neotune 0.1.0

Supervised fine-tuning of LLMs with LoRA and DeepSpeed

Supervised Fine-Tuning (SFT) with LoRA and DeepSpeed

This project provides a streamlined pipeline for fine-tuning Large Language Models (LLMs) like Llama 3.1 using Low-Rank Adaptation (LoRA) and DeepSpeed for efficient distributed training.

📂 Directory Structure

SFT/
├── configs/                 # Configuration files (planned)
├── <placeholder>/           # Datasets
├── <placeholder>/           # Output directory for checkpoints and adapters
├── run.sh                   # Main entry point script
├── lora_sft.py              # Main training and inference script
├── config.yaml              # Hyperparameters and paths configuration
├── ds_config.json           # DeepSpeed configuration
├── requirements.txt         # Python dependencies
└── README.md                # This file

🚀 Setup

1. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate
   

2. Install dependencies:

   pip install -r requirements.txt
   

   (Ensure deepspeed is installed compatible with your CUDA version)

3. Configure Environment: Create a .env file in the root directory:

   HF_TOKEN=your_huggingface_token
   WANDB_API_KEY=your_wandb_key  # Optional, for logging
   

🛠️ Configuration

• config.yaml: Controls model ID, dataset paths, training hyperparameters (learning rate, epochs, batch size), and LoRA settings.
• ds_config.json: Configures DeepSpeed optimization (ZeRO stage, offloading, mixed precision).

🏃 Usage

Use the provided scripts/run.sh wrapper for easy execution. It automatically handles directory paths.

Training

To start fine-tuning the model:

# Default: Train on 2 GPUs
./run.sh train 2

# Train on 4 GPUs
./scripts/run.sh train 4

Inference

To evaluate the fine-tuned model on the test set:

./scripts/run.sh inference

Custom Configuration

You can specify a custom DeepSpeed config file:

./scripts/run.sh --config custom_ds_config.json train 4

📊 Monitoring

Training progress (loss, accuracy, etc.) is logged to MLflow (and/or WandB if configured).

To view MLflow logs locally:

mlflow ui

Then open http://localhost:5000 in your browser.

🐛 Troubleshooting

• deepspeed: command not found: Ensure you have activated the virtual environment where deepspeed is installed.
• CUDA Errors: Check ds_config.json to ensure batch sizes and offloading settings fit your GPU memory.

☸️ Ray Train + Kubernetes (KubeRay)

This repo now includes a Ray Train entrypoint for running multi-GPU training on a Ray cluster (including on Kubernetes via KubeRay).

• Ray entrypoint: SFT/ray_train_lora_sft.py
• KubeRay RayJob template: SFT/k8s/rayjob-lora-sft.yaml
• Container build: SFT/k8s/Dockerfile

Local Ray (single node)

pip install -r requirements.txt
python ray_train_lora_sft.py --num_workers 2

Kubernetes (high level)

1. Build/push the image from SFT/k8s/Dockerfile and set it in rayjob-lora-sft.yaml.
2. Create PVCs for:
   • /workspace/SFT/data_dir (your training data)
   • /mnt/ray-results (Ray Train run storage / checkpoints)
3. Apply the RayJob:

kubectl apply -f SFT/k8s/rayjob-lora-sft.yaml