Finished exporting data.yaml, plan.yaml.

Workspace creator validated for 101 MNIST and 301 Watermarking with MNIST Signed-off-by: Parth Mandaliya <parthx.mandaliya@intel.com>
ParthMandaliya · Sep 28, 2023 · 3c4fd3a · 3c4fd3a
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diff --git a/openfl-tutorials/experimental/Workflow_Interface_101_MNIST-Test-Approach1.ipynb b/openfl-tutorials/experimental/Workflow_Interface_101_MNIST-Test-Approach1.ipynb
diff --git a/openfl-tutorials/experimental/Workflow_Interface_101_MNIST-Test-Approach2 copy.ipynb b/openfl-tutorials/experimental/Workflow_Interface_101_MNIST-Test-Approach2 copy.ipynb
diff --git a/openfl-tutorials/experimental/Workflow_Interface_101_MNIST-Test-Approach2.ipynb b/openfl-tutorials/experimental/Workflow_Interface_101_MNIST-Test-Approach2.ipynb
diff --git a/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb b/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb
@@ -42,6 +42,25 @@
     "The workflow interface is a new way of composing federated learning expermients with OpenFL. It was borne through conversations with researchers and existing users who had novel use cases that didn't quite fit the standard horizontal federated learning paradigm. "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9819a58c",
+   "metadata": {},
+   "source": [
+    "Before we start the experiment, let's write some `nbdev` tags which enable us to convert this notebook to aggregator-based-workspace workflow.\n",
+    "Tag below specifies default python script name which will be created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e4d1637",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| default_exp experiment"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -67,6 +86,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#| export\n",
+    "\n",
     "!pip install git+https://github.com/intel/openfl.git\n",
     "!pip install -r requirements_workflow_interface.txt\n",
     "!pip install torch\n",
@@ -94,6 +115,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#| export\n",
+    "\n",
     "import torch.nn as nn\n",
     "import torch.nn.functional as F\n",
     "import torch.optim as optim\n",
@@ -153,7 +176,7 @@
     "        x = F.dropout(x, training=self.training)\n",
     "        x = self.fc2(x)\n",
     "        return F.log_softmax(x)\n",
-    "    \n",
+    "\n",
     "def inference(network,test_loader):\n",
     "    network.eval()\n",
     "    test_loss = 0\n",
@@ -192,13 +215,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#| export\n",
+    "\n",
     "from copy import deepcopy\n",
     "\n",
     "from openfl.experimental.interface import FLSpec, Aggregator, Collaborator\n",
     "from openfl.experimental.runtime import LocalRuntime\n",
     "from openfl.experimental.placement import aggregator, collaborator\n",
     "\n",
-    "\n",
     "def FedAvg(models):\n",
     "    new_model = models[0]\n",
     "    state_dicts = [model.state_dict() for model in models]\n",
@@ -234,6 +258,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#| export\n",
+    "\n",
     "class FederatedFlow(FLSpec):\n",
     "\n",
     "    def __init__(self, model=None, optimizer=None, rounds=3, **kwargs):\n",
@@ -333,10 +359,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Aggregator\n",
+    "#| export\n",
+    "\n",
     "aggregator_ = Aggregator()\n",
     "\n",
     "collaborator_names = [\"Portland\", \"Seattle\", \"Chandler\", \"Bangalore\"]\n",
+    "n_collaborators = len(collaborator_names)\n",
     "\n",
     "def callable_to_initialize_collaborator_private_attributes(index, n_collaborators, batch_size, train_dataset, test_dataset):\n",
     "    train = deepcopy(train_dataset)\n",
@@ -351,20 +379,18 @@
     "        \"test_loader\": torch.utils.data.DataLoader(test, batch_size=batch_size, shuffle=True),\n",
     "    }\n",
     "\n",
-    "# Setup collaborators private attributes via callable function\n",
     "collaborators = []\n",
     "for idx, collaborator_name in enumerate(collaborator_names):\n",
     "    collaborators.append(\n",
     "        Collaborator(\n",
     "            name=collaborator_name, num_cpus=0, num_gpus=0,\n",
     "            private_attributes_callable=callable_to_initialize_collaborator_private_attributes,\n",
-    "            index=idx, n_collaborators=len(collaborator_names),\n",
+    "            index=idx, n_collaborators=n_collaborators,\n",
     "            train_dataset=mnist_train, test_dataset=mnist_test, batch_size=64\n",
     "        )\n",
     "    )\n",
     "\n",
-    "local_runtime = LocalRuntime(aggregator=aggregator_, collaborators=collaborators,\n",
-    "                             backend=\"ray\")\n",
+    "local_runtime = LocalRuntime(aggregator=aggregator_, collaborators=collaborators, backend=\"ray\")\n",
     "print(f'Local runtime collaborators = {local_runtime.collaborators}')"
    ]
   },
@@ -384,6 +410,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#| export\n",
+    "\n",
     "model = None\n",
     "best_model = None\n",
     "optimizer = None\n",
@@ -392,6 +420,26 @@
     "flflow.run()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46d1259b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from openfl.experimental.workspace_builder import WorkspaceBuilder\n",
+    "\n",
+    "notebook_path = \"./Workflow_Interface_101_MNIST-Test-Approach2 copy.ipynb\"\n",
+    "template_workspace_path = f\"/home/{os.environ['USER']}/env-workspace-creator/openfl/openfl-workspace/experimental/template_workspace/\"\n",
+    "\n",
+    "workspace_builder = WorkspaceBuilder(notebook_path, \"experiment\", f\"/home/{os.environ['USER']}\", template_workspace_path)\n",
+    "\n",
+    "workspace_builder.generate_requirements()\n",
+    "workspace_builder.generate_plan_yaml()\n",
+    "workspace_builder.generate_data_yaml()"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -702,7 +750,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.17"
+   "version": "3.8.18"
   }
  },
  "nbformat": 4,

diff --git a/openfl-tutorials/experimental/Workflow_Interface_301_MNIST_Watermarking.ipynb b/openfl-tutorials/experimental/Workflow_Interface_301_MNIST_Watermarking.ipynb
@@ -46,17 +46,30 @@
     "First we start by installing the necessary dependencies for the workflow interface"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d79eacbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| default_exp experiment"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "f7475cba",
    "metadata": {},
    "outputs": [],
    "source": [
+    "#| export\n",
+    "\n",
     "!pip install git+https://github.com/intel/openfl.git\n",
     "!pip install -r requirements_workflow_interface.txt\n",
     "!pip install matplotlib\n",
-    "!pip install torch torchvision\n",
+    "!pip install torch\n",
+    "!pip install torchvision\n",
     "!pip install git+https://github.com/pyviz-topics/imagen.git@master\n",
     "\n",
     "\n",
@@ -82,6 +95,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# | export\n",
+    "\n",
     "import torch.nn as nn\n",
     "import torch.nn.functional as F\n",
     "import torch.optim as optim\n",
@@ -173,6 +188,7 @@
     "def train_model(model, optimizer, data_loader, entity, round_number, log=False):\n",
     "    # Helper function to train the model\n",
     "    train_loss = 0\n",
+    "    log_interval = 20\n",
     "    model.train()\n",
     "    for batch_idx, (X, y) in enumerate(data_loader):\n",
     "        optimizer.zero_grad()\n",
@@ -185,8 +201,7 @@
     "\n",
     "        train_loss += loss.item() * len(X)\n",
     "        if batch_idx % log_interval == 0 and log:\n",
-    "            print(\n",
-    "                \"{:<20} Train Epoch: {:<3} [{:<3}/{:<4} ({:<.0f}%)] Loss: {:<.6f}\".format(\n",
+    "            print(\"{:<20} Train Epoch: {:<3} [{:<3}/{:<4} ({:<.0f}%)] Loss: {:<.6f}\".format(\n",
     "                    entity,\n",
     "                    round_number,\n",
     "                    batch_idx * len(X),\n",
@@ -217,6 +232,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#| export\n",
+    "\n",
     "watermark_dir = \"./files/watermark-dataset/MWAFFLE/\"\n",
     "\n",
     "\n",
@@ -401,6 +418,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#| export\n",
+    "\n",
     "from copy import deepcopy\n",
     "\n",
     "from openfl.experimental.interface import FLSpec, Aggregator, Collaborator\n",
@@ -448,6 +467,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#| export\n",
+    "\n",
     "class FederatedFlow_MNIST_Watermarking(FLSpec):\n",
     "    \"\"\"\n",
     "    This Flow demonstrates Watermarking on a Deep Learning Model in Federated Learning\n",
@@ -576,7 +597,7 @@
     "            self.optimizer,\n",
     "            self.train_loader,\n",
     "            \"<Collab: {:<20}\".format(self.input + \">\"),\n",
-    "            self.round_number,\n",
+    "            self.round_number if self.round_number is not None else 0,\n",
     "            log=True,\n",
     "        )\n",
     "\n",
@@ -696,6 +717,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#| export\n",
+    "\n",
     "# Set random seed\n",
     "random_seed = 42\n",
     "torch.manual_seed(random_seed)\n",
@@ -740,6 +763,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#| export\n",
+    "\n",
     "def callable_to_initialize_aggregator_private_attributes(watermark_data, batch_size):\n",
     "    return {\n",
     "        \"watermark_data_loader\": torch.utils.data.DataLoader(\n",
@@ -765,6 +790,7 @@
     "    \"Bangalore\",\n",
     "    \"New Delhi\",\n",
     "]\n",
+    "n_collaborators = len(collaborator_names)\n",
     "\n",
     "def callable_to_initialize_collaborator_private_attributes(index, n_collaborators, batch_size, train_dataset, test_dataset):\n",
     "    train = deepcopy(train_dataset)\n",
@@ -786,7 +812,7 @@
     "        Collaborator(\n",
     "            name=collaborator_name, num_cpus=0, num_gpus=0,\n",
     "            private_attributes_callable=callable_to_initialize_collaborator_private_attributes,\n",
-    "            index=idx, n_collaborators=len(collaborator_names),\n",
+    "            index=idx, n_collaborators=n_collaborators,\n",
     "            train_dataset=mnist_train, test_dataset=mnist_test, batch_size=64\n",
     "        )\n",
     "    )\n",
@@ -811,6 +837,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "#| export\n",
+    "\n",
     "model = None\n",
     "best_model = None\n",
     "optimizer = None\n",
@@ -833,15 +861,36 @@
     "    print(f\"Starting round {i}...\")\n",
     "    flflow.run()\n",
     "    flflow.round_number += 1\n",
-    "    aggregated_model_accuracy = flflow.aggregated_model_accuracy\n",
-    "    if aggregated_model_accuracy > top_model_accuracy:\n",
-    "        print(\n",
-    "            f\"\\nAccuracy improved to {aggregated_model_accuracy} for round {i}, Watermark Acc: {flflow.watermark_retrain_validation_score}\\n\"\n",
-    "        )\n",
-    "        top_model_accuracy = aggregated_model_accuracy\n",
-    "        best_model = flflow.model\n",
+    "    if hasattr(flflow, \"aggregated_model_accuracy\"):\n",
+    "        aggregated_model_accuracy = flflow.aggregated_model_accuracy\n",
+    "        if aggregated_model_accuracy > top_model_accuracy:\n",
+    "            print(\n",
+    "                f\"\\nAccuracy improved to {aggregated_model_accuracy} for round {i}, Watermark Acc: {flflow.watermark_retrain_validation_score}\\n\"\n",
+    "            )\n",
+    "            top_model_accuracy = aggregated_model_accuracy\n",
+    "            best_model = flflow.model\n",
+    "\n",
+    "        torch.save(best_model.state_dict(), \"watermarked_mnist_model.pth\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21c98aae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from openfl.experimental.workspace_builder import WorkspaceBuilder\n",
+    "\n",
+    "notebook_path = \"./Workflow_Interface_301_MNIST_Watermarking.ipynb\"\n",
+    "template_workspace_path = f\"/home/{os.environ['USER']}/env-workspace-creator/openfl/openfl-workspace/experimental/template_workspace/\"\n",
+    "\n",
+    "workspace_builder = WorkspaceBuilder(notebook_path, \"experiment\", f\"/home/{os.environ['USER']}\", template_workspace_path)\n",
     "\n",
-    "torch.save(best_model.state_dict(), \"watermarked_mnist_model.pth\")"
+    "workspace_builder.generate_requirements()\n",
+    "workspace_builder.generate_plan_yaml()\n",
+    "workspace_builder.generate_data_yaml()"
    ]
   },
   {
@@ -882,7 +931,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.8.18"
   },
   "vscode": {
    "interpreter": {

diff --git a/openfl-workspace/experimental/template_workspace/plan/plan.yaml b/openfl-workspace/experimental/template_workspace/plan/plan.yaml
@@ -1,22 +1,20 @@
 # Copyright (C) 2020-2021 Intel Corporation
 # Licensed subject to the terms of the separately executed evaluation license agreement between Intel Corporation and you.
 
-aggregator :
-  defaults : plan/defaults/aggregator.yaml
-  template : openfl.experimental.component.Aggregator
-  settings : {}
-
-
-collaborator :
-  defaults : plan/defaults/collaborator.yaml
-  template : openfl.experimental.component.Collaborator
-  settings : {}
+aggregator:
+  defaults: plan/defaults/aggregator.yaml
+  template: openfl.experimental.component.Aggregator
+  settings:
+    rounds_to_train: 1
 
+collaborator:
+  defaults: plan/defaults/collaborator.yaml
+  template: openfl.experimental.component.Collaborator
+  settings: {}
 
 federated_flow:
   template: src.flow.TinyImageNetFlow
   settings: {}
 
-
-network :
-  defaults : plan/defaults/network.yaml
+network:
+  defaults: plan/defaults/network.yaml