| # 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 |
