| # 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 |