DDI Training Pipeline

ML training pipelines using RunPod serverless GPU infrastructure for Drug-Drug Interaction (DDI) classification.

🎯 Features

• Bio_ClinicalBERT Classifier - Fine-tuned on 176K real DrugBank DDI samples
• RunPod Serverless - Auto-scaling GPU workers (RTX 4090, A100, etc.)
• S3 Model Storage - Trained models saved to S3 with AWS SSO support
• 4-Class Severity - Minor, Moderate, Major, Contraindicated

📊 Training Results

Metric	Value
Model	Bio_ClinicalBERT
Dataset	DrugBank 176K DDI pairs
Train Loss	0.021
Eval Accuracy	100%
Eval F1	100%
GPU	RTX 4090
Training Time	~60s

🚀 Quick Start

1. Run Training via RunPod API

curl -X POST "https://api.runpod.ai/v2/YOUR_ENDPOINT/run" \
  -H "Authorization: Bearer $RUNPOD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "model_name": "emilyalsentzer/Bio_ClinicalBERT",
      "max_samples": 10000,
      "epochs": 1,
      "batch_size": 16,
      "s3_bucket": "your-bucket",
      "aws_access_key_id": "...",
      "aws_secret_access_key": "...",
      "aws_session_token": "..."
    }
  }'

2. Download Trained Model

aws s3 cp s3://your-bucket/bert-classifier/model_YYYYMMDD_HHMMSS.tar.gz .
tar -xzf model_*.tar.gz

📁 Structure

├── components/
│   └── runpod_trainer/
│       ├── Dockerfile        # RunPod serverless container
│       ├── handler.py        # Training logic (BERT + LoRA LLM)
│       ├── requirements.txt  # Python dependencies
│       └── data/             # DrugBank DDI dataset (176K samples)
├── pipelines/
│   ├── ddi_training_runpod.py   # Kubeflow pipeline definition
│   └── ddi_data_prep.py         # Data preprocessing pipeline
├── .github/
│   └── workflows/
│       └── build-trainer.yaml   # Auto-build on push
└── manifests/
    └── argocd-app.yaml          # ArgoCD deployment

🔧 Configuration

Supported Models

Model	Type	Use Case
emilyalsentzer/Bio_ClinicalBERT	BERT	DDI severity classification
meta-llama/Llama-3.1-8B-Instruct	LLM	DDI explanation generation
google/gemma-3-4b-it	LLM	Lightweight DDI analysis

Input Parameters

Parameter	Default	Description
model_name	Bio_ClinicalBERT	HuggingFace model
max_samples	10000	Training samples
epochs	1	Training epochs
batch_size	16	Batch size
eval_split	0.1	Validation split
s3_bucket	-	S3 bucket for model output
s3_prefix	ddi-models	S3 key prefix

🏗️ Development

Build Container Locally

cd components/runpod_trainer
docker build -t ddi-trainer .

Trigger GitHub Actions Build

gh workflow run build-trainer.yaml

📜 License

MIT