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Unverified Commit c70c4cc9 authored by Xiaomeng Zhao's avatar Xiaomeng Zhao Committed by GitHub
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Merge pull request #3071 from myhloli/dev 

fix: remove unnecessary horizontal rules for improved readability in documentation
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docs/en/quick_start/docker_deployment.md
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......@@ -13,8 +13,6 @@ docker build -t mineru-sglang:latest -f Dockerfile .
> The [Dockerfile](https://github.com/opendatalab/MinerU/blob/master/docker/global/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.
......@@ -28,8 +26,6 @@ MinerU's Docker uses `lmsysorg/sglang` as the base image, so it includes the `sg
>
> 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
......@@ -44,8 +40,6 @@ docker run --gpus all \
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.yaml](https://github.com/opendatalab/MinerU/blob/master/docker/compose.yaml) file that you can use to quickly start MinerU services.
......
docs/en/usage/advanced_cli_parameters.md
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# Advanced Command Line Parameters
---
## SGLang Acceleration Parameter Optimization
### Memory Optimization Parameters
......@@ -11,8 +9,6 @@
> - 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:
......@@ -20,15 +16,11 @@
> - 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]
> - 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`
> - 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)
---
## GPU Device Selection and Configuration
### CUDA_VISIBLE_DEVICES Basic Usage
......@@ -39,8 +31,6 @@
> ```
> - 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:
......@@ -52,8 +42,6 @@
> CUDA_VISIBLE_DEVICES="" # No GPU will be visible
> ```
---
## Practical Application Scenarios
> [!TIP]
> Here are some possible usage scenarios:
......
docs/en/usage/quick_usage.md
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......@@ -7,8 +7,6 @@ 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.
---
## 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
......@@ -35,8 +33,6 @@ mineru -p <input_path> -o <output_path> -b vlm-transformers
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.
---
## 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)
......@@ -72,8 +68,6 @@ If you need to adjust parsing options through custom parameters, you can also ch
> 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
MinerU is now ready to use out of the box, but also supports extending functionality through configuration files. You can edit `mineru.json` file in your user directory to add custom configurations.
......
docs/zh/quick_start/docker_deployment.md
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......@@ -13,8 +13,6 @@ docker build -t mineru-sglang:latest -f Dockerfile .
> [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。
......@@ -27,8 +25,6 @@ Mineru的docker使用了`lmsysorg/sglang`作为基础镜像，因此在docker中
>
> 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
......@@ -43,8 +39,6 @@ docker run --gpus all \
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.md)。
---
## 通过 Docker Compose 直接启动服务
我们提供了[compose.yml](https://github.com/opendatalab/MinerU/blob/master/docker/compose.yaml)文件，您可以通过它来快速启动MinerU服务。
......
docs/zh/usage/advanced_cli_parameters.md
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# 命令行参数进阶
---
## SGLang 加速参数优化
### 显存优化参数
......@@ -11,8 +9,6 @@
> - 如果您使用单张显卡遇到显存不足的情况时，可能需要调低KV缓存大小，`--mem-fraction-static 0.5`，如仍出现显存不足问题，可尝试进一步降低到`0.4`或更低
> - 如您有两张以上显卡，可尝试通过张量并行（TP）模式简单扩充可用显存：`--tp-size 2`
---
### 性能优化参数
> [!TIP]
> 如果您已经可以正常使用sglang对vlm模型进行加速推理，但仍然希望进一步提升推理速度，可以尝试以下参数：
......@@ -20,15 +16,11 @@
> - 如果您有超过多张显卡，可以使用sglang的多卡并行模式来增加吞吐量：`--dp-size 2`
> - 同时您可以启用`torch.compile`来将推理速度加速约15%：`--enable-torch-compile`
---
### 参数传递说明
> [!TIP]
> - 所有sglang官方支持的参数都可用通过命令行参数传递给 MinerU，包括以下命令:`mineru`、`mineru-sglang-server`、`mineru-gradio`、`mineru-api`
> - 如果您想了解更多有关`sglang`的参数使用方法，请参考 [sglang官方文档](https://docs.sglang.ai/backend/server_arguments.html#common-launch-commands)
---
## GPU 设备选择与配置
### CUDA_VISIBLE_DEVICES 基本用法
......@@ -39,8 +31,6 @@
> ```
> - 这种指定方式对所有的命令行调用都有效，包括 `mineru`、`mineru-sglang-server`、`mineru-gradio` 和 `mineru-api`，且对`pipeline`、`vlm`后端均适用。
---
### 常见设备配置示例
> [!TIP]
> 以下是一些常见的 `CUDA_VISIBLE_DEVICES` 设置示例：
......@@ -52,8 +42,6 @@
> CUDA_VISIBLE_DEVICES="" # No GPU will be visible
> ```
---
## 实际应用场景
> [!TIP]
......
docs/zh/usage/cli_tools.md
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......@@ -31,33 +31,28 @@ 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.
--host TEXT 服务器主机地址（默认：127.0.0.1）
--port INTEGER 服务器端口（默认：8000）
--reload 启用自动重载（开发模式）
--help 显示此帮助信息并退出
```
```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.
--enable-example BOOLEAN 启用示例文件输入(需要将示例文件放置在当前
执行命令目录下的 `example` 文件夹中)
--enable-sglang-engine BOOLEAN 启用 SgLang 引擎后端以提高处理速度
--enable-api BOOLEAN 启用 Gradio API 以提供应用程序服务
--max-convert-pages INTEGER 设置从 PDF 转换为 Markdown 的最大页数
--server-name TEXT 设置 Gradio 应用程序的服务器主机名
--server-port INTEGER 设置 Gradio 应用程序的服务器端口
--latex-delimiters-type [a|b|all]
Set the type of LaTeX delimiters to use in
Markdown rendering:'a' for type '$', 'b' for
type '()[]', 'all' for both types.
--help Show this message and exit.
设置在 Markdown 渲染中使用的 LaTeX 分隔符类型
('a' 表示 '$' 类型，'b' 表示 '()[]' 类型，
'all' 表示两种类型都使用)
--help 显示此帮助信息并退出
```
## 环境变量说明
......@@ -71,5 +66,3 @@ MinerU命令行工具的某些参数存在相同功能的环境变量配置，
- `MINERU_TOOLS_CONFIG_JSON`：用于指定配置文件路径，默认为用户目录下的`mineru.json`，可通过环境变量指定其他配置文件路径。
- `MINERU_FORMULA_ENABLE`：用于启用公式解析，默认为`true`，可通过环境变量设置为`false`来禁用公式解析。
- `MINERU_TABLE_ENABLE`：用于启用表格解析，默认为`true`，可通过环境变量设置为`false`来禁用表格解析。
docs/zh/usage/quick_usage.md
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......@@ -7,8 +7,6 @@ export MINERU_MODEL_SOURCE=modelscope
```
有关模型源配置和自定义本地模型路径的更多信息，请参考文档中的[模型源说明](./model_source.md)。
---
## 通过命令行快速使用
MinerU内置了命令行工具，用户可以通过命令行快速使用MinerU进行PDF解析：
```bash
......@@ -25,7 +23,6 @@ mineru -p <input_path> -o <output_path>
> 命令行工具会在Linux和macOS系统自动尝试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/) 选择适合自己cuda版本的命令安装支持加速的`torch`和`torchvision`。
```bash
# 或指定vlm后端解析
mineru -p <input_path> -o <output_path> -b vlm-transformers
......@@ -35,8 +32,6 @@ mineru -p <input_path> -o <output_path> -b vlm-transformers
如果需要通过自定义参数调整解析选项，您也可以在文档中查看更详细的[命令行工具使用说明](./cli_tools.md)。
---
## 通过api、webui、sglang-client/server进阶使用
- 通过python api直接调用：[Python 调用示例](https://github.com/opendatalab/MinerU/blob/master/demo/demo.py)
......@@ -72,8 +67,6 @@ mineru -p <input_path> -o <output_path> -b vlm-transformers
> 所有sglang官方支持的参数都可用通过命令行参数传递给 MinerU，包括以下命令:`mineru`、`mineru-sglang-server`、`mineru-gradio`、`mineru-api`，
> 我们整理了一些`sglang`使用中的常用参数和使用方法，可以在文档[命令行进阶参数](./advanced_cli_parameters.md)中获取。
---
## 基于配置文件扩展 MinerU 功能
MinerU 现已实现开箱即用，但也支持通过配置文件扩展功能。您可通过编辑用户目录下的 `mineru.json` 文件，添加自定义配置。
......
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