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- Deleted .readthedocs.yaml files from multiple directories
- Removed outdated API and user guide documentation files
- Deleted command line usage examples
- Removed CUDA acceleration guide
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.readthedocs.yaml deleted 100644 → 0
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version: 2
build:
os: ubuntu-22.04
tools:
python: "3.10"
formats:
- epub
python:
install:
- requirements: next_docs/zh_cn/requirements.txt
sphinx:
configuration: next_docs/zh_cn/conf.py
docs/README_Ascend_NPU_Acceleration_zh_CN.md deleted 100644 → 0
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# Ascend NPU 加速
## 简介
本文档介绍如何在 Ascend NPU 上使用 MinerU。本文档内容已在`华为 Atlas 800T A2`服务器上测试通过。
```
CPU：鲲鹏 920 aarch64 2.6GHz
NPU：Ascend 910B 64GB
OS：openEuler 22.03 (LTS-SP3)/ Ubuntu 22.04.5 LTS
CANN：8.0.RC2
驱动版本：24.1.rc2.1
```
由于适配 Ascend NPU 的环境较为复杂，建议使用 Docker 容器运行 MinerU。
通过docker运行MinerU前需确保物理机已安装支持CANN 8.0.RC2的驱动和固件。
## 构建镜像
请保持网络状况良好，并执行以下代码构建镜像。
```bash
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/ascend_npu/Dockerfile -O Dockerfile
docker build -t mineru_npu:latest .
```
如果构建过程中未发生报错则说明镜像构建成功。
## 运行容器
```bash
docker run -it -u root --name mineru-npu --privileged=true \
--ipc=host \
--network=host \
--device=/dev/davinci0 \
--device=/dev/davinci1 \
--device=/dev/davinci2 \
--device=/dev/davinci3 \
--device=/dev/davinci4 \
--device=/dev/davinci5 \
--device=/dev/davinci6 \
--device=/dev/davinci7 \
--device=/dev/davinci_manager \
--device=/dev/devmm_svm \
--device=/dev/hisi_hdc \
-v /var/log/npu/:/usr/slog \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
mineru_npu:latest \
/bin/bash -c "echo 'source /opt/mineru_venv/bin/activate' >> ~/.bashrc && exec bash"
magic-pdf --help
```
docs/README_Ubuntu_CUDA_Acceleration_en_US.md deleted 100644 → 0
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# Ubuntu 22.04 LTS
### 1. Check if NVIDIA Drivers Are Installed
```sh
nvidia-smi
```
If you see information similar to the following, it means that the NVIDIA drivers are already installed, and you can skip Step 2.
> [!NOTE]
> Notice:`CUDA Version` should be >= 12.4, If the displayed version number is less than 12.4, please upgrade the driver.
```plaintext
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 570.133.07 Driver Version: 572.83 CUDA Version: 12.8 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 3060 Ti WDDM | 00000000:01:00.0 On | N/A |
| 0% 51C P8 12W / 200W | 1489MiB / 8192MiB | 5% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
```
### 2. Install the Driver
If no driver is installed, use the following command:
```sh
sudo apt-get update
sudo apt-get install nvidia-driver-570-server
```
Install the proprietary driver and restart your computer after installation.
```sh
reboot
```
### 3. Install Anaconda
If Anaconda is already installed, skip this step.
```sh
wget https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Linux-x86_64.sh
bash Anaconda3-2024.06-1-Linux-x86_64.sh
```
In the final step, enter `yes`, close the terminal, and reopen it.
### 4. Create an Environment Using Conda
```bash
conda create -n mineru 'python=3.12' -y
conda activate mineru
```
### 5. Install Applications
```sh
pip install -U magic-pdf[full]
```
> [!TIP]
> After installation, you can check the version of `magic-pdf` using the following command:
>
> ```sh
> magic-pdf --version
> ```
### 6. Download Models
Refer to detailed instructions on [how to download model files](how_to_download_models_en.md).
## 7. Understand the Location of the Configuration File
After completing the [6. Download Models](#6-download-models) step, the script will automatically generate a `magic-pdf.json` file in the user directory and configure the default model path.
You can find the `magic-pdf.json` file in your user directory.
> [!TIP]
> The user directory for Linux is "/home/username".
### 8. First Run
Download a sample file from the repository and test it.
```sh
wget https://github.com/opendatalab/MinerU/raw/master/demo/pdfs/small_ocr.pdf
magic-pdf -p small_ocr.pdf -o ./output
```
### 9. Test CUDA Acceleration
If your graphics card has at least **6GB** of VRAM, follow these steps to test CUDA acceleration:
1. Modify the value of `"device-mode"` in the `magic-pdf.json` configuration file located in your home directory.
```json
{
"device-mode": "cuda"
}
```
2. Test CUDA acceleration with the following command:
```sh
magic-pdf -p small_ocr.pdf -o ./output
```
\ No newline at end of file
docs/README_Ubuntu_CUDA_Acceleration_zh_CN.md deleted 100644 → 0
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# Ubuntu 22.04 LTS
## 1. 检测是否已安装nvidia驱动
```bash
nvidia-smi
```
如果看到类似如下的信息，说明已经安装了nvidia驱动，可以跳过步骤2
> [!NOTE]
> `CUDA Version` 显示的版本号应 >= 12.4，如显示的版本号小于12.4，请升级驱动
```plaintext
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 570.133.07 Driver Version: 572.83 CUDA Version: 12.8 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 3060 Ti WDDM | 00000000:01:00.0 On | N/A |
| 0% 51C P8 12W / 200W | 1489MiB / 8192MiB | 5% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
```
## 2. 安装驱动
如没有驱动，则通过如下命令
```bash
sudo apt-get update
sudo apt-get install nvidia-driver-570-server
```
安装专有驱动，安装完成后，重启电脑
```bash
reboot
```
## 3. 安装anacoda
如果已安装conda，可以跳过本步骤
```bash
wget -U NoSuchBrowser/1.0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2024.06-1-Linux-x86_64.sh
bash Anaconda3-2024.06-1-Linux-x86_64.sh
```
最后一步输入yes，关闭终端重新打开
## 4. 使用conda 创建环境
```bash
conda create -n mineru 'python=3.12' -y
conda activate mineru
```
## 5. 安装应用
```bash
pip install -U magic-pdf[full] -i https://mirrors.aliyun.com/pypi/simple
```
> [!TIP]
> 下载完成后，您可以通过以下命令检查`magic-pdf`的版本：
>
> ```bash
> magic-pdf --version
> ```
## 6. 下载模型
详细参考 [如何下载模型文件](how_to_download_models_zh_cn.md)
## 7. 了解配置文件存放的位置
完成[6.下载模型](#6-下载模型)步骤后，脚本会自动生成用户目录下的magic-pdf.json文件，并自动配置默认模型路径。
您可在【用户目录】下找到magic-pdf.json文件。
> [!TIP]
> linux用户目录为 "/home/用户名"
## 8. 第一次运行
从仓库中下载样本文件，并测试
```bash
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/demo/pdfs/small_ocr.pdf
magic-pdf -p small_ocr.pdf -o ./output
```
## 9. 测试CUDA加速
如果您的显卡显存大于等于 **6GB** ，可以进行以下流程，测试CUDA解析加速效果
**1.修改【用户目录】中配置文件magic-pdf.json中"device-mode"的值**
```json
{
"device-mode":"cuda"
}
```
**2.运行以下命令测试cuda加速效果**
```bash
magic-pdf -p small_ocr.pdf -o ./output
```
> [!TIP]
> CUDA加速是否生效可以根据log中输出的各个阶段cost耗时来简单判断，通常情况下，使用cuda加速会比cpu更快。
docs/README_Windows_CUDA_Acceleration_en_US.md deleted 100644 → 0
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# Windows 10/11
### 1. Install CUDA and cuDNN
You need to install a CUDA version that is compatible with torch's requirements. For details, please refer to the [official PyTorch website](https://pytorch.org/get-started/locally/).
- CUDA 11.8 https://developer.nvidia.com/cuda-11-8-0-download-archive
- CUDA 12.4 https://developer.nvidia.com/cuda-12-4-0-download-archive
- CUDA 12.6 https://developer.nvidia.com/cuda-12-6-0-download-archive
- CUDA 12.8 https://developer.nvidia.com/cuda-12-8-0-download-archive
### 2. Install Anaconda
If Anaconda is already installed, you can skip this step.
Download link: https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Windows-x86_64.exe
### 3. Create an Environment Using Conda
```bash
conda create -n mineru 'python=3.12' -y
conda activate mineru
```
### 4. Install Applications
```
pip install -U magic-pdf[full]
```
> [!IMPORTANT]
> After installation, you can check the version of `magic-pdf` using the following command:
>
> ```bash
> magic-pdf --version
> ```
### 5. Download Models
Refer to detailed instructions on [how to download model files](how_to_download_models_en.md).
### 6. Understand the Location of the Configuration File
After completing the [5. Download Models](#5-download-models) step, the script will automatically generate a `magic-pdf.json` file in the user directory and configure the default model path.
You can find the `magic-pdf.json` file in your 【user directory】 .
> [!TIP]
> The user directory for Windows is "C:/Users/username".
### 7. First Run
Download a sample file from the repository and test it.
```powershell
wget https://github.com/opendatalab/MinerU/raw/master/demo/pdfs/small_ocr.pdf -O small_ocr.pdf
magic-pdf -p small_ocr.pdf -o ./output
```
### 8. Test CUDA Acceleration
If your graphics card has at least 6GB of VRAM, follow these steps to test CUDA-accelerated parsing performance.
1. **Overwrite the installation of torch and torchvision** supporting CUDA.(Please select the appropriate index-url based on your CUDA version. For more details, refer to the [PyTorch official website](https://pytorch.org/get-started/locally/).)
```
pip install --force-reinstall torch torchvision --index-url https://download.pytorch.org/whl/cu124
```
2. **Modify the value of `"device-mode"`** in the `magic-pdf.json` configuration file located in your user directory.
```json
{
"device-mode": "cuda"
}
```
3. **Run the following command to test CUDA acceleration**:
```
magic-pdf -p small_ocr.pdf -o ./output
```
\ No newline at end of file
docs/README_Windows_CUDA_Acceleration_zh_CN.md deleted 100644 → 0
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# Windows10/11
## 1. 安装cuda环境
需要安装符合torch要求的cuda版本，具体可参考[torch官网](https://pytorch.org/get-started/locally/)
- CUDA 11.8 https://developer.nvidia.com/cuda-11-8-0-download-archive
- CUDA 12.4 https://developer.nvidia.com/cuda-12-4-0-download-archive
- CUDA 12.6 https://developer.nvidia.com/cuda-12-6-0-download-archive
- CUDA 12.8 https://developer.nvidia.com/cuda-12-8-0-download-archive
## 2. 安装anaconda
如果已安装conda，可以跳过本步骤
下载链接：
https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2024.06-1-Windows-x86_64.exe
## 3. 使用conda 创建环境
```bash
conda create -n mineru 'python=3.12' -y
conda activate mineru
```
## 4. 安装应用
```bash
pip install -U magic-pdf[full] -i https://mirrors.aliyun.com/pypi/simple
```
> [!IMPORTANT]
> 下载完成后，您可以通过以下命令检查magic-pdf的版本
>
> ```bash
> magic-pdf --version
> ```
## 5. 下载模型
详细参考 [如何下载模型文件](how_to_download_models_zh_cn.md)
## 6. 了解配置文件存放的位置
完成[5.下载模型](#5-下载模型)步骤后，脚本会自动生成用户目录下的magic-pdf.json文件，并自动配置默认模型路径。
您可在【用户目录】下找到magic-pdf.json文件。
> [!TIP]
> windows用户目录为 "C:/Users/用户名"
## 7. 第一次运行
从仓库中下载样本文件，并测试
```powershell
wget https://github.com/opendatalab/MinerU/raw/master/demo/pdfs/small_ocr.pdf -O small_ocr.pdf
magic-pdf -p small_ocr.pdf -o ./output
```
## 8. 测试CUDA加速
如果您的显卡显存大于等于 **6GB** ，可以进行以下流程，测试CUDA解析加速效果
**1.覆盖安装支持cuda的torch和torchvision**(请根据cuda版本选择合适的index-url，具体可参考[torch官网](https://pytorch.org/get-started/locally/))
```bash
pip install --force-reinstall torch torchvision --index-url https://download.pytorch.org/whl/cu124
```
**2.修改【用户目录】中配置文件magic-pdf.json中"device-mode"的值**
```json
{
"device-mode":"cuda"
}
```
**3.运行以下命令测试cuda加速效果**
```bash
magic-pdf -p small_ocr.pdf -o ./output
```
> [!TIP]
> CUDA加速是否生效可以根据log中输出的各个阶段的耗时来简单判断，通常情况下，cuda加速后运行速度比cpu更快。
docs/how_to_download_models_en.md deleted 100644 → 0
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Model downloads are divided into initial downloads and updates to the model directory. Please refer to the corresponding documentation for instructions on how to proceed.
# Initial download of model files
### Download the Model from Hugging Face
Use a Python Script to Download Model Files from Hugging Face
```bash
pip install huggingface_hub
wget https://github.com/opendatalab/MinerU/raw/master/scripts/download_models_hf.py -O download_models_hf.py
python download_models_hf.py
```
The Python script will automatically download the model files and configure the model directory in the configuration file.
The configuration file can be found in the user directory, with the filename `magic-pdf.json`.
# How to update models previously downloaded
## 1. Models downloaded via Hugging Face or Model Scope
If you previously downloaded models via Hugging Face or Model Scope, you can rerun the Python script used for the initial download. This will automatically update the model directory to the latest version.
docs/how_to_download_models_zh_cn.md deleted 100644 → 0
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模型下载分为首次下载和更新模型目录，请参考对应的文档内容进行操作
# 首次下载模型文件
模型文件可以从 Hugging Face 或 Model Scope 下载，由于网络原因，国内用户访问HF可能会失败，请使用 ModelScope。
<details>
<summary>方法一：从 Hugging Face 下载模型</summary>
<p>使用python脚本 从Hugging Face下载模型文件</p>
<pre><code>pip install huggingface_hub
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/scripts/download_models_hf.py -O download_models_hf.py
python download_models_hf.py</code></pre>
<p>python脚本会自动下载模型文件并配置好配置文件中的模型目录</p>
</details>
## 方法二：从 ModelScope 下载模型
### 使用python脚本 从ModelScope下载模型文件
```bash
pip install modelscope
wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/scripts/download_models.py -O download_models.py
python download_models.py
```
python脚本会自动下载模型文件并配置好配置文件中的模型目录
配置文件可以在用户目录中找到，文件名为`magic-pdf.json`
> [!TIP]
> windows的用户目录为 "C:\\Users\\用户名", linux用户目录为 "/home/用户名", macOS用户目录为 "/Users/用户名"
# 此前下载过模型，如何更新
## 1. 通过 Hugging Face 或 Model Scope 下载过模型
如此前通过 HuggingFace 或 Model Scope 下载过模型，可以重复执行此前的模型下载python脚本，将会自动将模型目录更新到最新版本。
next_docs/en/.readthedocs.yaml deleted 100644 → 0
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version: 2
build:
os: ubuntu-22.04
tools:
python: "3.10"
formats:
- epub
python:
install:
- requirements: next_docs/requirements.txt
sphinx:
configuration: next_docs/en/conf.py
next_docs/en/Makefile deleted 100644 → 0
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# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for the first two.
SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
SOURCEDIR = .
BUILDDIR = _build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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