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Commit 406b8ea9 authored by myhloli's avatar myhloli
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feat: add installation guides for extension modules and model sources

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README_zh-CN.md
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docker/compose.yaml
View file @ 406b8ea9
# Documentation:
# https://docs.sglang.ai/backend/server_arguments.html#common-launch-commands
services:
mineru-sglang:
mineru-sglang-server:
image: mineru-sglang:latest
container_name: mineru-sglang
container_name: mineru-sglang-server
restart: always
profiles: ["sglang-server"]
ports:
- 30000:30000
environment:
......@@ -30,3 +29,66 @@ services:
- driver: nvidia
device_ids: ["0"]
capabilities: [gpu]
mineru-api:
image: mineru-sglang:latest
container_name: mineru-api
restart: always
profiles: ["api"]
ports:
- 8000:8000
environment:
MINERU_MODEL_SOURCE: local
entrypoint: mineru-api
command:
--host 0.0.0.0
--port 8000
# parameters for sglang-engine
# --enable-torch-compile # You can also enable torch.compile to accelerate inference speed by approximately 15%
# --dp-size 2 # If using multiple GPUs, increase throughput using sglang's multi-GPU parallel mode
# --tp-size 2 # If you have more than one GPU, you can expand available VRAM using tensor parallelism (TP) mode.
# --mem-fraction-static 0.5 # If running on a single GPU and encountering VRAM shortage, reduce the KV cache size by this parameter, if VRAM issues persist, try lowering it further to `0.4` or below.
ulimits:
memlock: -1
stack: 67108864
ipc: host
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: [ "0" ]
capabilities: [ gpu ]
mineru-gradio:
image: mineru-sglang:latest
container_name: mineru-gradio
restart: always
profiles: ["gradio"]
ports:
- 7860:7860
environment:
MINERU_MODEL_SOURCE: local
entrypoint: mineru-gradio
command:
--server-name 0.0.0.0
--server-port 7860
--enable-sglang-engine true # Enable the sglang engine for Gradio
# --enable-api false # If you want to disable the API, set this to false
# --max-convert-pages 20 # If you want to limit the number of pages for conversion, set this to a specific number
# parameters for sglang-engine
# --enable-torch-compile # You can also enable torch.compile to accelerate inference speed by approximately 15%
# --dp-size 2 # If using multiple GPUs, increase throughput using sglang's multi-GPU parallel mode
# --tp-size 2 # If you have more than one GPU, you can expand available VRAM using tensor parallelism (TP) mode.
# --mem-fraction-static 0.5 # If running on a single GPU and encountering VRAM shortage, reduce the KV cache size by this parameter, if VRAM issues persist, try lowering it further to `0.4` or below.
ulimits:
memlock: -1
stack: 67108864
ipc: host
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: [ "0" ]
capabilities: [ gpu ]
docs/en/index.md
View file @ 406b8ea9
<div align="center" xmlns="http://www.w3.org/1999/html">
<!-- logo -->
<p align="center">
<img src="images/MinerU-logo.png" width="300px" style="vertical-align:middle;">
<img src="../images/MinerU-logo.png" width="300px" style="vertical-align:middle;">
</p>
</div>
<!-- icon -->
[![stars](https://img.shields.io/github/stars/opendatalab/MinerU.svg)](https://github.com/opendatalab/MinerU)
......@@ -15,22 +15,19 @@
[![Downloads](https://static.pepy.tech/badge/mineru)](https://pepy.tech/project/mineru)
[![Downloads](https://static.pepy.tech/badge/mineru/month)](https://pepy.tech/project/mineru)
[![OpenDataLab](https://img.shields.io/badge/Demo_on_OpenDataLab-blue?logo=data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTM0IiBoZWlnaHQ9IjEzNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj48cGF0aCBkPSJtMTIyLDljMCw1LTQsOS05LDlzLTktNC05LTksNC05LDktOSw5LDQsOSw5eiIgZmlsbD0idXJsKCNhKSIvPjxwYXRoIGQ9Im0xMjIsOWMwLDUtNCw5LTksOXMtOS00LTktOSw0LTksOS05LDksNCw5LDl6IiBmaWxsPSIjMDEwMTAxIi8+PHBhdGggZD0ibTkxLDE4YzAsNS00LDktOSw5cy05LTQtOS05LDQtOSw5LTksOSw0LDksOXoiIGZpbGw9InVybCgjYikiLz48cGF0aCBkPSJtOTEsMThjMCw1LTQsOS05LDlzLTktNC05LTksNC05LDktOSw5LDQsOSw5eiIgZmlsbD0iIzAxMDEwMSIvPjxwYXRoIGZpbGwtcnVsZT0iZXZlbm9kZCIgY2xpcC1ydWxlPSJldmVub2RkIiBkPSJtMzksNjJjMCwxNiw4LDMwLDIwLDM4LDctNiwxMi0xNiwxMi0yNlY0OWMwLTQsMy03LDYtOGw0Ni0xMmM1LTEsMTEsMywxMSw4djMxYzAsMzctMzAsNjYtNjYsNjYtMzcsMC02Ni0zMC02Ni02NlY0NmMwLTQsMy03LDYtOGwyMC02YzUtMSwxMSwzLDExLDh2MjF6bS0yOSw2YzAsMTYsNiwzMCwxNyw0MCwzLDEsNSwxLDgsMSw1LDAsMTAtMSwxNS0zQzM3LDk1LDI5LDc5LDI5LDYyVjQybC0xOSw1djIweiIgZmlsbD0idXJsKCNjKSIvPjxwYXRoIGZpbGwtcnVsZT0iZXZlbm9kZCIgY2xpcC1ydWxlPSJldmVub2RkIiBkPSJtMzksNjJjMCwxNiw4LDMwLDIwLDM4LDctNiwxMi0xNiwxMi0yNlY0OWMwLTQsMy03LDYtOGw0Ni0xMmM1LTEsMTEsMywxMSw4djMxYzAsMzctMzAsNjYtNjYsNjYtMzcsMC02Ni0zMC02Ni02NlY0NmMwLTQsMy03LDYtOGwyMC02YzUtMSwxMSwzLDExLDh2MjF6bS0yOSw2YzAsMTYsNiwzMCwxNyw0MCwzLDEsNSwxLDgsMSw1LDAsMTAtMSwxNS0zQzM3LDk1LDI5LDc5LDI5LDYyVjQybC0xOSw1djIweiIgZmlsbD0iIzAxMDEwMSIvPjxkZWZzPjxsaW5lYXJHcmFkaWVudCBpZD0iYSIgeDE9Ijg0IiB5MT0iNDEiIHgyPSI3NSIgeTI9IjEyMCIgZ3JhZGllbnRVbml0cz0idXNlclNwYWNlT25Vc2UiPjxzdG9wIHN0b3AtY29sb3I9IiNmZmYiLz48c3RvcCBvZmZzZXQ9IjEiIHN0b3AtY29sb3I9IiMyZTJlMmUiLz48L2xpbmVhckdyYWRpZW50PjxsaW5lYXJHcmFkaWVudCBpZD0iYiIgeDE9Ijg0IiB5MT0iNDEiIHgyPSI3NSIgeTI9IjEyMCIgZ3JhZGllbnRVbml0cz0idXNlclNwYWNlT25Vc2UiPjxzdG9wIHN0b3AtY29sb3I9IiNmZmYiLz48c3RvcCBvZmZzZXQ9IjEiIHN0b3AtY29sb3I9IiMyZTJlMmUiLz48L2xpbmVhckdyYWRpZW50PjxsaW5lYXJHcmFkaWVudCBpZD0iYyIgeDE9Ijg0IiB5MT0iNDEiIHgyPSI3NSIgeTI9IjEyMCIgZ3JhZGllbnRVbml0cz0idXNlclNwYWNlT25Vc2UiPjxzdG9wIHN0b3AtY29sb3I9IiNmZmYiLz48c3RvcCBvZmZzZXQ9IjEiIHN0b3AtY29sb3I9IiMyZTJlMmUiLz48L2xpbmVhckdyYWRpZW50PjwvZGVmcz48L3N2Zz4=&labelColor=white)](https://mineru.net/OpenSourceTools/Extractor?source=github)
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[![ModelScope](https://img.shields.io/badge/Demo_on_ModelScope-purple?logo=data:image/svg+xml;base64,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&labelColor=white)](https://www.modelscope.cn/studios/OpenDataLab/MinerU)
[![HuggingFace](https://img.shields.io/badge/Demo_on_HuggingFace-yellow.svg?logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAF8AAABYCAMAAACkl9t/AAAAk1BMVEVHcEz/nQv/nQv/nQr/nQv/nQr/nQv/nQv/nQr/wRf/txT/pg7/yRr/rBD/zRz/ngv/oAz/zhz/nwv/txT/ngv/0B3+zBz/nQv/0h7/wxn/vRb/thXkuiT/rxH/pxD/ogzcqyf/nQvTlSz/czCxky7/SjifdjT/Mj3+Mj3wMj15aTnDNz+DSD9RTUBsP0FRO0Q6O0WyIxEIAAAAGHRSTlMADB8zSWF3krDDw8TJ1NbX5efv8ff9/fxKDJ9uAAAGKklEQVR42u2Z63qjOAyGC4RwCOfB2JAGqrSb2WnTw/1f3UaWcSGYNKTdf/P+mOkTrE+yJBulvfvLT2A5ruenaVHyIks33npl/6C4s/ZLAM45SOi/1FtZPyFur1OYofBX3w7d54Bxm+E8db+nDr12ttmESZ4zludJEG5S7TO72YPlKZFyE+YCYUJTBZsMiNS5Sd7NlDmKM2Eg2JQg8awbglfqgbhArjxkS7dgp2RH6hc9AMLdZYUtZN5DJr4molC8BfKrEkPKEnEVjLbgW1fLy77ZVOJagoIcLIl+IxaQZGjiX597HopF5CkaXVMDO9Pyix3AFV3kw4lQLCbHuMovz8FallbcQIJ5Ta0vks9RnolbCK84BtjKRS5uA43hYoZcOBGIG2Epbv6CvFVQ8m8loh66WNySsnN7htL58LNp+NXT8/PhXiBXPMjLSxtwp8W9f/1AngRierBkA+kk/IpUSOeKByzn8y3kAAAfh//0oXgV4roHm/kz4E2z//zRc3/lgwBzbM2mJxQEa5pqgX7d1L0htrhx7LKxOZlKbwcAWyEOWqYSI8YPtgDQVjpB5nvaHaSnBaQSD6hweDi8PosxD6/PT09YY3xQA7LTCTKfYX+QHpA0GCcqmEHvr/cyfKQTEuwgbs2kPxJEB0iNjfJcCTPyocx+A0griHSmADiC91oNGVwJ69RudYe65vJmoqfpul0lrqXadW0jFKH5BKwAeCq+Den7s+3zfRJzA61/Uj/9H/VzLKTx9jFPPdXeeP+L7WEvDLAKAIoF8bPTKT0+TM7W8ePj3Rz/Yn3kOAp2f1Kf0Weony7pn/cPydvhQYV+eFOfmOu7VB/ViPe34/EN3RFHY/yRuT8ddCtMPH/McBAT5s+vRde/gf2c/sPsjLK+m5IBQF5tO+h2tTlBGnP6693JdsvofjOPnnEHkh2TnV/X1fBl9S5zrwuwF8NFrAVJVwCAPTe8gaJlomqlp0pv4Pjn98tJ/t/fL++6unpR1YGC2n/KCoa0tTLoKiEeUPDl94nj+5/Tv3/eT5vBQ60X1S0oZr+IWRR8Ldhu7AlLjPISlJcO9vrFotky9SpzDequlwEir5beYAc0R7D9KS1DXva0jhYRDXoExPdc6yw5GShkZXe9QdO/uOvHofxjrV/TNS6iMJS+4TcSTgk9n5agJdBQbB//IfF/HpvPt3Tbi7b6I6K0R72p6ajryEJrENW2bbeVUGjfgoals4L443c7BEE4mJO2SpbRngxQrAKRudRzGQ8jVOL2qDVjjI8K1gc3TIJ5KiFZ1q+gdsARPB4NQS4AjwVSt72DSoXNyOWUrU5mQ9nRYyjp89Xo7oRI6Bga9QNT1mQ/ptaJq5T/7WcgAZywR/XlPGAUDdet3LE+qS0TI+g+aJU8MIqjo0Kx8Ly+maxLjJmjQ18rA0YCkxLQbUZP1WqdmyQGJLUm7VnQFqodmXSqmRrdVpqdzk5LvmvgtEcW8PMGdaS23EOWyDVbACZzUJPaqMbjDxpA3Qrgl0AikimGDbqmyT8P8NOYiqrldF8rX+YN7TopX4UoHuSCYY7cgX4gHwclQKl1zhx0THf+tCAUValzjI7Wg9EhptrkIcfIJjA94evOn8B2eHaVzvBrnl2ig0So6hvPaz0IGcOvTHvUIlE2+prqAxLSQxZlU2stql1NqCCLdIiIN/i1DBEHUoElM9dBravbiAnKqgpi4IBkw+utSPIoBijDXJipSVV7MpOEJUAc5Qmm3BnUN+w3hteEieYKfRZSIUcXKMVf0u5wD4EwsUNVvZOtUT7A2GkffHjByWpHqvRBYrTV72a6j8zZ6W0DTE86Hn04bmyWX3Ri9WH7ZU6Q7h+ZHo0nHUAcsQvVhXRDZHChwiyi/hnPuOsSEF6Exk3o6Y9DT1eZ+6cASXk2Y9k+6EOQMDGm6WBK10wOQJCBwren86cPPWUcRAnTVjGcU1LBgs9FURiX/e6479yZcLwCBmTxiawEwrOcleuu12t3tbLv/N4RLYIBhYexm7Fcn4OJcn0+zc+s8/VfPeddZHAGN6TT8eGczHdR/Gts1/MzDkThr23zqrVfAMFT33Nx1RJsx1k5zuWILLnG/vsH+Fv5D4NTVcp1Gzo8AAAAAElFTkSuQmCC&labelColor=white)](https://huggingface.co/spaces/opendatalab/MinerU)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/myhloli/3b3a00a4a0a61577b6c30f989092d20d/mineru_demo.ipynb)
[![arXiv](https://img.shields.io/badge/arXiv-2409.18839-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2409.18839)
[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/opendatalab/MinerU)
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<br>
<br>
🚀<a href="https://mineru.net/?source=github">Access MinerU Now→✅ Zero-Install Web Version ✅ Full-Featured Desktop Client ✅ Instant API Access; Skip deployment headaches – get all product formats in one click. Developers, dive in!</a>
🚀<a href="https://mineru.net/?source=github">MinerU Official Website→✅ Zero-Install Online Version ✅ Full-Featured Client ✅ Developer API Online Access, skip deployment hassles, get all product formats with one click, go fast!</a>
</p>
<!-- join us -->
......@@ -38,28 +35,34 @@
<p align="center">
👋 join us on <a href="https://discord.gg/Tdedn9GTXq" target="_blank">Discord</a> and <a href="http://mineru.space/s/V85Yl" target="_blank">WeChat</a>
</p>
</div>
## Project Introduction
MinerU is a tool that converts PDFs into machine-readable formats (e.g., markdown, JSON), allowing for easy extraction into any format.
MinerU was born during the pre-training process of [InternLM](https://github.com/InternLM/InternLM). We focus on solving symbol conversion issues in scientific literature and hope to contribute to technological development in the era of large models.
Compared to well-known commercial products, MinerU is still young. If you encounter any issues or if the results are not as expected, please submit an issue on [issue](https://github.com/opendatalab/MinerU/issues) and **attach the relevant PDF**.
Compared to well-known commercial products domestically and internationally, MinerU is still young. If you encounter any issues or if the results are not as expected, please submit an issue on [GitHub Issues](https://github.com/opendatalab/MinerU/issues) and **attach the relevant PDF**.
![type:video](https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c)
## Key Features
- Remove headers, footers, footnotes, page numbers, etc., to ensure semantic coherence.
- Output text in human-readable order, suitable for single-column, multi-column, and complex layouts.
- Preserve the structure of the original document, including headings, paragraphs, lists, etc.
- Extract images, image descriptions, tables, table titles, and footnotes.
- Automatically recognize and convert formulas in the document to LaTeX format.
- Automatically recognize and convert tables in the document to HTML format.
- Automatically detect scanned PDFs and garbled PDFs and enable OCR functionality.
- OCR supports detection and recognition of 84 languages.
- Supports multiple output formats, such as multimodal and NLP Markdown, JSON sorted by reading order, and rich intermediate formats.
- Supports various visualization results, including layout visualization and span visualization, for efficient confirmation of output quality.
- Supports running in a pure CPU environment, and also supports GPU(CUDA)/NPU(CANN)/MPS acceleration
- Compatible with Windows, Linux, and Mac platforms.
\ No newline at end of file
- Remove headers, footers, footnotes, page numbers and other elements to ensure semantic coherence
- Output text in human reading order, suitable for single-column, multi-column and complex layouts
- Retain the original document structure, including titles, paragraphs, lists, etc.
- Extract images, image descriptions, tables, table titles and footnotes
- Automatically identify and convert formulas in documents to LaTeX format
- Automatically identify and convert tables in documents to HTML format
- Automatically detect scanned PDFs and garbled PDFs, and enable OCR functionality
- OCR supports detection and recognition of 84 languages
- Support multiple output formats, such as multimodal and NLP Markdown, reading-order-sorted JSON, and information-rich intermediate formats
- Support multiple visualization results, including layout visualization, span visualization, etc., for efficient confirmation of output effects and quality inspection
- Support pure CPU environment operation, and support GPU(CUDA)/NPU(CANN)/MPS acceleration
- Compatible with Windows, Linux and Mac platforms
## User Guide
- [Quick Start Guide](./quick_start/index.md)
- [Detailed Usage Instructions](./usage/index.md)
docs/en/quick_start/docker_deployment.md 0 → 100644
View file @ 406b8ea9
# Deploying MinerU with Docker
MinerU provides a convenient Docker deployment method, which helps quickly set up the environment and solve some tricky environment compatibility issues.
## Build Docker Image using Dockerfile:
```bash
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/china/Dockerfile
docker build -t mineru-sglang:latest -f Dockerfile .
```
> [!TIP]
> The [Dockerfile](https://github.com/opendatalab/MinerU/blob/master/docker/china/Dockerfile) uses `lmsysorg/sglang:v0.4.8.post1-cu126` as the base image by default, supporting Turing/Ampere/Ada Lovelace/Hopper platforms.
> If you are using the newer `Blackwell` platform, please modify the base image to `lmsysorg/sglang:v0.4.8.post1-cu128-b200` before executing the build operation.
## Docker Description
MinerU's Docker uses `lmsysorg/sglang` as the base image, so it includes the `sglang` inference acceleration framework and necessary dependencies by default. Therefore, on compatible devices, you can directly use `sglang` to accelerate VLM model inference.
> [!NOTE]
> Requirements for using `sglang` to accelerate VLM model inference:
> - Device must have Turing architecture or later graphics cards with 8GB+ available VRAM.
> - The host machine's graphics driver should support CUDA 12.6 or higher; `Blackwell` platform should support CUDA 12.8 or higher. You can check the driver version using the `nvidia-smi` command.
> - Docker container must have access to the host machine's graphics devices.
>
> If your device doesn't meet the above requirements, you can still use other features of MinerU, but cannot use `sglang` to accelerate VLM model inference, meaning you cannot use the `vlm-sglang-engine` backend or start the `vlm-sglang-server` service.
## Start Docker Container:
```bash
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 -p 7860:7860 -p 8000:8000 \
--ipc=host \
-it mineru-sglang:latest \
/bin/bash
```
After executing this command, you will enter the Docker container's interactive terminal with some ports mapped for potential services. You can directly run MinerU-related commands within the container to use MinerU's features.
You can also directly start MinerU services by replacing `/bin/bash` with service startup commands. For detailed instructions, please refer to the [MinerU Usage Documentation](../usage/index.md).
## Start Services Directly with Docker Compose
We provide a `compose.yml` file that you can use to quickly start MinerU services.
```bash
# Download compose.yaml file
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
```
- Start `sglang-server` service and connect to `sglang-server` via `vlm-sglang-client` backend:
```bash
docker compose -f compose.yaml --profile mineru-sglang-server up -d
# In another terminal, connect to sglang server via sglang client (only requires CPU and network, no sglang environment needed)
mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://<server_ip>:30000
```
- Start API service:
```bash
docker compose -f compose.yaml --profile mineru-api up -d
```
Access `http://<server_ip>:8000/docs` in your browser to view the API documentation.
- Start Gradio WebUI service:
```bash
docker compose -f compose.yaml --profile mineru-gradio up -d
```
Access `http://<server_ip>:7860` in your browser to use the Gradio WebUI or access `http://<server_ip>:7860/?view=api` to use the Gradio API.
docs/en/quick_start/extension_modules.md 0 → 100644
View file @ 406b8ea9
# MinerU Extension Modules Installation Guide
MinerU supports installing extension modules on demand based on different needs to enhance functionality or support specific model backends.
## Common Scenarios
### Core Functionality Installation
The `core` module is the core dependency of MinerU, containing all functional modules except `sglang`. Installing this module ensures the basic functionality of MinerU works properly.
```bash
uv pip install mineru[core]
```
---
### Using `sglang` to Accelerate VLM Model Inference
The `sglang` module provides acceleration support for VLM model inference, suitable for graphics cards with Turing architecture and later (8GB+ VRAM). Installing this module can significantly improve model inference speed.
In the configuration, `all` includes both `core` and `sglang` modules, so `mineru[all]` and `mineru[core,sglang]` are equivalent.
```bash
uv pip install mineru[all]
```
> [!TIP]
> If exceptions occur during installation of the complete package including sglang, please refer to the [sglang official documentation](https://docs.sglang.ai/start/install.html) to try to resolve the issue, or directly use the [Docker](./docker_deployment.md) deployment method.
---
### Installing Lightweight Client to Connect to sglang-server
If you need to install a lightweight client on edge devices to connect to `sglang-server`, you can install the basic mineru package, which is very lightweight and suitable for devices with only CPU and network connectivity.
```bash
uv pip install mineru
```
---
### Using Pipeline Backend on Outdated Linux Systems
If your system is too outdated to meet the dependency requirements of `mineru[core]`, this option can minimally meet MinerU's runtime requirements, suitable for old systems that cannot be upgraded and only need to use the pipeline backend.
```bash
uv pip install mineru[pipeline_old_linux]
```
docs/en/quick_start/index.md
View file @ 406b8ea9
# Quick Start
If you encounter any installation issues, please first consult the [FAQ](../FAQ/index.md).
If you encounter any installation issues, please check the [FAQ](../FAQ/index.md) first.
## Online Experience
If the parsing results are not as expected, refer to the [Known Issues](../known_issues.md).
- Official online demo: The official online version has the same functionality as the client, with a beautiful interface and rich features, requires login to use
- [![OpenDataLab](https://img.shields.io/badge/Demo_on_OpenDataLab-blue?logo=data:image/svg+xml;base64,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&labelColor=white)](https://mineru.net/OpenSourceTools/Extractor?source=github)
- Gradio-based online demo: A WebUI developed based on Gradio, with a simple interface and only core parsing functionality, no login required
- [![ModelScope](https://img.shields.io/badge/Demo_on_ModelScope-purple?logo=data:image/svg+xml;base64,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&labelColor=white)](https://www.modelscope.cn/studios/OpenDataLab/MinerU)
- [![HuggingFace](https://img.shields.io/badge/Demo_on_HuggingFace-yellow.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/spaces/opendatalab/MinerU)
There are three different ways to experience MinerU:
- [Online Demo](online_demo.md)
- [Local Deployment](local_deployment.md)
## Local Deployment
> [!WARNING]
> **Pre-installation Notice—Hardware and Software Environment Support**
>
> To ensure the stability and reliability of the project, we only optimize and test for specific hardware and software environments during development. This ensures that users deploying and running the project on recommended system configurations will get the best performance with the fewest compatibility issues.
> **Prerequisites - Hardware and Software Environment Support**
>
> To ensure the stability and reliability of the project, we have optimized and tested only specific hardware and software environments during development. This ensures that users can achieve optimal performance and encounter the fewest compatibility issues when deploying and running the project on recommended system configurations.
>
> By focusing resources on the mainline environment, our team can more efficiently resolve potential bugs and develop new features.
> By concentrating our resources and efforts on mainstream environments, our team can more efficiently resolve potential bugs and timely develop new features.
>
> In non-mainline environments, due to the diversity of hardware and software configurations, as well as third-party dependency compatibility issues, we cannot guarantee 100% project availability. Therefore, for users who wish to use this project in non-recommended environments, we suggest carefully reading the documentation and FAQ first. Most issues already have corresponding solutions in the FAQ. We also encourage community feedback to help us gradually expand support.
> In non-mainstream environments, due to the diversity of hardware and software configurations, as well as compatibility issues with third-party dependencies, we cannot guarantee 100% usability of the project. Therefore, for users who wish to use this project in non-recommended environments, we suggest carefully reading the documentation and FAQ first, as most issues have corresponding solutions in the FAQ. Additionally, we encourage community feedback on issues so that we can gradually expand our support range.
<table>
<tr>
......@@ -30,9 +31,9 @@ There are three different ways to experience MinerU:
</tr>
<tr>
<td>Operating System</td>
<td>windows/linux/mac</td>
<td>windows/linux</td>
<td>windows(wsl2)/linux</td>
<td>Linux / Windows / macOS</td>
<td>Linux / Windows</td>
<td>Linux / Windows (via WSL2)</td>
</tr>
<tr>
<td>CPU Inference Support</td>
......@@ -41,12 +42,12 @@ There are three different ways to experience MinerU:
</tr>
<tr>
<td>GPU Requirements</td>
<td>Turing architecture or later, 6GB+ VRAM or Apple Silicon</td>
<td colspan="2">Ampere architecture or later, 8GB+ VRAM</td>
<td>Turing architecture and later, 6GB+ VRAM or Apple Silicon</td>
<td colspan="2">Turing architecture and later, 8GB+ VRAM</td>
</tr>
<tr>
<td>Memory Requirements</td>
<td colspan="3">Minimum 16GB+, 32GB+ recommended</td>
<td colspan="3">Minimum 16GB+, recommended 32GB+</td>
</tr>
<tr>
<td>Disk Space Requirements</td>
......@@ -56,4 +57,32 @@ There are three different ways to experience MinerU:
<td>Python Version</td>
<td colspan="3">3.10-3.13</td>
</tr>
</table>
\ No newline at end of file
</table>
### Install MinerU
#### Install MinerU using pip or uv
```bash
pip install --upgrade pip
pip install uv
uv pip install -U "mineru[core]"
```
#### Install MinerU from source code
```bash
git clone https://github.com/opendatalab/MinerU.git
cd MinerU
uv pip install -e .[core]
```
> [!TIP]
> `mineru[core]` includes all core features except `sglang` acceleration, compatible with Windows / Linux / macOS systems, suitable for most users.
> If you need to use `sglang` acceleration for VLM model inference or install a lightweight client on edge devices, please refer to the documentation [Extension Modules Installation Guide](./extension_modules.md).
---
#### Deploy MinerU using Docker
MinerU provides a convenient Docker deployment method, which helps quickly set up the environment and solve some tricky environment compatibility issues.
You can get the [Docker Deployment Instructions](./docker_deployment.md) in the documentation.
---
docs/en/usage/advanced_cli_parameters.md 0 → 100644
View file @ 406b8ea9
# Advanced Command Line Parameters
## SGLang Acceleration Parameter Optimization
### Memory Optimization Parameters
> [!TIP]
> SGLang acceleration mode currently supports running on Turing architecture graphics cards with a minimum of 8GB VRAM, but graphics cards with <24GB VRAM may encounter insufficient memory issues. You can optimize memory usage with the following parameters:
> - If you encounter insufficient VRAM when using a single graphics card, you may need to reduce the KV cache size with `--mem-fraction-static 0.5`. If VRAM issues persist, try reducing it further to `0.4` or lower.
> - If you have two or more graphics cards, you can try using tensor parallelism (TP) mode to simply expand available VRAM: `--tp-size 2`
### Performance Optimization Parameters
> [!TIP]
> If you can already use SGLang normally for accelerated VLM model inference but still want to further improve inference speed, you can try the following parameters:
> - If you have multiple graphics cards, you can use SGLang's multi-card parallel mode to increase throughput: `--dp-size 2`
> - You can also enable `torch.compile` to accelerate inference speed by approximately 15%: `--enable-torch-compile`
### Parameter Passing Instructions
> [!TIP]
> - If you want to learn more about `sglang` parameter usage, please refer to the [SGLang official documentation](https://docs.sglang.ai/backend/server_arguments.html#common-launch-commands)
> - All officially supported SGLang parameters can be passed to MinerU through command line arguments, including the following commands: `mineru`, `mineru-sglang-server`, `mineru-gradio`, `mineru-api`
## GPU Device Selection and Configuration
### CUDA_VISIBLE_DEVICES Basic Usage
> [!TIP]
> - In any situation, you can specify visible GPU devices by adding the `CUDA_VISIBLE_DEVICES` environment variable at the beginning of the command line. For example:
> ```bash
> CUDA_VISIBLE_DEVICES=1 mineru -p <input_path> -o <output_path>
> ```
> - This specification method is effective for all command line calls, including `mineru`, `mineru-sglang-server`, `mineru-gradio`, and `mineru-api`, and applies to both `pipeline` and `vlm` backends.
### Common Device Configuration Examples
> [!TIP]
> - Here are some common `CUDA_VISIBLE_DEVICES` setting examples:
> ```bash
> CUDA_VISIBLE_DEVICES=1 Only device 1 will be seen
> CUDA_VISIBLE_DEVICES=0,1 Devices 0 and 1 will be visible
> CUDA_VISIBLE_DEVICES="0,1" Same as above, quotation marks are optional
> CUDA_VISIBLE_DEVICES=0,2,3 Devices 0, 2, 3 will be visible; device 1 is masked
> CUDA_VISIBLE_DEVICES="" No GPU will be visible
> ```
### Practical Application Scenarios
> [!TIP]
> Here are some possible usage scenarios:
> - If you have multiple graphics cards and need to specify cards 0 and 1, using multi-card parallelism to start 'sglang-server', you can use the following command:
> ```bash
> CUDA_VISIBLE_DEVICES=0,1 mineru-sglang-server --port 30000 --dp-size 2
> ```
> - If you have multiple graphics cards and need to start two `fastapi` services on cards 0 and 1, listening on different ports respectively, you can use the following commands:
> ```bash
> # In terminal 1
> CUDA_VISIBLE_DEVICES=0 mineru-api --host 127.0.0.1 --port 8000
> # In terminal 2
> CUDA_VISIBLE_DEVICES=1 mineru-api --host 127.0.0.1 --port 8001
> ```
docs/en/usage/cli_tools.md 0 → 100644
View file @ 406b8ea9
# Command Line Tools Usage Instructions
## View Help Information
To view help information for MinerU command line tools, you can use the `--help` parameter. Here are help information examples for various command line tools:
```bash
mineru --help
Usage: mineru [OPTIONS]
Options:
-v, --version Show version and exit
-p, --path PATH Input file path or directory (required)
-o, --output PATH Output directory (required)
-m, --method [auto|txt|ocr] Parsing method: auto (default), txt, ocr (pipeline backend only)
-b, --backend [pipeline|vlm-transformers|vlm-sglang-engine|vlm-sglang-client]
Parsing backend (default: pipeline)
-l, --lang [ch|ch_server|ch_lite|en|korean|japan|chinese_cht|ta|te|ka|latin|arabic|east_slavic|cyrillic|devanagari]
Specify document language (improves OCR accuracy, pipeline backend only)
-u, --url TEXT Service address when using sglang-client
-s, --start INTEGER Starting page number for parsing (0-based)
-e, --end INTEGER Ending page number for parsing (0-based)
-f, --formula BOOLEAN Enable formula parsing (default: enabled)
-t, --table BOOLEAN Enable table parsing (default: enabled)
-d, --device TEXT Inference device (e.g., cpu/cuda/cuda:0/npu/mps, pipeline backend only)
--vram INTEGER Maximum GPU VRAM usage per process (GB) (pipeline backend only)
--source [huggingface|modelscope|local]
Model source, default: huggingface
--help Show help information
```
```bash
mineru-api --help
Usage: mineru-api [OPTIONS]
Options:
--host TEXT Server host (default: 127.0.0.1)
--port INTEGER Server port (default: 8000)
--reload Enable auto-reload (development mode)
--help Show this message and exit.
```
```bash
mineru-gradio --help
Usage: mineru-gradio [OPTIONS]
Options:
--enable-example BOOLEAN Enable example files for input. The example
files to be input need to be placed in the
`example` folder within the directory where
the command is currently executed.
--enable-sglang-engine BOOLEAN Enable SgLang engine backend for faster
processing.
--enable-api BOOLEAN Enable gradio API for serving the
application.
--max-convert-pages INTEGER Set the maximum number of pages to convert
from PDF to Markdown.
--server-name TEXT Set the server name for the Gradio app.
--server-port INTEGER Set the server port for the Gradio app.
--latex-delimiters-type [a|b|all]
Set the type of LaTeX delimiters to use in
Markdown rendering: 'a' for type '$', 'b' for
type '()[]', 'all' for both types.
--help Show this message and exit.
```
## Environment Variables Description
Some parameters of MinerU command line tools have equivalent environment variable configurations. Generally, environment variable configurations have higher priority than command line parameters and take effect across all command line tools.
- `MINERU_DEVICE_MODE`: Used to specify inference device, supports device types like `cpu/cuda/cuda:0/npu/mps`, only effective for `pipeline` backend.
- `MINERU_VIRTUAL_VRAM_SIZE`: Used to specify maximum GPU VRAM usage per process (GB), only effective for `pipeline` backend.
- `MINERU_MODEL_SOURCE`: Used to specify model source, supports `huggingface/modelscope/local`, defaults to `huggingface`, can be switched to `modelscope` or local models through environment variables.
- `MINERU_TOOLS_CONFIG_JSON`: Used to specify configuration file path, defaults to `mineru.json` in user directory, can specify other configuration file paths through environment variables.
- `MINERU_FORMULA_ENABLE`: Used to enable formula parsing, defaults to `true`, can be set to `false` through environment variables to disable formula parsing.
- `MINERU_TABLE_ENABLE`: Used to enable table parsing, defaults to `true`, can be set to `false` through environment variables to disable table parsing.
docs/en/usage/index.md
View file @ 406b8ea9
# Using MinerU
## Command Line Usage
### Basic Usage
The simplest command line invocation is:
```bash
mineru -p <input_path> -o <output_path>
```
- `<input_path>`: Local PDF/Image file or directory (supports pdf/png/jpg/jpeg/webp/gif)
- `<output_path>`: Output directory
### View Help Information
Get all available parameter descriptions:
## Quick Model Source Configuration
MinerU uses `huggingface` as the default model source. If users cannot access `huggingface` due to network restrictions, they can conveniently switch the model source to `modelscope` through environment variables:
```bash
mineru --help
```
### Parameter Details
```text
Usage: mineru [OPTIONS]
Options:
-v, --version Show version and exit
-p, --path PATH Input file path or directory (required)
-o, --output PATH Output directory (required)
-m, --method [auto|txt|ocr] Parsing method: auto (default), txt, ocr (pipeline backend only)
-b, --backend [pipeline|vlm-transformers|vlm-sglang-engine|vlm-sglang-client]
Parsing backend (default: pipeline)
-l, --lang [ch|ch_server|ch_lite|en|korean|japan|chinese_cht|ta|te|ka|latin|arabic|east_slavic|cyrillic|devanagari]
Specify document language (improves OCR accuracy, pipeline backend only)
-u, --url TEXT Service address when using sglang-client
-s, --start INTEGER Starting page number (0-based)
-e, --end INTEGER Ending page number (0-based)
-f, --formula BOOLEAN Enable formula parsing (default: on)
-t, --table BOOLEAN Enable table parsing (default: on)
-d, --device TEXT Inference device (e.g., cpu/cuda/cuda:0/npu/mps, pipeline backend only)
--vram INTEGER Maximum GPU VRAM usage per process (GB)(pipeline backend only)
--source [huggingface|modelscope|local]
Model source, default: huggingface
--help Show help information
export MINERU_MODEL_SOURCE=modelscope
```
For more information about model source configuration and custom local model paths, please refer to the [Model Source Documentation](./model_source.md) in the documentation.
---
## Model Source Configuration
MinerU automatically downloads required models from HuggingFace on first run. If HuggingFace is inaccessible, you can switch model sources:
### Switch to ModelScope Source
```bash
mineru -p <input_path> -o <output_path> --source modelscope
```
Or set environment variable:
## Quick Usage via Command Line
MinerU has built-in command line tools that allow users to quickly use MinerU for PDF parsing through the command line:
```bash
export MINERU_MODEL_SOURCE=modelscope
# Default parsing using pipeline backend
mineru -p <input_path> -o <output_path>
```
- `<input_path>`: Local PDF/image file or directory
- `<output_path>`: Output directory
### Using Local Models
#### 1. Download Models Locally
```bash
mineru-models-download --help
```
Or use interactive command-line tool to select models:
```bash
mineru-models-download
```
After download, model paths will be displayed in current terminal and automatically written to `mineru.json` in user directory.
> [!NOTE]
> The command line tool will automatically attempt cuda/mps acceleration on Linux and macOS systems. Windows users who need cuda acceleration should visit the [PyTorch official website](https://pytorch.org/get-started/locally/) to select the appropriate command for their cuda version to install acceleration-enabled `torch` and `torchvision`.
#### 2. Parse Using Local Models
> [!TIP]
> For more information about output files, please refer to [Output File Documentation](./output_file.md).
```bash
mineru -p <input_path> -o <output_path> --source local
# Or specify vlm backend for parsing
mineru -p <input_path> -o <output_path> -b vlm-transformers
```
> [!TIP]
> The vlm backend additionally supports `sglang` acceleration. Compared to the `transformers` backend, `sglang` can achieve 20-30x speedup. You can check the installation method for the complete package supporting `sglang` acceleration in the [Extension Modules Installation Guide](../quick_start/extension_modules.md).
Or enable via environment variable:
```bash
export MINERU_MODEL_SOURCE=local
mineru -p <input_path> -o <output_path>
```
If you need to adjust parsing options through custom parameters, you can also check the more detailed [Command Line Tools Usage Instructions](./cli_tools.md) in the documentation.
---
## Using sglang to Accelerate VLM Model Inference
### Through the sglang-engine Mode
```bash
mineru -p <input_path> -o <output_path> -b vlm-sglang-engine
```
### Through the sglang-server/client Mode
1. Start Server:
```bash
mineru-sglang-server --port 30000
```
2. Use Client in another terminal:
```bash
mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://127.0.0.1:30000
```
## Advanced Usage via API, WebUI, sglang-client/server
- Direct Python API calls: [Python Usage Example](https://github.com/opendatalab/MinerU/blob/master/demo/demo.py)
- FastAPI calls:
```bash
mineru-api --host 127.0.0.1 --port 8000
```
Access http://127.0.0.1:8000/docs in your browser to view the API documentation.
- Start Gradio WebUI visual frontend:
```bash
# Using pipeline/vlm-transformers/vlm-sglang-client backends
mineru-gradio --server-name 127.0.0.1 --server-port 7860
# Or using vlm-sglang-engine/pipeline backends (requires sglang environment)
mineru-gradio --server-name 127.0.0.1 --server-port 7860 --enable-sglang-engine true
```
Access http://127.0.0.1:7860 in your browser to use Gradio WebUI or access http://127.0.0.1:7860/?view=api to use the Gradio API.
- Using `sglang-client/server` method:
```bash
# Start sglang server (requires sglang environment)
mineru-sglang-server --port 30000
# In another terminal, connect to sglang server via sglang client (only requires CPU and network, no sglang environment needed)
mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://127.0.0.1:30000
```
> [!TIP]
> For more information about output files, please refer to [Output File Documentation](../output_file.md)
> All officially supported sglang parameters can be passed to MinerU through command line arguments, including the following commands: `mineru`, `mineru-sglang-server`, `mineru-gradio`, `mineru-api`.
> We have compiled some commonly used parameters and usage methods for `sglang`, which can be found in the documentation [Advanced Command Line Parameters](./advanced_cli_parameters.md).
## Extending MinerU Functionality with Configuration Files
---
\ No newline at end of file
- MinerU is now ready to use out of the box, but also supports extending functionality through configuration files. You can create a `mineru.json` file in your user directory to add custom configurations.
- The `mineru.json` file will be automatically generated when you use the built-in model download command `mineru-models-download`, or you can create it by copying the [configuration template file](https://github.com/opendatalab/MinerU/blob/master/mineru.template.json) to your user directory and renaming it to `mineru.json`.
- Here are some available configuration options:
- `latex-delimiter-config`: Used to configure LaTeX formula delimiters, defaults to `$` symbol, can be modified to other symbols or strings as needed.
- `llm-aided-config`: Used to configure parameters for LLM-assisted title hierarchy, compatible with all LLM models supporting `openai protocol`, defaults to using Alibaba Cloud Bailian's `qwen2.5-32b-instruct` model. You need to configure your own API key and set `enable` to `true` to enable this feature.
- `models-dir`: Used to specify local model storage directory, please specify model directories for `pipeline` and `vlm` backends separately. After specifying the directory, you can use local models by configuring the environment variable `export MINERU_MODEL_SOURCE=local`.
docs/en/usage/model_source.md 0 → 100644
View file @ 406b8ea9
# Model Source Documentation
MinerU uses `HuggingFace` and `ModelScope` as model repositories. Users can switch model sources or use local models as needed.
- `HuggingFace` is the default model source, providing excellent loading speed and high stability globally.
- `ModelScope` is the best choice for users in mainland China, providing seamlessly compatible `hf` SDK modules, suitable for users who cannot access HuggingFace.
## Methods to Switch Model Sources
### Switch via Command Line Parameters
Currently, only the `mineru` command line tool supports switching model sources through command line parameters. Other command line tools such as `mineru-api`, `mineru-gradio`, etc., do not support this yet.
```bash
mineru -p <input_path> -o <output_path> --source modelscope
```
### Switch via Environment Variables
You can switch model sources by setting environment variables in any situation. This applies to all command line tools and API calls.
```bash
export MINERU_MODEL_SOURCE=modelscope
```
or
```python
import os
os.environ["MINERU_MODEL_SOURCE"] = "modelscope"
```
>[!TIP]
> Model sources set through environment variables will take effect in the current terminal session until the terminal is closed or the environment variable is modified. They have higher priority than command line parameters - if both command line parameters and environment variables are set, the command line parameters will be ignored.
## Using Local Models
### 1. Download Models to Local Storage
```bash
mineru-models-download --help
```
or use the interactive command line tool to select model downloads:
```bash
mineru-models-download
```
>[!TIP]
>- After download completion, the model path will be output in the current terminal window and automatically written to `mineru.json` in the user directory.
>- After downloading models locally, you can freely move the model folder to other locations while updating the model path in `mineru.json`.
>- If you deploy the model folder to another server, please ensure you move the `mineru.json` file to the user directory of the new device and configure the model path correctly.
>- If you need to update model files, you can run the `mineru-models-download` command again. Model updates do not support custom paths currently - if you haven't moved the local model folder, model files will be incrementally updated; if you have moved the model folder, model files will be re-downloaded to the default location and `mineru.json` will be updated.
### 2. Use Local Models for Parsing
```bash
mineru -p <input_path> -o <output_path> --source local
```
or enable through environment variables:
```bash
export MINERU_MODEL_SOURCE=local
mineru -p <input_path> -o <output_path>
```
docs/mineru.template.json deleted 100644 → 0
View file @ fbc8d21d
{
"bucket_info":{
"bucket-name-1":["ak", "sk", "endpoint"],
"bucket-name-2":["ak", "sk", "endpoint"]
},
"latex-delimiter-config": {
"display": {
"left": "$$",
"right": "$$"
},
"inline": {
"left": "$",
"right": "$"
}
},
"llm-aided-config": {
"title_aided": {
"api_key": "your_api_key",
"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
"model": "qwen2.5-32b-instruct",
"enable": false
}
},
"models-dir": {
"pipeline": "",
"vlm": ""
},
"config_version": "1.3.0"
}
\ No newline at end of file
docs/zh/FAQ/index.md
View file @ 406b8ea9
# 常见问题解答
## 1.在WSL2的Ubuntu22.04中遇到报错`ImportError: libGL.so.1: cannot open shared object file: No such file or directory`
如果未能列出您的问题，您也可以使用[DeepWiki](https://deepwiki.com/opendatalab/MinerU)与AI助手交流，这可以解决大部分常见问题。
如果您仍然无法解决问题，您可通过[Discord](https://discord.gg/Tdedn9GTXq)或[WeChat](http://mineru.space/s/V85Yl)加入社区，与其他用户和开发者交流。
### 1. 在WSL2的Ubuntu22.04中遇到报错`ImportError: libGL.so.1: cannot open shared object file: No such file or directory`
WSL2的Ubuntu22.04中缺少`libgl`库，可通过以下命令安装`libgl`库解决：
......@@ -11,7 +15,7 @@ sudo apt-get install libgl1-mesa-glx
参考：https://github.com/opendatalab/MinerU/issues/388
## 2.在 CentOS 7 或 Ubuntu 18 系统安装MinerU时报错`ERROR: Failed building wheel for simsimd`
### 2. 在 CentOS 7 或 Ubuntu 18 系统安装MinerU时报错`ERROR: Failed building wheel for simsimd`
新版本albumentations(1.4.21)引入了依赖simsimd,由于simsimd在linux的预编译包要求glibc的版本大于等于2.28，导致部分2019年之前发布的Linux发行版无法正常安装，可通过如下命令安装:
```
......@@ -21,3 +25,17 @@ pip install -U "mineru[pipeline_old_linux]"
```
参考：https://github.com/opendatalab/MinerU/issues/1004
### 3. 在 Linux 系统安装并使用时，解析结果缺失部份文字信息。
MinerU在>=2.0的版本中使用`pypdfium2`代替`pymupdf`作为PDF页面的渲染引擎，以解决AGPLv3的许可证问题，在某些Linux发行版，由于缺少CJK字体，可能会在将PDF渲染成图片的过程中丢失部份文字。
为了解决这个问题，您可以通过以下命令安装noto字体包，这在Ubuntu/debian系统中有效：
```bash
sudo apt update
sudo apt install fonts-noto-core
sudo apt install fonts-noto-cjk
fc-cache -fv
```
也可以直接使用我们的[Docker部署](../quick_start/docker_deployment.md)方式构建镜像，镜像中默认包含以上字体包。
参考：https://github.com/opendatalab/MinerU/issues/2915
\ No newline at end of file
docs/zh/index.md
View file @ 406b8ea9
......@@ -3,7 +3,7 @@
<p align="center">
<img src="../images/MinerU-logo.png" width="300px" style="vertical-align:middle;">
</p>
</div>
<!-- icon -->
[![stars](https://img.shields.io/github/stars/opendatalab/MinerU.svg)](https://github.com/opendatalab/MinerU)
......@@ -21,15 +21,12 @@
[![arXiv](https://img.shields.io/badge/arXiv-2409.18839-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2409.18839)
[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/opendatalab/MinerU)
<div align="center" xmlns="http://www.w3.org/1999/html">
<a href="https://trendshift.io/repositories/11174" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11174" alt="opendatalab%2FMinerU | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
<!-- hot link -->
<p align="center">
<a href="https://github.com/opendatalab/PDF-Extract-Kit">PDF-Extract-Kit: 高质量PDF解析工具箱</a>🔥🔥🔥
<br>
<br>
🚀<a href="https://mineru.net/?source=github">MinerU 官网入口→✅ 免装在线版 ✅ 全功能客户端 ✅ 开发者API在线调用，省去部署麻烦，多种产品形态一键get，速冲！</a>
</p>
......@@ -62,4 +59,10 @@ MinerU诞生于[书生-浦语](https://github.com/InternLM/InternLM)的预训练
- 支持多种输出格式，如多模态与NLP的Markdown、按阅读顺序排序的JSON、含有丰富信息的中间格式等
- 支持多种可视化结果，包括layout可视化、span可视化等，便于高效确认输出效果与质检
- 支持纯CPU环境运行，并支持 GPU(CUDA)/NPU(CANN)/MPS 加速
- 兼容Windows、Linux和Mac平台
\ No newline at end of file
- 兼容Windows、Linux和Mac平台
## 使用指南
- [快速上手指南](./quick_start/index.md)
- [详细使用说明](./usage/index.md)
\ No newline at end of file
docs/zh/known_issues.md deleted 100644 → 0
View file @ fbc8d21d
# Known Issues
- 阅读顺序基于模型对可阅读内容在空间中的分布进行排序，在极端复杂的排版下可能会部分区域乱序
- 对竖排文字的支持较为有限
- 目录和列表通过规则进行识别，少部分不常见的列表形式可能无法识别
- 代码块在layout模型里还没有支持
- 漫画书、艺术图册、小学教材、习题尚不能很好解析
- 表格识别在复杂表格上可能会出现行/列识别错误
- 在小语种PDF上，OCR识别可能会出现字符不准确的情况（如拉丁文的重音符号、阿拉伯文易混淆字符等）
- 部分公式可能会无法在markdown中渲染
\ No newline at end of file
docs/zh/quick_start/docker_deployment.md 0 → 100644
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# 使用docker部署Mineru
MinerU提供了便捷的docker部署方式，这有助于快速搭建环境并解决一些棘手的环境兼容问题。
## 使用 Dockerfile 构建镜像：
```bash
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/china/Dockerfile
docker build -t mineru-sglang:latest -f Dockerfile .
```
> [!TIP]
> [Dockerfile](https://github.com/opendatalab/MinerU/blob/master/docker/china/Dockerfile)默认使用`lmsysorg/sglang:v0.4.8.post1-cu126`作为基础镜像，支持Turing/Ampere/Ada Lovelace/Hopper平台，
> 如您使用较新的`Blackwell`平台，请将基础镜像修改为`lmsysorg/sglang:v0.4.8.post1-cu128-b200` 再执行build操作。
## Docker说明
Mineru的docker使用了`lmsysorg/sglang`作为基础镜像，因此在docker中默认集成了`sglang`推理加速框架和必需的依赖环境。因此在满足条件的设备上，您可以直接使用`sglang`加速VLM模型推理。
> [!NOTE]
> 使用`sglang`加速VLM模型推理需要满足的条件是：
> - 设备包含Turing及以后架构的显卡，且可用显存大于等于8G。
> - 物理机的显卡驱动应支持CUDA 12.6或更高版本，`Blackwell`平台应支持CUDA 12.8及更高版本，可通过`nvidia-smi`命令检查驱动版本。
> - docker中能够访问物理机的显卡设备。
>
> 如果您的设备不满足上述条件，您仍然可以使用MinerU的其他功能，但无法使用`sglang`加速VLM模型推理，即无法使用`vlm-sglang-engine`后端和启动`vlm-sglang-server`服务。
## 启动 Docker 容器：
```bash
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 -p 7860:7860 -p 8000:8000 \
--ipc=host \
-it mineru-sglang:latest \
/bin/bash
```
执行该命令后，您将进入到Docker容器的交互式终端，并映射了一些端口用于可能会使用的服务，您可以直接在容器内运行MinerU相关命令来使用MinerU的功能。
您也可以直接通过替换`/bin/bash`为服务启动命令来启动MinerU服务，详细说明请参考[MinerU使用文档](../usage/index_back.md)。
## 通过 Docker Compose 直接启动服务
我们提供了`compose.yml`文件，您可以通过它来快速启动MinerU服务。
```bash
# 下载 compose.yaml 文件
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
```
- 启动`sglang-server`服务，并通过`vlm-sglang-client`后端连接`sglang-server`：
```bash
docker compose -f compose.yaml --profile mineru-sglang-server up -d
# 在另一个终端中通过sglang client连接sglang server（只需cpu与网络，不需要sglang环境）
mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://<server_ip>:30000
```
- 启动 API 服务：
```bash
docker compose -f compose.yaml --profile mineru-api up -d
```
在浏览器中访问 `http://<server_ip>:8000/docs` 查看API文档。
- 启动 Gradio WebUI 服务：
```bash
docker compose -f compose.yaml --profile mineru-gradio up -d
```
在浏览器中访问 `http://<server_ip>:7860` 使用 Gradio WebUI 或访问 `http://<server_ip>:7860/?view=api` 使用 Gradio API。
\ No newline at end of file
docs/zh/quick_start/extension_modules.md 0 → 100644
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# MinerU 扩展模块安装指南
MinerU 支持根据不同需求，按需安装扩展模块，以增强功能或支持特定的模型后端。
## 常见场景
### 核心功能安装
`core` 模块是 MinerU 的核心依赖，包含了除`sglang`外的所有功能模块。安装此模块可以确保 MinerU 的基本功能正常运行。
```bash
uv pip install mineru[core]
```
---
### 使用`sglang`加速 VLM 模型推理
`sglang` 模块提供了对 VLM 模型推理的加速支持，适用于具有 Turing 及以后架构的显卡（8G 显存及以上）。安装此模块可以显著提升模型推理速度。
在配置中，`all`包含了`core`和`sglang`模块，因此`mineru[all]`和`mineru[core,sglang]`是等价的。
```bash
uv pip install mineru[all]
```
> [!TIP]
> 如在安装包含sglang的完整包过程中发生异常，请参考 [sglang 官方文档](https://docs.sglang.ai/start/install.html) 尝试解决，或直接使用 [Docker](./docker_deployment.md) 方式部署镜像。
---
### 安装轻量版client连接sglang-server使用
如果您需要在边缘设备上安装轻量版的 client 端以连接 `sglang-server`，可以安装mineru的基础包，非常轻量，适合在只有cpu和网络连接的设备上使用。
```bash
uv pip install mineru
```
---
### 在过时的linux系统上使用pipeline后端
如果您的系统过于陈旧，无法满足`mineru[core]`的依赖要求，该选项可以最低限度的满足 MinerU 的运行需求，适用于老旧系统无法升级且仅需使用 pipeline 后端的场景。
```bash
uv pip install mineru[pipeline_old_linux]
```
\ No newline at end of file
docs/zh/quick_start/index.md
View file @ 406b8ea9
......@@ -2,15 +2,16 @@
如果遇到任何安装问题，请先查询 [FAQ](../FAQ/index.md)
## 在线体验
如果遇到解析效果不及预期，参考 [Known Issues](../known_issues.md)
- 官网在线demo：官网在线版功能与客户端一致，界面美观，功能丰富，需要登录使用
- [![OpenDataLab](https://img.shields.io/badge/Demo_on_OpenDataLab-blue?logo=data:image/svg+xml;base64,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&labelColor=white)](https://mineru.net/OpenSourceTools/Extractor?source=github)
- 基于Gradio的在线demo：基于gradio开发的webui，界面简洁，仅包含核心解析功能，免登录
- [![ModelScope](https://img.shields.io/badge/Demo_on_ModelScope-purple?logo=data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIzIiBoZWlnaHQ9IjIwMCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCiA8Zz4KICA8dGl0bGU+TGF5ZXIgMTwvdGl0bGU+CiAgPHBhdGggaWQ9InN2Z18xNCIgZmlsbD0iIzYyNGFmZiIgZD0ibTAsODkuODRsMjUuNjUsMGwwLDI1LjY0OTk5bC0yNS42NSwwbDAsLTI1LjY0OTk5eiIvPgogIDxwYXRoIGlkPSJzdmdfMTUiIGZpbGw9IiM2MjRhZmYiIGQ9Im05OS4xNCwxMTUuNDlsMjUuNjUsMGwwLDI1LjY1bC0yNS42NSwwbDAsLTI1LjY1eiIvPgogIDxwYXRoIGlkPSJzdmdfMTYiIGZpbGw9IiM2MjRhZmYiIGQ9Im0xNzYuMDksMTQxLjE0bC0yNS42NDk5OSwwbDAsMjIuMTlsNDcuODQsMGwwLC00Ny44NGwtMjIuMTksMGwwLDI1LjY1eiIvPgogIDxwYXRoIGlkPSJzdmdfMTciIGZpbGw9IiMzNmNmZDEiIGQ9Im0xMjQuNzksODkuODRsMjUuNjUsMGwwLDI1LjY0OTk5bC0yNS42NSwwbDAsLTI1LjY0OTk5eiIvPgogIDxwYXRoIGlkPSJzdmdfMTgiIGZpbGw9IiMzNmNmZDEiIGQ9Im0wLDY0LjE5bDI1LjY1LDBsMCwyNS42NWwtMjUuNjUsMGwwLC0yNS42NXoiLz4KICA8cGF0aCBpZD0ic3ZnXzE5IiBmaWxsPSIjNjI0YWZmIiBkPSJtMTk4LjI4LDg5Ljg0bDI1LjY0OTk5LDBsMCwyNS42NDk5OWwtMjUuNjQ5OTksMGwwLC0yNS42NDk5OXoiLz4KICA8cGF0aCBpZD0ic3ZnXzIwIiBmaWxsPSIjMzZjZmQxIiBkPSJtMTk4LjI4LDY0LjE5bDI1LjY0OTk5LDBsMCwyNS42NWwtMjUuNjQ5OTksMGwwLC0yNS42NXoiLz4KICA8cGF0aCBpZD0ic3ZnXzIxIiBmaWxsPSIjNjI0YWZmIiBkPSJtMTUwLjQ0LDQybDAsMjIuMTlsMjUuNjQ5OTksMGwwLDI1LjY1bDIyLjE5LDBsMCwtNDcuODRsLTQ3Ljg0LDB6Ii8+CiAgPHBhdGggaWQ9InN2Z18yMiIgZmlsbD0iIzM2Y2ZkMSIgZD0ibTczLjQ5LDg5Ljg0bDI1LjY1LDBsMCwyNS42NDk5OWwtMjUuNjUsMGwwLC0yNS42NDk5OXoiLz4KICA8cGF0aCBpZD0ic3ZnXzIzIiBmaWxsPSIjNjI0YWZmIiBkPSJtNDcuODQsNjQuMTlsMjUuNjUsMGwwLC0yMi4xOWwtNDcuODQsMGwwLDQ3Ljg0bDIyLjE5LDBsMCwtMjUuNjV6Ii8+CiAgPHBhdGggaWQ9InN2Z18yNCIgZmlsbD0iIzYyNGFmZiIgZD0ibTQ3Ljg0LDExNS40OWwtMjIuMTksMGwwLDQ3Ljg0bDQ3Ljg0LDBsMCwtMjIuMTlsLTI1LjY1LDBsMCwtMjUuNjV6Ii8+CiA8L2c+Cjwvc3ZnPg==&labelColor=white)](https://www.modelscope.cn/studios/OpenDataLab/MinerU)
- [![HuggingFace](https://img.shields.io/badge/Demo_on_HuggingFace-yellow.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/spaces/opendatalab/MinerU)
有2种不同方式可以体验MinerU的效果：
- [在线体验](online_demo.md)
- [本地部署](local_deployment.md)
## 本地部署
> [!WARNING]
> **安装前必看——软硬件环境支持说明**
......@@ -30,9 +31,9 @@
</tr>
<tr>
<td>操作系统</td>
<td>windows/linux/mac</td>
<td>windows/linux</td>
<td>windows(wsl2)/linux</td>
<td>Linux / Windows / macOS</td>
<td>Linux / Windows</td>
<td>Linux / Windows (via WSL2)</td>
</tr>
<tr>
<td>CPU推理支持</td>
......@@ -42,7 +43,7 @@
<tr>
<td>GPU要求</td>
<td>Turing及以后架构，6G显存以上或Apple Silicon</td>
<td colspan="2">Ampere及以后架构，8G显存以上</td>
<td colspan="2">Turing及以后架构，8G显存以上</td>
</tr>
<tr>
<td>内存要求</td>
......@@ -56,4 +57,32 @@
<td>python版本</td>
<td colspan="3">3.10-3.13</td>
</tr>
</table>
\ No newline at end of file
</table>
### 安装 MinerU
#### 使用pip或uv安装MinerU
```bash
pip install --upgrade pip -i https://mirrors.aliyun.com/pypi/simple
pip install uv -i https://mirrors.aliyun.com/pypi/simple
uv pip install -U "mineru[core]" -i https://mirrors.aliyun.com/pypi/simple
```
#### 通过源码安装MinerU
```bash
git clone https://github.com/opendatalab/MinerU.git
cd MinerU
uv pip install -e .[core] -i https://mirrors.aliyun.com/pypi/simple
```
> [!TIP]
> `mineru[core]`包含除`sglang`加速外的所有核心功能，兼容Windows / Linux / macOS系统，适合绝大多数用户。
> 如果您有使用`sglang`加速VLM模型推理，或是在边缘设备安装轻量版client端等需求，可以参考文档[安装扩展模块指南](./extension_modules.md)。
---
#### 使用docker部署Mineru
MinerU提供了便捷的docker部署方式，这有助于快速搭建环境并解决一些棘手的环境兼容问题。
您可以在文档中获取[Docker部署说明](./docker_deployment.md)。
---
\ No newline at end of file
docs/zh/quick_start/local_deployment.md deleted 100644 → 0
View file @ fbc8d21d
# 本地部署
## 安装 MinerU
### 使用 pip 或 uv 安装
```bash
pip install --upgrade pip -i https://mirrors.aliyun.com/pypi/simple
pip install uv -i https://mirrors.aliyun.com/pypi/simple
uv pip install -U "mineru[core]" -i https://mirrors.aliyun.com/pypi/simple
```
### 源码安装
```bash
git clone https://github.com/opendatalab/MinerU.git
cd MinerU
uv pip install -e .[core] -i https://mirrors.aliyun.com/pypi/simple
```
> [!NOTE]
> Linux和macOS系统安装后自动支持cuda/mps加速，Windows用户如需使用cuda加速，
> 请前往 [Pytorch官网](https://pytorch.org/get-started/locally/) 选择合适的cuda版本安装pytorch。
### 安装完整版（支持 sglang 加速）（需确保设备有Turing及以后架构，8G显存及以上显卡）
如需使用 **sglang 加速 VLM 模型推理**，请选择合适的方式安装完整版本：
- 使用uv或pip安装
```bash
uv pip install -U "mineru[all]" -i https://mirrors.aliyun.com/pypi/simple
```
- 从源码安装：
```bash
uv pip install -e .[all] -i https://mirrors.aliyun.com/pypi/simple
```
> [!TIP]
> sglang安装过程中如发生异常，请参考[sglang官方文档](https://docs.sglang.ai/start/install.html)尝试解决或直接使用docker方式安装。
- 使用 Dockerfile 构建镜像：
```bash
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/china/Dockerfile
docker build -t mineru-sglang:latest -f Dockerfile .
```
启动 Docker 容器：
```bash
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
--ipc=host \
mineru-sglang:latest \
mineru-sglang-server --host 0.0.0.0 --port 30000
```
或使用 Docker Compose 启动：
```bash
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
docker compose -f compose.yaml up -d
```
> [!TIP]
> Dockerfile默认使用`lmsysorg/sglang:v0.4.8.post1-cu126`作为基础镜像，支持Turing/Ampere/Ada Lovelace/Hopper平台，
> 如您使用较新的`Blackwell`平台，请将基础镜像修改为`lmsysorg/sglang:v0.4.8.post1-cu128-b200`。
### 安装client（用于在仅需 CPU 和网络连接的边缘设备上连接 sglang-server）
```bash
uv pip install -U mineru -i https://mirrors.aliyun.com/pypi/simple
mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://<host_ip>:<port>
```
---
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# 在线体验
[![OpenDataLab](https://img.shields.io/badge/Demo_on_OpenDataLab-blue?logo=data:image/svg+xml;base64,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&labelColor=white)](https://mineru.net/OpenSourceTools/Extractor?source=github)
[![ModelScope](https://img.shields.io/badge/Demo_on_ModelScope-purple?logo=data:image/svg+xml;base64,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&labelColor=white)](https://www.modelscope.cn/studios/OpenDataLab/MinerU)
[![HuggingFace](https://img.shields.io/badge/Demo_on_HuggingFace-yellow.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/spaces/opendatalab/MinerU)
\ No newline at end of file
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# TODO
- [x] 基于模型的阅读顺序
- [x] 正文中目录、列表识别
- [x] 表格识别
- [x] 标题分级
- [ ] 正文中代码块识别
- [ ] [化学式识别](../chemical_knowledge_introduction/introduction.pdf)
- [ ] 几何图形识别
\ No newline at end of file
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