From 8b17d7ae1018582b58c0d22850cb5d5a8a90eba6 Mon Sep 17 00:00:00 2001
From: Parth Mandaliya <parthx.mandaliya@intel.com>
Date: Fri, 5 May 2023 06:01:38 -0700
Subject: [PATCH] Modified and Added tutorial
 Workflow-Interface_201_Exclusive_GPUs_with_Ray.ipynb

Signed-off-by: Parth Mandaliya <parthx.mandaliya@intel.com>
---
 .../experimental/Privacy_Meter/cifar10_PM.py  | 12 +++--
 .../Workflow_Interface_101_MNIST.ipynb        | 48 ++++++++++++++++---
 ..._Interface_102_Aggregator_Validation.ipynb | 13 ++++-
 ...c_Institutional_Incremental_Learning.ipynb | 18 ++++++-
 ...nterface_201_Exclusive_GPUs_with_Ray.ipynb | 36 ++++++++++----
 ...low_Interface_301_MNIST_Watermarking.ipynb | 41 ++++++++--------
 ...ce_401_FedProx_with_Synthetic_nonIID.ipynb | 21 ++++----
 7 files changed, 132 insertions(+), 57 deletions(-)

diff --git a/openfl-tutorials/experimental/Privacy_Meter/cifar10_PM.py b/openfl-tutorials/experimental/Privacy_Meter/cifar10_PM.py
index 050f8cf79fa..cc400182009 100644
--- a/openfl-tutorials/experimental/Privacy_Meter/cifar10_PM.py
+++ b/openfl-tutorials/experimental/Privacy_Meter/cifar10_PM.py
@@ -712,6 +712,8 @@ def end(self):
     )
 
     # Split train, test, and population dataset among collaborators
+    # this function will be called before executing collaborator steps
+    # which will return private attributes dictionary for each collaborator
     def callable_to_initialize_collaborator_private_attributes(
             index, n_collaborators, train_ds, test_ds, population_ds, args):
         # construct the training and test and population dataset
@@ -778,17 +780,17 @@ def callable_to_initialize_collaborator_private_attributes(
             Collaborator(
                 name=collab_name,
                 private_attributes_callable=callable_to_initialize_collaborator_private_attributes,
-                # Set `num_gpus=0.5` to `num_gpus=0.0` to run on CPU
-                num_cpus=0.0, num_gpus=0.5,  # Assuming GPU is available in the machine
+                # If 1 GPU is available in the machine
+                # Set `num_gpus=0.0` to `num_gpus=0.5` to run on GPU with ray backend with 2 collaborators
+                num_cpus=0.0, num_gpus=0.0,
                 index=idx, n_collaborators=len(collaborator_names),
                 train_ds=train_dataset, test_ds=test_dataset,
                 population_ds=population_dataset, args=args
             )
         )
 
-    # To activate the ray backend with parallel collaborator tasks run in their own process
-    # and exclusive GPUs assigned to tasks, set LocalRuntime with backend='ray':
-    local_runtime = LocalRuntime(aggregator=aggregator, collaborators=collaborators)
+    # Set backend='ray' to use ray-backend
+    local_runtime = LocalRuntime(aggregator=aggregator, collaborators=collaborators, backend="single_process")
 
     print(f"Local runtime collaborators = {local_runtime.collaborators}")
 
diff --git a/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb b/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb
index 6d7c9b752d8..511fd840c64 100644
--- a/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb
+++ b/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "14821d97",
    "metadata": {},
@@ -10,6 +11,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "bd059520",
    "metadata": {},
@@ -23,6 +25,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "39c3d86a",
    "metadata": {},
@@ -31,6 +34,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "a7989e72",
    "metadata": {},
@@ -39,6 +43,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "fc8e35da",
    "metadata": {},
@@ -47,6 +52,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "4dbb89b6",
    "metadata": {},
@@ -72,6 +78,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "7237eac4",
    "metadata": {},
@@ -165,6 +172,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "cd268911",
    "metadata": {},
@@ -312,9 +320,9 @@
    "id": "2aabf61e",
    "metadata": {},
    "source": [
-    "You'll notice in the `FederatedFlow` definition above that there were certain attributes that the flow was not initialized with, namely the `train_loader` and `test_loader` for each of the collaborators. These are **private_attributes** that are exposed only through the runtime. Each participant has it's own set of private attributes: a dictionary where the key is the attribute name, and the value is the object that will be made accessible through that participant's task. \n",
+    "Note that the private attributes are flexible, and you can choose to pass in a completely different type of object to any of the collaborators or aggregator (with an arbitrary name). These private attributes will always be filtered out of the current state when transferring from collaborator to aggregator, or vice versa.  \n",
     "\n",
-    "Below, we segment shards of the MNIST dataset for **four collaborators**: Portland, Seattle, Chandler, and Portland. Each has their own slice of the dataset that is accessible through the `train_loader` and `test_loader` attributes, which are set using the `callable_to_initialize_collaborator_private_attributes` callable function. Note that the private attributes are flexible, and you can choose to pass in a completely different type of object to any of the collaborators or aggregator (with an arbitrary name). These private attributes will always be filtered out of the current state when transfering from collaborator to aggregator, or vice versa.  "
+    "Private attributes can be set using callback function while instantiating the participant. Parameters required by the callback function are specified as arguments while instantiating the participant. In this example callback function, `callable_to_initialize_collaborator_private_attributes`, returns the private attributes `train_loader` and `test_loader` of the collaborator. Parameters required by the callback function `index`, `n_collaborators`, `batch_size`, `train_dataset`, `test_dataset` are passed appropriate values with the same names in the Collaborator constructor."
    ]
   },
   {
@@ -356,8 +364,25 @@
     "\n",
     "local_runtime = LocalRuntime(aggregator=aggregator_, collaborators=collaborators,\n",
     "                             backend=\"ray\")\n",
-    "print(f'Local runtime collaborators = {local_runtime.collaborators}')\n",
-    "\n",
+    "print(f'Local runtime collaborators = {local_runtime.collaborators}')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "278ad46b",
+   "metadata": {},
+   "source": [
+    "Now that we have our flow and runtime defined, let's run the experiment! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a175b4d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "model = None\n",
     "best_model = None\n",
     "optimizer = None\n",
@@ -367,11 +392,12 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "id": "278ad46b",
+   "id": "86b3dd2e",
    "metadata": {},
    "source": [
-    "Now that we have our flow and runtime defined, let's run the experiment! "
+    "Now that the flow has completed, let's get the final model and accuracy"
    ]
   },
   {
@@ -387,6 +413,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "5dd1558c",
    "metadata": {},
@@ -409,6 +436,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "a61a876d",
    "metadata": {},
@@ -458,6 +486,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "b55ccb19",
    "metadata": {},
@@ -486,6 +515,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e5efa1ff",
    "metadata": {},
@@ -504,6 +534,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "3292b2e0",
    "metadata": {},
@@ -542,6 +573,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "eb1866b7",
    "metadata": {},
@@ -570,6 +602,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "ef877a50",
    "metadata": {},
@@ -598,6 +631,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "9826c45f",
    "metadata": {},
@@ -616,6 +650,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "dd962ddc",
    "metadata": {},
@@ -634,6 +669,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "426f2395",
    "metadata": {},
diff --git a/openfl-tutorials/experimental/Workflow_Interface_102_Aggregator_Validation.ipynb b/openfl-tutorials/experimental/Workflow_Interface_102_Aggregator_Validation.ipynb
index 0392e47c069..c51a1937272 100644
--- a/openfl-tutorials/experimental/Workflow_Interface_102_Aggregator_Validation.ipynb
+++ b/openfl-tutorials/experimental/Workflow_Interface_102_Aggregator_Validation.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "14821d97",
    "metadata": {},
@@ -10,6 +11,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "bd059520",
    "metadata": {},
@@ -18,6 +20,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "fc8e35da",
    "metadata": {},
@@ -26,6 +29,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "4dbb89b6",
    "metadata": {},
@@ -50,6 +54,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "7237eac4",
    "metadata": {},
@@ -133,6 +138,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "cd268911",
    "metadata": {},
@@ -169,6 +175,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "b2e45614",
    "metadata": {
@@ -284,7 +291,9 @@
    "source": [
     "You'll notice in the `FederatedFlow` definition above that there were certain attributes that the flow was not initialized with, namely the `train_loader` and `test_loader` for each of the collaborators. These are **private_attributes** that are exposed only throught he runtime. Each participant has it's own set of private attributes: a dictionary where the key is the attribute name, and the value is the object that will be made accessible through that participant's task. \n",
     "\n",
-    "Below, we segment shards of the MNIST dataset for **four collaborators**: Portland, Seattle, Chandler, and Portland. Each has their own slice of the dataset that is accessible through the `train_loader` and `test_loader` attributes, which are set using the `callable_to_initialize_collaborator_private_attributes` callable function. Note that the private attributes are flexible, and you can choose to pass in a completely different type of object to any of the collaborators or aggregator (with an arbitrary name). These private attributes will always be filtered out of the current state when transfering from collaborator to aggregator, or vice versa.\n"
+    "Below, we segment shards of the MNIST dataset for **four collaborators**: `Portland`, `Seattle`, `Chandler`, and `Portland`. Each has their own slice of the dataset that is accessible through the `train_loader` and `test_loader` attributes, which are set using the `callable_to_initialize_collaborator_private_attributes` callable function. Note that the private attributes are flexible, and you can choose to pass in a completely different type of object to any of the collaborators or aggregator (with an arbitrary name). These private attributes will always be filtered out of the current state when transfering from collaborator to aggregator, or vice versa.\n",
+    "\n",
+    "Private attributes can be set using callback function while instantiating the participant. Parameters required by the callback function are specified as arguments while instantiating the participant. In this example callback function, `callable_to_initialize_collaborator_private_attributes`, returns the private attributes `train_loader` and `test_loader` of the collaborator. Callback function, `callable_to_initialize_aggregator_private_attributes`, returns the private attribute `test_loader` of the Aggregator."
    ]
   },
   {
@@ -342,6 +351,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "0525eaa9",
    "metadata": {},
@@ -365,6 +375,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "8b9f8d25",
    "metadata": {},
diff --git a/openfl-tutorials/experimental/Workflow_Interface_103_Cyclic_Institutional_Incremental_Learning.ipynb b/openfl-tutorials/experimental/Workflow_Interface_103_Cyclic_Institutional_Incremental_Learning.ipynb
index 1780afac90b..e69e7cea778 100644
--- a/openfl-tutorials/experimental/Workflow_Interface_103_Cyclic_Institutional_Incremental_Learning.ipynb
+++ b/openfl-tutorials/experimental/Workflow_Interface_103_Cyclic_Institutional_Incremental_Learning.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "14821d97",
    "metadata": {},
@@ -28,6 +29,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "fc8e35da",
    "metadata": {},
@@ -36,6 +38,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "4dbb89b6",
    "metadata": {},
@@ -60,6 +63,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "7237eac4",
    "metadata": {},
@@ -143,6 +147,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "cd268911",
    "metadata": {},
@@ -179,6 +184,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "b2e45614",
    "metadata": {
@@ -303,7 +309,9 @@
    "source": [
     "You'll notice in the `CyclicFlow` definition above that each collaborator performs **aggregated_model_validation**, **training**, and **local_model_validation** before passing it's model on to the next collaborator (through the aggregator). \n",
     "\n",
-    "Below, we segment shards of the MNIST dataset for **four collaborators**: Portland, Seattle, Chandler, and Portland **equally and IID**. Each has their own slice of the dataset that is accessible through the `train_loader` and `test_loader` attributes, which are set using the `callable_to_initialize_collaborator_private_attributes` callable function. Note that the private attributes are flexible, and you can choose to pass in a completely different type of object to any of the collaborators or aggregator (with an arbitrary name). These private attributes will always be filtered out of the current state when transfering from collaborator to aggregator, or vice versa.  "
+    "Below, we segment shards of the MNIST dataset for **four collaborators**: Portland, Seattle, Chandler, and Portland **equally and IID**. Each has their own slice of the dataset that is accessible through the `train_loader` and `test_loader` attributes, which are set using the `callable_to_initialize_collaborator_private_attributes` callable function. Note that the private attributes are flexible, and you can choose to pass in a completely different type of object to any of the collaborators or aggregator (with an arbitrary name). These private attributes will always be filtered out of the current state when transfering from collaborator to aggregator, or vice versa.\n",
+    "\n",
+    "Private attributes can be set using callback function while instantiating the participant. Parameters required by the callback function are specified as arguments while instantiating the participant. In this example callback function, `callable_to_initialize_collaborator_private_attributes`, returns the private attributes `train_loader` and `test_loader` of the collaborator. Callback function, `callable_to_initialize_aggregator_private_attributes`, returns the private attribute `test_loader` of the Aggregator."
    ]
   },
   {
@@ -315,7 +323,8 @@
    "source": [
     "collaborator_names = ['Portland', 'Seattle','Chandler','Bangalore']\n",
     "\n",
-    "def callable_to_initialize_aggregator_private_attributes(n_collaborators, test_dataset, batch_size):\n",
+    "def callable_to_initialize_aggregator_private_attributes(n_collaborators, test_dataset,\n",
+    "        batch_size):\n",
     "    aggregator_test = deepcopy(test_dataset)\n",
     "    aggregator_test.targets = test_dataset.targets[n_collaborators::n_collaborators+1]\n",
     "    aggregator_test.data = test_dataset.data[n_collaborators::n_collaborators+1]\n",
@@ -363,6 +372,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "0525eaa9",
    "metadata": {},
@@ -386,6 +396,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "ad93c508",
    "metadata": {},
@@ -500,6 +511,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e58fe9cd",
    "metadata": {},
@@ -619,6 +631,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "285d63a9",
    "metadata": {},
@@ -627,6 +640,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "8b9f8d25",
    "metadata": {},
diff --git a/openfl-tutorials/experimental/Workflow_Interface_201_Exclusive_GPUs_with_Ray.ipynb b/openfl-tutorials/experimental/Workflow_Interface_201_Exclusive_GPUs_with_Ray.ipynb
index 8f1fedfd6ac..821d5c3f61b 100644
--- a/openfl-tutorials/experimental/Workflow_Interface_201_Exclusive_GPUs_with_Ray.ipynb
+++ b/openfl-tutorials/experimental/Workflow_Interface_201_Exclusive_GPUs_with_Ray.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "14821d97",
    "metadata": {},
@@ -10,6 +11,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "bd059520",
    "metadata": {},
@@ -18,6 +20,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "fc8e35da",
    "metadata": {},
@@ -26,6 +29,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "4dbb89b6",
    "metadata": {},
@@ -51,6 +55,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "7237eac4",
    "metadata": {},
@@ -139,6 +144,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "cd268911",
    "metadata": {},
@@ -179,13 +185,14 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "b2e45614",
    "metadata": {
     "scrolled": true
    },
    "source": [
-    "Now we come to the updated flow definition. Here we request `@collaborator(num_gpus=1)` as the placement decorator, which will require a dedicated GPU for each collaborator task. Tune this based on your use case, but because this uses Ray internally, you can also pass through a [fraction of a GPU](https://docs.ray.io/en/latest/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus), which will allow more than one task to run on each GPU (i.e. `@collaborator(num_gpus=0.5)` would result in two tasks per GPU). "
+    "Now we come to the updated flow definition."
    ]
   },
   {
@@ -278,12 +285,12 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "7a133f9f",
+   "id": "49c4afa8",
    "metadata": {},
    "source": [
-    "You'll notice in the `FederatedFlow` definition above that there were certain attributes that the flow was not initialized with, namely the `train_loader` and `test_loader` for each of the collaborators. These are **private_attributes** that are exposed only throught he runtime. Each participant has it's own set of private attributes: a dictionary where the key is the attribute name, and the value is the object that will be made accessible through that participant's task. \n",
+    "In this step we define entities necessary to run the flow and create a function which returns dataset as private attributes of collaborator. As described in [quickstart](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb) we define entities necessary for the flow.\n",
     "\n",
-    "Below, we segment shards of the MNIST dataset for **four collaborators**: Portland, Seattle, Chandler, and Portland. Each has their own slice of the dataset that's accessible via the `train_loader` or `test_loader` attribute. Note that the private attributes are flexible, and you can choose to pass in a completely different type of object to any of the collaborators or aggregator (with an arbitrary name). These private attributes will always be filtered out of the current state when transfering from collaborator to aggregator, or vice versa."
+    "To request GPU(s) with ray-backend, we specify `num_gpus=0.5` as the argument while instantiating Collaborator, this will reserve 0.5 GPU for each of the 2 collaborators and therefore require a dedicated GPU for the exeriment. Tune this based on your use case, for example `num_gpus=0.5` for an experiment with 4 collaborators will require 2 dedicated GPUs. **NOTE:** Collaborator cannot span over multiple GPUs, for example `num_gpus=0.4` with 5 collaborators will require 3 dedicated GPUs. In this case collaborator 1 and 2 use GPU#1, collaborator 3 and 4 use GPU#2, and collaborator 5 uses GPU#3."
    ]
   },
   {
@@ -296,7 +303,7 @@
     "# Setup Aggregator private attributes via callable function\n",
     "aggregator = Aggregator()\n",
     "\n",
-    "collaborator_names = ['Portland', 'Seattle', 'Chandler','Bangalore']\n",
+    "collaborator_names = ['Portland', 'Seattle']\n",
     "\n",
     "def callable_to_initialize_collaborator_private_attributes(index, n_collaborators,\n",
     "        train_dataset, test_dataset, batch_size_train):\n",
@@ -317,7 +324,7 @@
     "for idx, collaborator_name in enumerate(collaborator_names):\n",
     "    collaborators.append(\n",
     "        Collaborator(\n",
-    "            name=collaborator_name, num_cpus=0, num_gpus=0.25,\n",
+    "            name=collaborator_name, num_cpus=0, num_gpus=0.5,\n",
     "            private_attributes_callable=callable_to_initialize_collaborator_private_attributes,\n",
     "            index=idx, n_collaborators=len(collaborator_names),\n",
     "            train_dataset=mnist_train, test_dataset=mnist_test, batch_size_train=batch_size_train\n",
@@ -331,13 +338,14 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "0525eaa9",
    "metadata": {},
    "source": [
     "Now that we have our flow and runtime defined, let's run the experiment! \n",
     "\n",
-    "(If you run this example on Google Colab with the GPU Runtime, you should see one task executing at a time.)"
+    "(If you run this example on Google Colab with the GPU Runtime, you should see four task executing at a time.)"
    ]
   },
   {
@@ -356,6 +364,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "10616d60",
    "metadata": {},
@@ -376,6 +385,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "8e084b41",
    "metadata": {},
@@ -415,6 +425,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "f8f7d05f",
    "metadata": {},
@@ -443,6 +454,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "a206b36c",
    "metadata": {},
@@ -461,6 +473,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "bf4ec317",
    "metadata": {},
@@ -499,11 +512,12 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "0c5522b7",
    "metadata": {},
    "source": [
-    "Now we see **12** steps: **4** collaborators each performed **3** rounds of model training  "
+    "Now we see **6** steps: **2** collaborators each performed **3** rounds of model training  "
    ]
   },
   {
@@ -513,7 +527,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "t = Task(f'CollaboratorGPUFlow/{run_id}/train/9')"
+    "t = Task(f'CollaboratorGPUFlow/{run_id}/train/11')"
    ]
   },
   {
@@ -527,6 +541,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "efd5da76",
    "metadata": {},
@@ -555,6 +570,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "3e92fab0",
    "metadata": {},
@@ -573,6 +589,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "ced6e90e",
    "metadata": {},
@@ -591,6 +608,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "8b9f8d25",
    "metadata": {},
diff --git a/openfl-tutorials/experimental/Workflow_Interface_301_MNIST_Watermarking.ipynb b/openfl-tutorials/experimental/Workflow_Interface_301_MNIST_Watermarking.ipynb
index 63b9ad8e305..e1832ef66a4 100644
--- a/openfl-tutorials/experimental/Workflow_Interface_301_MNIST_Watermarking.ipynb
+++ b/openfl-tutorials/experimental/Workflow_Interface_301_MNIST_Watermarking.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "dc13070c",
    "metadata": {},
@@ -11,6 +12,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "8f28c451",
    "metadata": {},
@@ -27,6 +29,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "a4394089",
    "metadata": {},
@@ -35,6 +38,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "857f9995",
    "metadata": {},
@@ -49,10 +53,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !pip install git+https://github.com/intel/openfl.git\n",
-    "# !pip install -r requirements_workflow_interface.txt\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 torchvision\n",
+    "!pip install torch torchvision\n",
     "!pip install git+https://github.com/pyviz-topics/imagen.git@master\n",
     "\n",
     "\n",
@@ -63,6 +67,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "7bd566df",
    "metadata": {},
@@ -195,6 +200,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "f0c55175",
    "metadata": {},
@@ -375,6 +381,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "d82d34fd",
    "metadata": {},
@@ -712,11 +719,17 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "3d7ce52f",
    "metadata": {},
    "source": [
-    "## Setup Federation"
+    "## Setup Federation\n",
+    "\n",
+    "Private attributes can be set using callback function while instantiating the participant. Parameters required by the callback function are specified as arguments while instantiating the participant. In this example callback function, there are 2 callable function namely `callable_to_initialize_aggregator_private_attributes`, and `callable_to_initialize_collaborator_private_attributes`, returns the private attributes respectively for aggregator and collaborator.\n",
+    "\n",
+    "\n",
+    "Aggregator callable function `callable_to_initialize_aggregator_private_attributes` returns `watermark_data_loader`, `pretrain_epochs`, `retrain_epochs`, `watermark_acc_threshold`, and `watermark_pretraining_completed`. Collaborator callable function `callable_to_initialize_aggregator_private_attributes` returns `train_loader` and `test_loader` of the collaborator."
    ]
   },
   {
@@ -748,7 +761,7 @@
     "collaborator_names = [\n",
     "    \"Portland\",\n",
     "    \"Seattle\",\n",
-    "#    \"Chandler\",\n",
+    "    \"Chandler\",\n",
     "    \"Bangalore\",\n",
     "    \"New Delhi\",\n",
     "]\n",
@@ -783,6 +796,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "02935ccf",
    "metadata": {},
@@ -831,6 +845,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "bf66c1cd",
    "metadata": {},
@@ -849,22 +864,6 @@
     "if flflow._checkpoint:\n",
     "    InspectFlow(flflow, flflow._run_id, show_html=True)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f60118f1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fefef69c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/openfl-tutorials/experimental/Workflow_Interface_401_FedProx_with_Synthetic_nonIID.ipynb b/openfl-tutorials/experimental/Workflow_Interface_401_FedProx_with_Synthetic_nonIID.ipynb
index 9a28c28d47f..be4bf4a19bd 100644
--- a/openfl-tutorials/experimental/Workflow_Interface_401_FedProx_with_Synthetic_nonIID.ipynb
+++ b/openfl-tutorials/experimental/Workflow_Interface_401_FedProx_with_Synthetic_nonIID.ipynb
@@ -39,8 +39,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !pip install git+https://github.com/intel/openfl.git\n",
-    "# !pip install -r https://raw.githubusercontent.com/intel/openfl/develop/openfl-tutorials/experimental/requirements_workflow_interface.txt\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 seaborn\n",
     "!pip install torch torchvision\n",
@@ -334,8 +334,8 @@
   },
   {
    "attachments": {
-    "image.png": {
-     "image/png": 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"
+    "federated-flow-diagram.png": {
+     "image/png": 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"
     }
    },
    "cell_type": "markdown",
@@ -350,10 +350,9 @@
     "-\t`join`: Take weighted average of the model. Performed on Aggregator\n",
     "-\t`end`: End of one round of flow. Flow can be run for *n_epochs* to obtain the desired results\n",
     "\n",
-    "We also import the FedProxOptimizer from openfl.utilities.optimizer \n",
+    "We also import the FedProxOptimizer from openfl.utilities.optimizer\n",
     "\n",
-    "\n",
-    "![image.png](attachment:image.png)"
+    "![federated-flow-diagram.png](attachment:federated-flow-diagram.png)"
    ]
   },
   {
@@ -574,7 +573,7 @@
    "source": [
     "# Setup Federation\n",
     "\n",
-    "In this step we define entities necessary to run the flow and create a function which returns dataset as private attributes of collaborator."
+    "In this step we define entities necessary to run the flow and create a function which returns dataset as private attributes of collaborator. As described in [quickstart](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb) we define entities necessary for the flow."
    ]
   },
   {
@@ -593,12 +592,10 @@
     "\n",
     "synthetic_federated_dataset = SyntheticFederatedDataset(\n",
     "    batch_size=batch_size, num_classes=10, num_collaborators=len(collaborator_names), seed=RANDOM_SEED)\n",
-    "# synthetic_federated_dataset.split(collaborators)\n",
     "\n",
     "def callable_to_initialize_collaborator_private_attributes(index):\n",
     "    return synthetic_federated_dataset.split(index)\n",
     "\n",
-    "# collaborators = [Collaborator(name=name) for name in collaborator_names]\n",
     "collaborators = []\n",
     "for idx, collaborator_name in enumerate(collaborator_names):\n",
     "    collaborators.append(\n",
@@ -669,8 +666,6 @@
     "    # Train, and Test dataset is divided into 9:1 ratio\n",
     "    _, train_y = callable_to_initialize_collaborator_private_attributes(idx)[\"train_loader\"].dataset[:]\n",
     "    _, test_y = callable_to_initialize_collaborator_private_attributes(idx)[\"test_loader\"].dataset[:]\n",
-    "        # _, train_y = collab.private_attributes[\"train_loader\"].dataset[:]\n",
-    "        # _, test_y = collab.private_attributes[\"test_loader\"].dataset[:]\n",
     "    # Append train, and test into 1 tensor array\n",
     "    y = pt.cat((train_y, test_y))\n",
     "    targets = np.argmax(y.numpy(), axis = 1)\n",
@@ -765,7 +760,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(24,6))\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 6))\n",
     "fig.subplots_adjust(hspace=0.4, top=0.8)\n",
     "\n",
     "fedprox_loss = loss_and_acc[\"FedProx\"][\"Train Loss\"]\n",