mirror of https://github.com/opendatalab/MinerU.git synced 2026-03-27 11:08:32 +07:00

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myhloli b0e220c5f0 refactor(demo): simplify batch_demo.py and update demo.py

- Remove unnecessary imports and code in batch_demo.py
- Update demo.py to use relative paths and improve code structure
- Adjust output directory structure in both scripts
- Remove redundant code and simplify functions

2025-04-02 23:58:17 +08:00

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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. Currently, torch supports CUDA 11.8/12.4/12.6.

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

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

conda create -n mineru 'python<3.13' -y
conda activate mineru

4. Install Applications

pip install -U magic-pdf[full]

Important

After installation, verify the version of magic-pdf:
magic-pdf --version
If the version number is less than 1.3.0, please report it in the issues section.

5. Download Models

Refer to detailed instructions on how to download model files.

6. Understand the Location of the Configuration File

After completing the 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.

  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.

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.)
```
pip install --force-reinstall torch==2.6.0 torchvision==0.21.1 "numpy<2.0.0" --index-url https://download.pytorch.org/whl/cu124
```
Modify the value of "device-mode" in the magic-pdf.json configuration file located in your user directory.
```
{
  "device-mode": "cuda"
}
```
Run the following command to test CUDA acceleration:
```
magic-pdf -p small_ocr.pdf -o ./output
```

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