Update xpu definition and copyright

openfl-tutorials/experimental/Workflow_Interface_104_MNIST_XPU.ipynb

@@ -14,13 +14,15 @@
    "id": "bd059520",
    "metadata": {},
    "source": [
-    "Welcome to the OpenFL Experimental Workflow Interface tutorial using XPU! This notebook introduces the API to get up and running with your first horizontal federated learning workflow using a XPU. This work has the following goals:\n",
+    "Welcome to the OpenFL Experimental Workflow Interface tutorial using Intel® Data Center GPU Max Series, also referred to as XPU in this context! Before we dive in, let's clarify some terms. XPU is a term coined by Intel to describe their line of computing devices, which includes CPUs, GPUs, FPGAs, and other accelerators. In this tutorial, we will be focusing on the Intel® Data Center GPU Max Series model, a powerful GPU that is part of Intel's XPU lineup. \n",
+    "\n",
+    "This notebook introduces the API to get up and running with your first horizontal federated learning workflow using a XPU. This work has the following goals:\n",
     "\n",
     "- Simplify the federated workflow representation\n",
     "- Help users better understand the steps in federated learning (weight extraction, compression, etc.)\n",
     "- Designed to maintain data privacy\n",
     "- Aims for syntatic consistency with the Netflix MetaFlow project. Infrastructure reuse where possible.\n",
-    "- Introduction for use OpenFL and Intel® Extension for PyTorch* with the latest performance optimizations for Intel hardware. Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs through the PyTorch* xpu device.\n",
+    "- Introduction for use OpenFL and Intel® Extension for PyTorch* with the latest performance optimizations for Intel hardware. Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs through the PyTorch* XPU device.\n",
     "   \n"
    ]
   },
@@ -30,8 +32,12 @@
    "metadata": {},
    "source": [
     "# Quick XPU Setup\n",
+    "In this tutorial, when we refer to XPU, we are specifically referring to the Intel® Data Center GPU Max Series. When using the Intel® Extension for PyTorch* package, selecting the device as 'xpu' will refer to this Intel® Data Center GPU Max Series.\n",
+    "\n",
     "For a successful setup, please follow the steps outlined in the [Installation Guide](https://intel.github.io/intel-extension-for-pytorch/xpu/2.1.10+xpu/tutorials/installation.html). This guide provides detailed information on system requirements and the installation process for the Intel® Extension for PyTorch. For a deeper understanding of features, APIs, and technical details, refer to the [Intel® Extension for PyTorch* Documentation](https://intel.github.io/intel-extension-for-pytorch/xpu/2.1.10+xpu/index.html).\n",
     "\n",
+    "Hardware Prerequisite: Intel® Data Center GPU Max Series.\n",
+    "\n",
     "This Jupyter Notebook has been tested and confirmed to work with the following versions:\n",
     "- intel-extension-for-pytorch==2.0.120 (xpu)\n",
     "- pytorch==2.0.1\n",

openfl-tutorials/interactive_api/PyTorch_TinyImageNet_XPU/README.md

@@ -3,13 +3,19 @@
 ## **How to run this tutorial (without TLC and locally as a simulation):**
 <br/>
 
+Before we dive in, let's clarify some terms. XPU is a term coined by Intel to describe their line of computing devices, which includes CPUs, GPUs, FPGAs, and other accelerators. In this tutorial, we will be focusing on the Intel® Data Center GPU Max Series model, a powerful GPU that is part of Intel's XPU lineup.
+
 ### 0a. If you haven't done so already, create a virtual environment, install OpenFL, and upgrade pip:
   - For help with this step, visit the "Install the Package" section of the [OpenFL installation instructions](https://openfl.readthedocs.io/en/latest/install.html#install-the-package).
 
 <br/>
 
 ### 0b. Quick XPU Setup
-  For a successful setup, please follow the steps outlined in the Installation Guide. This guide provides detailed information on system requirements and the installation process for the Intel® Extension for PyTorch. For a deeper understanding of features, APIs, and technical details, refer to the Intel® Extension for PyTorch* Documentation.
+  In this tutorial, when we refer to XPU, we are specifically referring to the Intel® Data Center GPU Max Series. When using the Intel® Extension for PyTorch* package, selecting the device as 'xpu' will refer to this Intel® Data Center GPU Max Series.
+
+  For a successful setup, please follow the steps outlined in the [Installation Guide](https://intel.github.io/intel-extension-for-pytorch/xpu/2.1.10+xpu/tutorials/installation.html). This guide provides detailed information on system requirements and the installation process for the Intel® Extension for PyTorch. For a deeper understanding of features, APIs, and technical details, refer to the [Intel® Extension for PyTorch* Documentation](https://intel.github.io/intel-extension-for-pytorch/xpu/2.1.10+xpu/index.html).
+
+Hardware Prerequisite: Intel® Data Center GPU Max Series.
 
 This Jupyter Notebook has been tested and confirmed to work with the following versions:
 

openfl-tutorials/interactive_api/PyTorch_TinyImageNet_XPU/envoy/tinyimagenet_shard_descriptor.py

@@ -1,4 +1,4 @@
-# Copyright (C) 2020-2021 Intel Corporation
+# Copyright (C) 2020-2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
 """TinyImageNet Shard Descriptor."""