Boost With Cuda ================ If your device supports CUDA and meets the GPU requirements of the mainline environment, you can use GPU acceleration. Please select the appropriate guide based on your system: - :ref:`ubuntu_22_04_lts_section` - :ref:`windows_10_or_11_section` .. _ubuntu_22_04_lts_section: Ubuntu 22.04 LTS ----------------- 1. Check if NVIDIA Drivers Are Installed ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: 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:: ``CUDA Version`` should be >= 12.4, If the displayed version number is less than 12.4, please upgrade the driver. .. code:: text +---------------------------------------------------------------------------------------+ | 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: .. code:: sh sudo apt-get update sudo apt-get install nvidia-driver-570-server Install the proprietary driver and restart your computer after installation. .. code:: sh reboot 3. Install Anaconda ~~~~~~~~~~~~~~~~~~~ If Anaconda is already installed, skip this step. .. code:: 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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Specify Python version 3.10～3.13. .. code:: sh conda create -n mineru 'python=3.12' -y conda activate mineru 5. Install Applications ~~~~~~~~~~~~~~~~~~~~~~~ .. code:: sh pip install -U magic-pdf[full] .. admonition:: TIP :class: tip After installation, you can check the version of ``magic-pdf`` using the following command: .. code:: sh magic-pdf --version 6. Download Models ~~~~~~~~~~~~~~~~~~ Refer to detailed instructions on :doc:`download_model_weight_files` 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. .. admonition:: TIP :class: tip The user directory for Linux is “/home/username”. 8. First Run ~~~~~~~~~~~~ Download a sample file from the repository and test it. .. code:: 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 **8GB** 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. .. code:: json { "device-mode": "cuda" } 2. Test CUDA acceleration with the following command: .. code:: sh magic-pdf -p small_ocr.pdf -o ./output .. _windows_10_or_11_section: Windows 10/11 -------------- 1. Install CUDA ~~~~~~~~~~~~~~~~~~~~~~~~~ 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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :: conda create -n mineru 'python=3.12' -y conda activate mineru 4. Install Applications ~~~~~~~~~~~~~~~~~~~~~~~ :: pip install -U magic-pdf[full] .. admonition:: Tip :class: tip After installation, you can check the version of ``magic-pdf``: .. code:: bash magic-pdf --version 5. Download Models ~~~~~~~~~~~~~~~~~~ Refer to detailed instructions on :doc:`download_model_weight_files` 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】 . .. admonition:: Tip :class: tip The user directory for Windows is “C:/Users/username”. 7. First Run ~~~~~~~~~~~~ Download a sample file from the repository and test it. .. code:: 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 8GB 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/).) .. code:: sh 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. .. code:: json { "device-mode": "cuda" } 3. **Run the following command to test CUDA acceleration**: :: magic-pdf -p small_ocr.pdf -o ./output