Add DDI training pipeline with RunPod serverless GPU support

components/runpod_trainer/handler.py

@@ -0,0 +1,183 @@
+"""
+RunPod Serverless Handler for DDI Model Training
+
+This runs on RunPod GPU instances and trains the Bio_ClinicalBERT model
+for drug-drug interaction detection.
+"""
+import os
+import json
+import runpod
+from typing import Dict, Any
+
+
+def download_from_minio(bucket: str, key: str, local_path: str):
+    """Download file from MinIO."""
+    import boto3
+    
+    s3 = boto3.client(
+        's3',
+        endpoint_url=os.environ['MINIO_ENDPOINT'],
+        aws_access_key_id=os.environ['MINIO_ACCESS_KEY'],
+        aws_secret_access_key=os.environ['MINIO_SECRET_KEY']
+    )
+    s3.download_file(bucket, key, local_path)
+
+
+def upload_to_minio(local_path: str, bucket: str, key: str):
+    """Upload file to MinIO."""
+    import boto3
+    
+    s3 = boto3.client(
+        's3',
+        endpoint_url=os.environ['MINIO_ENDPOINT'],
+        aws_access_key_id=os.environ['MINIO_ACCESS_KEY'],
+        aws_secret_access_key=os.environ['MINIO_SECRET_KEY']
+    )
+    s3.upload_file(local_path, bucket, key)
+
+
+def train_ddi_model(job_input: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Train DDI detection model.
+    
+    Expected input:
+    {
+        "model_name": "emilyalsentzer/Bio_ClinicalBERT",
+        "dataset_path": "datasets/ddi_train.json",
+        "epochs": 3,
+        "learning_rate": 2e-5,
+        "batch_size": 16,
+        "output_path": "models/ddi_model_v1"
+    }
+    """
+    import torch
+    from transformers import (
+        AutoTokenizer,
+        AutoModelForSequenceClassification,
+        TrainingArguments,
+        Trainer
+    )
+    from datasets import Dataset
+    import tempfile
+    import shutil
+    
+    # Extract parameters
+    model_name = job_input.get('model_name', 'emilyalsentzer/Bio_ClinicalBERT')
+    dataset_path = job_input.get('dataset_path', 'datasets/ddi_train.json')
+    epochs = job_input.get('epochs', 3)
+    learning_rate = job_input.get('learning_rate', 2e-5)
+    batch_size = job_input.get('batch_size', 16)
+    output_path = job_input.get('output_path', 'models/ddi_model')
+    
+    # Create temp directory
+    work_dir = tempfile.mkdtemp()
+    data_file = os.path.join(work_dir, 'train.json')
+    model_dir = os.path.join(work_dir, 'model')
+    
+    try:
+        # Download training data from MinIO
+        print(f"Downloading dataset from {dataset_path}...")
+        download_from_minio('datasets', dataset_path, data_file)
+        
+        # Load dataset
+        with open(data_file, 'r') as f:
+            train_data = json.load(f)
+        
+        dataset = Dataset.from_list(train_data)
+        
+        # Load model and tokenizer
+        print(f"Loading model: {model_name}")
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForSequenceClassification.from_pretrained(
+            model_name,
+            num_labels=5  # DDI severity levels: none, minor, moderate, major, contraindicated
+        )
+        
+        # Tokenize dataset
+        def tokenize_function(examples):
+            return tokenizer(
+                examples['text'],
+                padding='max_length',
+                truncation=True,
+                max_length=512
+            )
+        
+        tokenized_dataset = dataset.map(tokenize_function, batched=True)
+        
+        # Training arguments
+        training_args = TrainingArguments(
+            output_dir=model_dir,
+            num_train_epochs=epochs,
+            learning_rate=learning_rate,
+            per_device_train_batch_size=batch_size,
+            per_device_eval_batch_size=batch_size,
+            warmup_steps=100,
+            weight_decay=0.01,
+            logging_dir=os.path.join(work_dir, 'logs'),
+            logging_steps=10,
+            save_strategy='epoch',
+            evaluation_strategy='epoch' if 'validation' in train_data else 'no',
+            load_best_model_at_end=True if 'validation' in train_data else False,
+            fp16=torch.cuda.is_available(),
+        )
+        
+        # Initialize trainer
+        trainer = Trainer(
+            model=model,
+            args=training_args,
+            train_dataset=tokenized_dataset,
+        )
+        
+        # Train
+        print("Starting training...")
+        train_result = trainer.train()
+        
+        # Save model
+        print("Saving model...")
+        trainer.save_model(model_dir)
+        tokenizer.save_pretrained(model_dir)
+        
+        # Save training metrics
+        metrics = {
+            'train_loss': train_result.training_loss,
+            'epochs': epochs,
+            'model_name': model_name,
+            'samples': len(dataset)
+        }
+        
+        with open(os.path.join(model_dir, 'metrics.json'), 'w') as f:
+            json.dump(metrics, f)
+        
+        # Upload model to MinIO
+        print(f"Uploading model to {output_path}...")
+        for root, dirs, files in os.walk(model_dir):
+            for file in files:
+                local_file = os.path.join(root, file)
+                relative_path = os.path.relpath(local_file, model_dir)
+                minio_key = f"{output_path}/{relative_path}"
+                upload_to_minio(local_file, 'models', minio_key)
+        
+        return {
+            'status': 'success',
+            'model_path': f"s3://models/{output_path}",
+            'metrics': metrics
+        }
+        
+    except Exception as e:
+        return {
+            'status': 'error',
+            'error': str(e)
+        }
+    finally:
+        # Cleanup
+        shutil.rmtree(work_dir, ignore_errors=True)
+
+
+def handler(job):
+    """RunPod serverless handler."""
+    job_input = job['input']
+    return train_ddi_model(job_input)
+
+
+# RunPod serverless entrypoint
+runpod.serverless.start({'handler': handler})