# vLLM Scripts Development Notes

## Repository Purpose
This repository contains UV scripts for vLLM-based inference tasks. Focus on GPU-accelerated inference using vLLM's optimized engine.

## Key Patterns

### 1. GPU Requirements
All scripts MUST check for GPU availability:
```python
if not torch.cuda.is_available():
    logger.error("CUDA is not available. This script requires a GPU.")
    sys.exit(1)
```

### 2. vLLM Docker Image
Always use `vllm/vllm-openai:latest` for HF Jobs - it has all dependencies pre-installed.

### 3. Dependencies
Include custom PyPI indexes for vLLM and FlashInfer:
```python
# [[tool.uv.index]]
# url = "https://flashinfer.ai/whl/cu126/torch2.6"
# 
# [[tool.uv.index]]
# url = "https://wheels.vllm.ai/nightly"
```

## Current Scripts

1. **classify-dataset.py**: BERT-style text classification
   - Uses vLLM's classify task
   - Supports batch processing with configurable size
   - Automatically extracts label mappings from model config

## Future Scripts

Potential additions:
- Text generation with vLLM
- Embedding generation using sentence transformers
- Multi-modal inference
- Structured output generation

## Testing

Local testing requires GPU. For scripts without local GPU access:
1. Use HF Jobs with small test datasets
2. Verify script runs without syntax errors: `python -m py_compile script.py`
3. Check dependencies resolve: `uv pip compile`

## Performance Considerations

- Default batch size: 10,000 for local, up to 100,000 for HF Jobs
- L4 GPUs are cost-effective for classification
- Monitor GPU memory usage and adjust batch sizes accordingly