Windows 10/11

You need to install a CUDA version that is compatible with torch's requirements. For details, please refer to the official PyTorch website.

• 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

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

conda create -n mineru 'python=3.12' -y
conda activate mineru

pip install -U magic-pdf[full]

magic-pdf --version

Refer to detailed instructions on how to download model files.

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】 .

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

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.)

   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.

   {
     "device-mode": "cuda"
   }
   

3. Run the following command to test CUDA acceleration:

   magic-pdf -p small_ocr.pdf -o ./output