Update links post nb renames (#1111)

Signed-off-by: Shah, Karan <[email protected]>

README.md

@@ -44,7 +44,7 @@ Setup long-lived components to run many experiments in series. Recommended for F
 Define an experiment and distribute it manually. All participants can verify model code and [FL plan](https://openfl.readthedocs.io/en/latest/about/features_index/taskrunner.html#federated-learning-plan-fl-plan-settings) prior to execution. The federation is terminated when the experiment is finished
 
 - [Workflow Interface](https://openfl.readthedocs.io/en/latest/about/features_index/workflowinterface.html) ([*experimental*](https://openfl.readthedocs.io/en/latest/developer_guide/experimental_features.html)):
-Create complex experiments that extend beyond traditional horizontal federated learning. See the [experimental tutorials](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/) to learn how to coordinate [aggregator validation after collaborator model training](https://github.com/securefederatedai/openfl/tree/develop/openfl-tutorials/experimental/Workflow_Interface_102_Aggregator_Validation.ipynb), [perform global differentially private federated learning](https://github.com/psfoley/openfl/tree/experimental-workflow-interface/openfl-tutorials/experimental/Global_DP), measure the amount of private information embedded in a model after collaborator training with [privacy meter](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Privacy_Meter/readme.md), or [add a watermark to a federated model](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_301_MNIST_Watermarking.ipynb).
+Create complex experiments that extend beyond traditional horizontal federated learning. See the [experimental tutorials](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/) to learn how to coordinate [aggregator validation after collaborator model training](https://github.com/securefederatedai/openfl/tree/develop/openfl-tutorials/experimental/102_Aggregator_Validation.ipynb), [perform global differentially private federated learning](https://github.com/psfoley/openfl/tree/experimental-workflow-interface/openfl-tutorials/experimental/Global_DP), measure the amount of private information embedded in a model after collaborator training with [privacy meter](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Privacy_Meter/readme.md), or [add a watermark to a federated model](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/301_MNIST_Watermarking.ipynb).
 
 The quickest way to test OpenFL is to follow our [tutorials](https://github.com/securefederatedai/openfl/tree/develop/openfl-tutorials). </br>
 Read the [blog post](https://towardsdatascience.com/go-federated-with-openfl-8bc145a5ead1) explaining steps to train a model with OpenFL. </br>

docs/about/releases.md

@@ -8,10 +8,10 @@
      - [**Horovod**](https://github.com/securefederatedai/openfl/tree/develop/openfl-workspace/torch_llm_horovod): Use horovod to efficiently train LLMs across multiple private clusters  
      - **Neuralchat-7b fine-tuning**: Learn how to fine-tune [neuralchat-7b](https://github.com/securefederatedai/openfl/tree/develop/openfl-tutorials/experimental/LLM/neuralchat) using the Intel® Extension for Transformers and the workflow interface. 
 
-- **Workflow API enhancements**: Introducing an experimental [Workspace Export](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_1001_Workspace_Creation_from_JupyterNotebook.ipynb) feature that can be used to transform a Workflow API-based FL experiment into the TaskRunner API format for running in a distributed deployment. There is also groundwork laid for a future FederatedRuntime implementation for Workflow API, in addition to the currently supported LocalRuntime.  
+- **Workflow API enhancements**: Introducing an experimental [Workspace Export](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/1001_Workspace_Creation_from_JupyterNotebook.ipynb) feature that can be used to transform a Workflow API-based FL experiment into the TaskRunner API format for running in a distributed deployment. There is also groundwork laid for a future FederatedRuntime implementation for Workflow API, in addition to the currently supported LocalRuntime.  
 - **Federated Evaluation**: Federated evaluation allows for the assessment of ML models in a federated learning system by validating the model's performance locally on decentralized collaborator nodes, and then aggregating these metrics to gauge overall effectiveness, without compromising data privacy and security. FE is now officially supported by OpenFL, including [example tutorials](https://openfl.readthedocs.io/en/latest/about/features_index/fed_eval.html) on how to use this new feature (via TaskRunner API). 
 
-- **Expanded AI Accelerator Support**:  Intel® Data Center GPU Max Series support via the Intel® Extension for PyTorch, including examples for training on datasets such as [MNIST](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_104_MNIST_XPU.ipynb) (via Workflow API) and [TinyImageNet](https://github.com/securefederatedai/openfl/tree/develop/openfl-tutorials/interactive_api/PyTorch_TinyImageNet_XPU) (via Interactive API) 
+- **Expanded AI Accelerator Support**:  Intel® Data Center GPU Max Series support via the Intel® Extension for PyTorch, including examples for training on datasets such as [MNIST](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/104_MNIST_XPU.ipynb) (via Workflow API) and [TinyImageNet](https://github.com/securefederatedai/openfl/tree/develop/openfl-tutorials/interactive_api/PyTorch_TinyImageNet_XPU) (via Interactive API) 
 
 - **Improved straggler collaborator handling**: Improvements and bug fixes to aggregator’s fault-tolerance when collaborators stop responding or drop out of a federation. Introducing a cut-off timer-based policy and enabling other policies to be plugged-in. This capability is particularly relevant for large or geo-distributed federations. 
 
@@ -49,10 +49,10 @@ We are excited to announce the release of OpenFL 1.5.1 - our first since moving
 ### 1.5 Highlights
 * **New Workflows Interface (Experimental)** - a new way of composing federated learning experiments inspired by [Metaflow](https://github.com/Netflix/metaflow). Enables the creation of custom aggregator and collaborators tasks. This initial release is intended for simulation on a single node (using the LocalRuntime); distributed execution (FederatedRuntime) to be enabled in a future release. 
 * **New use cases enabled by the workflow interface**:
-    * **[End-of-round validation with aggregator dataset](https://github.com/intel/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_102_Aggregator_Validation.ipynb)** 
+    * **[End-of-round validation with aggregator dataset](https://github.com/intel/openfl/blob/develop/openfl-tutorials/experimental/102_Aggregator_Validation.ipynb)** 
     * **[Privacy Meter](https://github.com/intel/openfl/tree/develop/openfl-tutorials/experimental/Privacy_Meter)** - Privacy meter, based on state-of-the-art membership inference attacks, provides a tool to quantitatively audit data privacy in statistical and machine learning algorithms. The objective of a membership inference attack is to determine whether a given data record was in the training dataset of the target model. Measures of success (accuracy, area under the ROC curve, true positive rate at a given false positive rate ...) for particular membership inference attacks against a target model are used to estimate privacy loss for that model (how much information a target model leaks about its training data). Since stronger attacks may be possible, these measures serve as lower bounds of the actual privacy loss. The Privacy Meter workflow example generates privacy loss reports for all party's local model updates as well as the global models throughout all rounds of the FL training. 
     * **[Vertical Federated Learning Examples](https://github.com/intel/openfl/tree/develop/openfl-tutorials/experimental/Vertical_FL)** 
-    * **[Federated Model Watermarking](https://github.com/intel/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_301_MNIST_Watermarking.ipynb)** using the [WAFFLE](https://arxiv.org/pdf/2008.07298.pdf) method 
+    * **[Federated Model Watermarking](https://github.com/intel/openfl/blob/develop/openfl-tutorials/experimental/301_MNIST_Watermarking.ipynb)** using the [WAFFLE](https://arxiv.org/pdf/2008.07298.pdf) method 
     * **[Differential Privacy](https://github.com/intel/openfl/tree/develop/openfl-tutorials/experimental/Global_DP)** – Global differentially private federated learning using Opacus library to achieve a differentially private result w.r.t the inclusion or exclusion of any collaborator in the training process. At each round, a subset of collaborators are selected using a Poisson distribution over all collaborators, the selected collaborators perform local training with periodic clipping of their model delta (with respect to the current global model) to bound their contribution to the average of local model updates. Gaussian noise is then added to the average of these local models at the aggregator. This example is implemented in two different but statistically equivalent ways – the lower level API utilizes RDPAccountant and DPDataloader Opacus objects to perform privacy accounting and collaborator selection respectively, whereas the higher level API uses PrivacyEngine Opacus object for collaborator selection and internally utilizes RDPAccountant for privacy accounting. 
 * **[Habana Accelerator Support](https://github.com/intel/openfl/tree/develop/openfl-tutorials/interactive_api/HPU/PyTorch_TinyImageNet)** 
 * **Official support for Python 3.9 and 3.10** 

docs/get_started/examples/workflowinterface_pytorch_mnist.rst

@@ -17,7 +17,7 @@ This tutorial introduces the API to get up and running with your first horizonta
 
 - Aims for syntatic consistency with the Netflix MetaFlow project. Infrastructure reuse where possible.
 
-See `full notebook <https://github.com/securefederatedai/openfl/blob/f1657abe88632d542504d6d71ca961de9333913f/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb>`_.
+See `full notebook <https://github.com/securefederatedai/openfl/blob/f1657abe88632d542504d6d71ca961de9333913f/openfl-tutorials/experimental/101_MNIST.ipynb>`_.
 
 **What is it?**
 The workflow interface is a new way of composing federated learning experiments with |productName|. 

openfl-tutorials/experimental/1001_Workspace_Creation_from_JupyterNotebook.ipynb

@@ -962,7 +962,7 @@
     "from openfl.experimental.workspace_export import WorkspaceExport\n",
     "\n",
     "WorkspaceExport.export(\n",
-    "    notebook_path='./Workflow_Interface_1001_Workspace_Creation_from_JupyterNotebook.ipynb',\n",
+    "    notebook_path='./1001_Workspace_Creation_from_JupyterNotebook.ipynb',\n",
     "    output_workspace=f\"/home/{os.environ['USER']}/generated-workspace\"\n",
     ")"
    ]

openfl-tutorials/experimental/101_MNIST.ipynb

@@ -7,7 +7,7 @@
    "metadata": {},
    "source": [
     "# Workflow Interface 101: Quickstart\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/101_MNIST.ipynb)"
    ]
   },
   {

openfl-tutorials/experimental/102_Aggregator_Validation.ipynb

@@ -16,7 +16,7 @@
    "id": "bd059520",
    "metadata": {},
    "source": [
-    "In this tutorial, we build on the ideas from the [first](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb) quick start notebook, and demonstrate how to perform validation on the aggregator after training."
+    "In this tutorial, we build on the ideas from the [first](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/101_MNIST.ipynb) quick start notebook, and demonstrate how to perform validation on the aggregator after training."
    ]
   },
   {

openfl-tutorials/experimental/104_Keras_MNIST_with_GPU.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "# Workflow Interface 104: Working with Keras\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_104_Keras_MNIST_with_GPU.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/104_Keras_MNIST_with_GPU.ipynb)"
    ]
   },
   {

openfl-tutorials/experimental/104_MNIST_XPU.ipynb

@@ -6,7 +6,7 @@
    "metadata": {},
    "source": [
     "# Workflow Interface 104: MNIST XPU\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_104_MNIST_XPU.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/104_MNIST_XPU.ipynb)"
    ]
   },
   {

openfl-tutorials/experimental/201_Exclusive_GPUs_with_Ray.ipynb

@@ -7,7 +7,7 @@
    "metadata": {},
    "source": [
     "# Workflow Interface 201: Using Ray to request exclusive GPUs\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_201_Exclusive_GPUs_with_Ray.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/201_Exclusive_GPUs_with_Ray.ipynb)"
    ]
   },
   {

openfl-tutorials/experimental/301_MNIST_Watermarking.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# Workflow Interface 301: Watermarking\n",
     "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_301_MNIST_Watermarking.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/301_MNIST_Watermarking.ipynb)"
    ]
   },
   {

openfl-tutorials/experimental/401_MNIST_Aggregator_Validation_Ray_Watermarking.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# Workflow Interface 401: Aggregator validation with a watermark dataset using Ray\n",
     "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_401_MNIST_Aggregator_Validation_Ray_Watermarking.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/401_MNIST_Aggregator_Validation_Ray_Watermarking.ipynb)"
    ]
   },
   {
@@ -20,9 +20,9 @@
     "This tutorial is a merge of some of the previous notebooks.\n",
     "\n",
     "The purpose of this OpenFL Workflow Interface tutorial is to showcase the following:\n",
-    "- Performing validation on the aggregator (see the [102](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_102_Aggregator_Validation.ipynb) notebook)\n",
-    "- Training with watermarking of DL Model in Federated Learning (see the [301](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_301_MNIST_Watermarking.ipynb) notebook)\n",
-    "- Utilizing multiple GPUs for concurrent model training using the Ray Backend in LocalRuntime (see the [201](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_201_Exclusive_GPUs_with_Ray.ipynb) notebook).\n",
+    "- Performing validation on the aggregator (see the [102](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/102_Aggregator_Validation.ipynb) notebook)\n",
+    "- Training with watermarking of DL Model in Federated Learning (see the [301](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/301_MNIST_Watermarking.ipynb) notebook)\n",
+    "- Utilizing multiple GPUs for concurrent model training using the Ray Backend in LocalRuntime (see the [201](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/201_Exclusive_GPUs_with_Ray.ipynb) notebook).\n",
     "\n",
     "Watermarking enables the Model owner to assert ownership rights and detect stolen model instances."
    ]
@@ -443,7 +443,7 @@
    "id": "c917b085",
    "metadata": {},
    "source": [
-    "Let us now define the Workflow for Watermark embedding. Here we use the same tasks as the [quickstart](https://github.com/psfoley/openfl/blob/experimental-workflow-interface/openfl-tutorials/experimental/Workflow_Interface_MNIST.ipynb), and define following additional steps for Watermarking:\n",
+    "Let us now define the Workflow for Watermark embedding. Here we use the same tasks as the [quickstart](https://github.com/psfoley/openfl/blob/experimental-workflow-interface/openfl-tutorials/experimental/MNIST.ipynb), and define following additional steps for Watermarking:\n",
     "- PRE-TRAIN (watermark_retrain): At the start (once), initial model is trained on Watermark dataset for a specified number of epochs.\n",
     "- RE-TRAIN (watermark_pretrain): Every training round, Aggregated model is retrained on Watermark dataset until a desired acc threshold is reached or max number of retrain rounds are expired.\n",
     "\n",
@@ -713,7 +713,7 @@
    "source": [
     "In the `AggregatorValCollaboratorGPUWatermarking` definition above, you will notice that certain attributes of the flow were not initialized, namely the `watermark_data_loader` for Aggregator and `train_loader`, `test_loader` for the Collaborators. \n",
     "\n",
-    "- Collaborator attributes are created in the same manner as described in [quickstart](https://github.com/psfoley/openfl/blob/experimental-workflow-interface/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb).\n",
+    "- Collaborator attributes are created in the same manner as described in [quickstart](https://github.com/psfoley/openfl/blob/experimental-workflow-interface/openfl-tutorials/experimental/101_MNIST.ipynb).\n",
     "\n",
     "- `watermark_data_loader` is created as a **private attribute** of the Aggregator and it is exposed only via the runtime. This property enables the Watermark dataset to be hidden from the collaborators as Aggregator private attributes are filtered before the state is transferred to Collaborators (in the same manner as Collaborator private attributes are hidden from Aggregator).\n",
     "\n",

openfl-tutorials/experimental/LLM/neuralchat/Workflow_Interface_NeuralChat.ipynb

@@ -15,7 +15,7 @@
    "id": "bd059520",
    "metadata": {},
    "source": [
-    "In this tutorial, we build on the ideas from the [first](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/Workflow_Interface_101_MNIST.ipynb) quick start notebook, and demonstrate how to fine-tune a Large Language Model (LLM) in a federated learning workflow. \n",
+    "In this tutorial, we build on the ideas from the [first](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/101_MNIST.ipynb) quick start notebook, and demonstrate how to fine-tune a Large Language Model (LLM) in a federated learning workflow. \n",
     "\n",
     "We will fine-tune **Intel's [neural-chat-7b](https://huggingface.co/Intel/neural-chat-7b-v1)** model on the [MedQuAD](https://github.com/abachaa/MedQuAD) dataset, an open-source medical question-answer pair dataset collated from 12 NIH websites. To do this, we will leverage the **[Intel(R) Extension for Transformers](https://github.com/intel/intel-extension-for-transformers)**, which extends th [Hugging Face Transformers](https://github.com/huggingface/transformers)  library with added features for optimal performance on Intel hardware.."
    ]