run across the collaborators
  - Removed all other training related tasks from assigner settings except "aggregated_model_validation"
 Now let's replace the ``init.pbuf`` with the previously saved ``trained_model.pbuf``

@@ -88,7 +88,7 @@ Each YAML top-level section contains the following subsections:
 
 The following is an example of a **plan.yaml**:
 
-.. literalinclude:: ../../../openfl-workspace/torch_cnn_mnist/plan/plan.yaml
+.. literalinclude:: ../../../openfl-workspace/torch/mnist/plan/plan.yaml
   :language: yaml
 
 
@@ -150,22 +150,22 @@ STEP 1: Create a Workspace
 		$ fx
 
 
-2. 	This example uses the :code:`keras_cnn_mnist` template.
+2. 	This example uses the :code:`keras/mnist` template.
 
-	Set the environment variables to use the :code:`keras_cnn_mnist` as the template and :code:`${HOME}/my_federation` as the path to the workspace directory.
+	Set the environment variables to use the :code:`keras/mnist` as the template and :code:`${HOME}/my_federation` as the path to the workspace directory.
 
     .. code-block:: shell
 
-        $ export WORKSPACE_TEMPLATE=keras_cnn_mnist
+        $ export WORKSPACE_TEMPLATE=keras/mnist
         $ export WORKSPACE_PATH=${HOME}/my_federation
 
 3.	Decide a workspace template, which are end-to-end federated learning training demonstrations. The following is a sample of available templates:
 
- - :code:`keras_cnn_mnist`: a workspace with a simple `Keras <http://keras.io/>`__ CNN model that will download the `MNIST <http://yann.lecun.com/exdb/mnist/>`_ dataset and train in a federation.
+ - :code:`keras/mnist`: a workspace with a simple `Keras <http://keras.io/>`__ CNN model that will download the `MNIST <http://yann.lecun.com/exdb/mnist/>`_ dataset and train in a federation.
  - :code:`tf_2dunet`: a workspace with a simple `TensorFlow <http://tensorflow.org>`__ CNN model that will use the `BraTS <https://www.med.upenn.edu/sbia/brats2017/data.html>`_ dataset and train in a federation.
  - :code:`tf_cnn_histology`: a workspace with a simple `TensorFlow <http://tensorflow.org>`__ CNN model that will download the `Colorectal Histology <https://zenodo.org/record/53169#.XGZemKwzbmG>`_ dataset and train in a federation.
- - :code:`torch_cnn_histology`: a workspace with a simple `PyTorch <http://pytorch.org/>`__ CNN model that will download the `Colorectal Histology <https://zenodo.org/record/53169#.XGZemKwzbmG>`_ dataset and train in a federation.
- - :code:`torch_cnn_mnist`: a workspace with a simple `PyTorch <http://pytorch.org>`__ CNN model that will download the `MNIST <http://yann.lecun.com/exdb/mnist/>`_ dataset and train in a federation.
+ - :code:`keras/histology`: a workspace with a simple `PyTorch <http://pytorch.org/>`__ CNN model that will download the `Colorectal Histology <https://zenodo.org/record/53169#.XGZemKwzbmG>`_ dataset and train in a federation.
+ - :code:`torch/mnist`: a workspace with a simple `PyTorch <http://pytorch.org>`__ CNN model that will download the `MNIST <http://yann.lecun.com/exdb/mnist/>`_ dataset and train in a federation.
 
   See the complete list of available templates.
 

@@ -83,7 +83,7 @@ For logging through Tensorboard, enable the parameter :code:`write_logs : true`
     settings :
         write_logs : true
 
-Follow the steps below to write your custom callback function instead. As an example, a full implementation can be found at `Federated_Pytorch_MNIST_Tutorial.ipynb <https://github.com/intel/openfl/blob/develop/openfl-tutorials/Federated_Pytorch_MNIST_Tutorial.ipynb>`_ and in the **torch_cnn_mnist** workspace.
+Follow the steps below to write your custom callback function instead. As an example, a full implementation can be found at `Federated_Pytorch_MNIST_Tutorial.ipynb <https://github.com/intel/openfl/blob/develop/openfl-tutorials/Federated_Pytorch_MNIST_Tutorial.ipynb>`_ and in the **torch/mnist** workspace.
 
 1. Define the callback function, like how you defined in Python API, in the **src** directory in your workspace.
 
@@ -95,9 +95,9 @@ Follow the steps below to write your custom callback function instead. As an exa
     defaults : plan/defaults/aggregator.yaml
     template : openfl.component.Aggregator
     settings :
-      init_state_path     : save/torch_cnn_mnist_init.pbuf
-      best_state_path     : save/torch_cnn_mnist_best.pbuf
-      last_state_path     : save/torch_cnn_mnist_last.pbuf
+      init_state_path     : save/torch/mnist_init.pbuf
+      best_state_path     : save/torch/mnist_best.pbuf
+      last_state_path     : save/torch/mnist_last.pbuf
       rounds_to_train     : 10
       write_logs          : true
       log_metric_callback :

@@ -29,7 +29,7 @@ The following are the straggler handling algorithms supported in OpenFL:
 Demonstration of adding the straggler handling interface
 =========================================================
 
-The example template, **torch_cnn_mnist_straggler_check**, uses the ``PercentageBasedStragglerHandling``. To gain a better understanding of how experiments perform, you can modify the **percent_collaborators_needed** or **minimum_reporting** parameter in the template **plan.yaml** or even choose **CutoffTimeBasedStragglerHandling** function instead:
+The example template, **torch/mnist_straggler_check**, uses the ``PercentageBasedStragglerHandling``. To gain a better understanding of how experiments perform, you can modify the **percent_collaborators_needed** or **minimum_reporting** parameter in the template **plan.yaml** or even choose **CutoffTimeBasedStragglerHandling** function instead:
 
     .. code-block:: yaml