Extended functionality for CIFAR

extension/training/examples/CIFAR/README.md

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+# CIFAR 10 End-to-End Fine-Tuning Tutorial
+
+## Objective:
+
+This tutorial guides the users through the training process of a simple PyTorch CNN model on the server and subsequently fine-tune the model on their edge devices.
+
+### Key Objectives
+
+1. **Server-Side Training**: Users can leverage the computational resource of the server to perform initial model training using PyTorch.
+2. **Edge Device Fine-Tuning**: Pre-trained models are lowered and deployed on mobile devices through ExecuTorch where they undergo fine-tuning.
+3. **Performance Benchmarking**: To track comprehensive performance metrics for on-device fine-tuning operations, measuring factors such as training speed, memory usage, and model accuracy to evaluate ExecuTorch's effectiveness in the edge environment.
+
+## ExecuTorch Environment Setup
+
+For easier management of Python environments and packages, we recommended using a Python environment management tool such as `conda`, `venv`, or `uv`. In this demonstration, we will use `uv` to set up the Python environment.
+
+To install ExecuTorch in a [`uv`](https://docs.astral.sh/uv/getting-started/installation/) Python environment use the following commands:
+
+```bash
+$ git clone https://github.com/pytorch/executorch.git --recurse-submodules
+$ cd executorch
+$ uv venv --seed --prompt et --python 3.10
+$ source .venv/bin/activate
+$ git fetch origin
+$ git submodule sync --recursive
+$ git submodule update --init --recursive
+$ ./install_executorch.sh
+```
+
+## Data Preparation
+
+We can download the CIFAR-10 dataset from the [official website](https://www.cs.toronto.edu/~kriz/cifar.html) and extract it to the desired location. Alternatively, we can also use the following command to download, extract, and create a balanced dataset:
+
+```bash
+python data_utils.py --train-data-batch-path ./data/cifar-10/cifar-10-batches-py/data_batch_1 --train-output-path ./data/cifar-10/extracted_data/train_data.bin --test-data-batch-path ./data/cifar-10/cifar-10-batches-py/test_batch --test-output-path ./data/cifar-10/extracted_data/test_data.bin --train-images-per-class 100
+```
+
+## Model Export
+
+Alternatively, if the users have a pre-trained pytorch model, they can export the standalone `pte`file using the following command:
+
+```bash
+python export.py --train-model-path cifar10_model.pth --pte-only-model-path cifar10_model.pte
+```
+
+For getting the `pte` and `ptd` files, they can use the following command:
+
+```bash
+python export.py --train-model-path cifar10_model.pth --with-ptd --pte-model-path cifar10_model.pte --ptd-model-path .
+```
+
+## Model Training and Fine-Tuning
+
+To run the end-to-end example, the users can use the following command:
+
+```bash
+python main.py --data-dir ./data --model-path cifar10_model.pth --pte-model-path cifar10_model.pte --split-pte-model-path cifar10_model_pte_only.pte --save-pt-json cifar10_pt.json --save-et-json cifar10_et.json --ptd-model-dir . --epochs 1 --fine-tune-epochs 1
+```