flash-mineru 0.0.2
Fast Inference Architecture for MinerU
License: Apache-2.0
Requires Python: <4,>=3.10
Keywords: AI, artificial intelligence

# Flash-MinerU ⚡️📄

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[简体中文](./README-zh.md) | English

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> Accelerating the **VLM Inference Pipeline** of the open-source PDF parsing project **MinerU** with **Ray**

Flash-MinerU is a **lightweight and low-intrusion** acceleration project. Its goal is to leverage **Ray’s parallel and distributed capabilities** to parallelize and accelerate the most time-consuming stage in **MinerU** — the **VLM (Vision-Language Model) inference stage** — thereby significantly improving the overall throughput of **PDF → Markdown** processing.

This project is positioned as a **parallelization and engineering accelerator**, rather than a reimplementation of MinerU’s core algorithms. Its design goals include:

- **Minimal dependencies, lightweight installation**
  - One-click install & run via `pip install flash-mineru`
  - Tested in **domestic computing environments such as METAX**
- **Maximum reuse of MinerU’s original logic and data structures**
  - Preserving algorithmic behavior and output consistency
- **Multi-GPU / multi-process / multi-cluster friendly**
  - Designed for large-scale batch PDF processing, easy to scale up

---

## ✨ Features

- 🚀 **Ray-based parallel inference**  
  PDF pages / images are sliced into batches and dispatched to multiple Ray actors for parallel execution

- 🧠 **VLM inference acceleration**  
  Focuses on the VLM inference stage in MinerU; currently defaults to **vLLM** for high-throughput inference

- 🧩 **Low-intrusion design**  
  Retains MinerU’s original intermediate structures (`middle_json`) and Markdown generation logic

---

## 📦 Installation

### Basic installation (lightweight mode)

Suitable if you have **already installed the inference backend manually** (e.g., vLLM), or are using an image with a prebuilt environment:

```bash
pip install flash-mineru
```

### Install with vLLM backend enabled (optional)

If you want Flash-MinerU to install vLLM as the inference backend for you:

```bash
pip install flash-mineru[vllm]
```

---

## 🚀 Quickstart

### Minimal Python API example

```python
from flash_mineru import MineruEngine

# Path to PDFs
pdfs = [
    "resnet.pdf",
    "yolo.pdf",
    "text2sql.pdf",
]

engine = MineruEngine(
    model="<path_to_local>/MinerU2.5-2509-1.2B",
    # Model can be downloaded from https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B
    batch_size=2,              # Number of PDFs processed concurrently per model instance
    replicas=3,                # Number of parallel vLLM / model instances
    num_gpus_per_replica=0.5, # Fraction of GPU memory used per instance (vLLM KV cache)
    save_dir="outputs_mineru", # Output directory for parsed results
)

results = engine.run(pdfs)
print(results)  # list[list[str]], dir name of the output files
```

### Output structure

* Each PDF’s parsing results will be generated under:

  ```
  <save_dir>/<pdf_name>/
  ```

* The Markdown file is located by default at:

  ```
  <save_dir>/<pdf_name>/vlm/<pdf_name>.md
  ```

---

## 📊 Benchmark
<details>
<summary><strong>~4× end-to-end speedup on multi-GPU setups (experimental details)</strong></summary>

### Experimental Setup

- **Dataset**
  - 23 academic paper PDFs (each with 9–37 pages)
  - Each PDF duplicated 16 times
  - **368 medium-length PDF files** in total

- **Versions**
  - MinerU: official **v2.7.5**
  - Flash-MinerU: partially based on logic from **MinerU v2.5.x**, with parallelization applied to the VLM inference stage

- **Hardware**
  - Single machine with **8 × NVIDIA A100 GPUs**

---

### Results

| Method | Inference Configuration | Total Time |
|----|----|----|
| MinerU (vanilla) | vLLM backend | ~65 min |
| Flash-MinerU | 16 × VLM processes, single machine with 8 GPUs | **~16 min** |
| Flash-MinerU | 3 × VLM processes, single GPU | ~40 min |

---

### Summary

- Under the **same 8× A100 setup**, Flash-MinerU achieves an **~4× end-to-end speedup** compared to vanilla MinerU
- Even on a **single-GPU setup**, multi-process VLM inference significantly improves overall throughput
- The performance gains mainly come from **parallelizing the VLM inference stage** and **more efficient GPU utilization**

> Note: The benchmark focuses on overall throughput. The output structure and result quality remain consistent with MinerU.

</details>

---

## 🗺️ Roadmap

* [ ] Benchmark scripts (single GPU vs multiple replicas)
* [ ] Support for more inference backends (e.g., sglang)
* [ ] Service-oriented deployment (HTTP API / task queue)
* [ ] Sample datasets and more comprehensive documentation

---

## 🤝 Acknowledgements

* **MinerU**
  This project is built upon MinerU’s overall algorithm design and engineering practices, and parallelizes its VLM inference pipeline.
  The `mineru_core/` directory contains code logic copied from and adapted to the MinerU project.
  We extend our sincere respect and gratitude to the original authors and all contributors of MinerU.
  🔗 Official repository / homepage:
  [https://github.com/opendatalab/MinerU](https://github.com/opendatalab/MinerU)

* **Ray**
  Provides powerful abstractions for distributed and parallel computing, making multi-GPU and multi-process orchestration simpler and more reliable.
  🔗 Official website:
  [https://www.ray.io/](https://www.ray.io/)
  🔗 Official GitHub:
  [https://github.com/ray-project/ray](https://github.com/ray-project/ray)

* **vLLM**
  Provides a high-throughput, production-ready inference engine (currently the default backend).
  🔗 Official website:
  [https://vllm.ai/](https://vllm.ai/)
  🔗 Official GitHub:
  [https://github.com/vllm-project/vllm](https://github.com/vllm-project/vllm)

---

## 📜 License

**AGPL-3.0**

> Notes:
> The `mineru_core/` directory in this project contains derivative code based on **MinerU (AGPL-3.0)**.
> In accordance with the AGPL-3.0 license requirements, this repository as a whole is released under **AGPL-3.0** as a derivative work.
> For details, please refer to the root `LICENSE` file and `mineru_core/README.md`.