Add Kubeflow Trainer V2 based finetuning demo example

Author: abhijeet-dhumal

examples/kft-v2/README.md

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+# 🚀 Kubeflow Training V2: Advanced ML Training with Distributed Computing
+
+This directory contains comprehensive examples demonstrating **Kubeflow Training V2** capabilities for distributed training using the Kubeflow Trainer SDK.
+
+## 🎯 **What This Directory Demonstrates**
+
+- **Kubeflow Trainer SDK**: Programmatic TrainJob creation and management
+- **Checkpointing**: Controller-managed resume/suspended compatibility for model checkpoints
+- **Distributed Training**: Multi-node Multi-CPU/GPU coordination with NCCL/GLOO backends
+
+---
+### **TRL (Transformer Reinforcement Learning) Integration**
+- **SFTTrainer**: Supervised fine-tuning with instruction following
+- **PEFT-LoRA**: Parameter-efficient fine-tuning with Low-Rank Adaptation
+- **Model Support**: GPT-2, Llama, and other transformer models
+- **Dataset Integration**: Alpaca dataset for instruction-following tasks
+
+### **Distributed Training Capabilities**
+- **Multi-Node Support**: Scale training across multiple nodes
+- **Multi-GPU Coordination**: NCCL backend CUDA for NVIDIA GPUs, ROCm for AMD GPUs
+- **CPU Training**: GLOO backend for CPU-based training
+- **Resource Flexibility**: Configurable compute resources per node
+
+---
+
+## 📋 **Prerequisites**
+
+### **Cluster Requirements**
+- **OpenShift Cluster**: With OpenShift AI (RHOAI) 2.17+ installed
+- **Required Components**: `dashboard`, `trainingoperator`, and `workbenches` enabled
+- **Storage**: Persistent volume claim named `workspace` of minimum 50GB with RWX (ReadWriteMany) access mode
+
+---
+
+## 🛠️ **Setup Instructions**
+
+### **1. Repository Setup**
+
+Clone the repository and navigate to the kft-v2 directory:
+
+```bash
+git clone https://github.com/opendatahub-io/distributed-workloads.git
+cd distributed-workloads/examples/kft-v2
+```
+
+### **2. Persistent Volume Setup**
+
+Create a shared persistent volume for checkpoint storage:
+
+```bash
+oc apply -f manifests/shared_pvc.yaml
+```
+
+### **3. Cluster Training Runtime Setup**
+
+Apply the cluster training runtime configuration:
+
+```bash
+oc apply -f manifests/cluster_training_runtime.yaml
+```
+
+This creates the necessary ClusterTrainingRuntime resources for PyTorch training.
+
+
+## Setup
+
+* Access the OpenShift AI dashboard, for example from the top navigation bar menu:
+
+![](./docs/01.png)
+
+* Log in, then go to _Data Science Projects_ and create a project:
+
+![](./docs/02.png)
+
+* Once the project is created, click on _Create a workbench_:
+  
+![](./docs/03.png)
+
+* Then create a workbench with the following settings:
+  
+    * Select the `PyTorch` (or the `ROCm-PyTorch`) notebook image:
+
+    * Select the _Medium_ container size and a sufficient persistent storage volume. 
+
+    ![](./docs/04.png)
+
+    ![](./docs/05.png)
+
+    > [!NOTE]
+    >
+    > * Adding an accelerator is only needed to test the fine-tuned model from within the workbench so you can spare an accelerator if needed.
+    > * Keep the default 20GB workbench storage, it is enough to run the inference from within the workbench.
+
+
+    * Review the configuration and click _Create workbench_
+
+* From "Workbenches" page, click on _Open_ when the workbench you've just created becomes ready:
+
+![](./docs/06.png)
+
+---
+
+## 🚀 **Quick Start Examples**
+
+### **Example 1: Fashion-MNIST Training**
+
+Run the Fashion-MNIST training example:
+
+```python
+from scripts.mnist import train_fashion_mnist
+
+# Configure training parameters
+config = {
+    "epochs": 10,
+    "batch_size": 64,
+    "learning_rate": 0.001,
+    "checkpoint_dir": "/mnt/shared/checkpoints"
+}
+
+# Start training
+train_fashion_mnist(config)
+```
+
+### **Example 2: TRL GPT-2 Fine-tuning**
+
+Run the TRL training example:
+
+```python
+from scripts.trl_training import trl_train
+
+# Configure TRL parameters
+config = {
+    "model_name": "gpt2",
+    "dataset_name": "alpaca",
+    "lora_r": 16,
+    "lora_alpha": 32,
+    "max_seq_length": 512
+}
+
+# Start TRL training
+trl_train(config)
+```
+
+![](./docs/07.png)
+
+---
+
+## 📊 **Training Examples**
+
+### **Fashion-MNIST Classification**
+
+The `mnist.py` script demonstrates:
+
+- **Distributed Training**: Multi-GPU Fashion-MNIST classification
+- **Checkpointing**: Automatic checkpoint creation and resumption
+- **Progress Tracking**: Real-time training progress monitoring
+- **Error Handling**: Robust error handling and recovery
+
+**Key Features:**
+- CNN architecture for image classification
+- Distributed data loading with DistributedSampler
+- Automatic mixed precision (AMP) training
+- Comprehensive logging and metrics
+
+### **TRL GPT-2 Fine-tuning**
+
+The `trl_training.py` script demonstrates:
+
+- **Instruction Following**: Fine-tuning GPT-2 on Alpaca dataset
+- **PEFT-LoRA**: Parameter-efficient fine-tuning
+- **Checkpoint Management**: TRL-compatible checkpointing
+- **Distributed Coordination**: Multi-node training coordination
+
+**Key Features:**
+- SFTTrainer for supervised fine-tuning
+- LoRA adapters for efficient parameter updates
+- Instruction-following dataset processing
+- Hugging Face model integration
+
+---
+
+## 📚 **References and Documentation**
+
+- **[Kubeflow Trainer SDK](https://github.com/kubeflow/sdk)**: Official SDK documentation
+- **[TRL Documentation](https://huggingface.co/docs/trl/)**: Transformer Reinforcement Learning
+- **[PEFT Documentation](https://huggingface.co/docs/peft/)**: Parameter-Efficient Fine-Tuning
+- **[PyTorch Distributed Training](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html)**: Distributed training guide
+- **[OpenShift AI Documentation](https://access.redhat.com/documentation/en-us/red_hat_openshift_ai)**: RHOAI documentation
+
+---