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PaddleOCR-VL Apple Silicon Environment Configuration Tutorial

This tutorial is a guide for configuring the PaddleOCR-VL Apple Silicon environment. The purpose is to complete the relevant environment setup. After the environment configuration is complete, please refer to the PaddleOCR-VL Usage Tutorial to use PaddleOCR-VL.

Apple Silicon include, but are not limited to:

  • Apple M1
  • Apple M2
  • Apple M3
  • Apple M4

PaddleOCR-VL has been verified for accuracy and speed on the Apple M4. However, due to hardware diversity, compatibility with other Apple Silicon has not yet been confirmed. We welcome the community to test on different hardware setups and share your results.

1. Environment Preparation

We strongly recommend installing PaddleOCR-VL in a virtual environment to avoid dependency conflicts. For example, use the Python venv standard library to create a virtual environment:

# Create a virtual environment
python -m venv .venv_paddleocr
# Activate the environment
source .venv_paddleocr/bin/activate

Execute the following commands to complete the installation:

python -m pip install paddlepaddle==3.2.1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
python -m pip install -U "paddleocr[doc-parser]"

Please install PaddlePaddle framework version 3.2.1 or above.

2. Quick Start

Please refer to the corresponding section in the PaddleOCR-VL Usage Tutorial.

3. Improving VLM Inference Performance Using Inference Acceleration Frameworks

The inference performance under default configurations is not fully optimized and may not meet actual production requirements. This step mainly introduces how to use the MLX-VLM inference acceleration framework to improve the inference performance of PaddleOCR-VL.

3.1 Starting the VLM Inference Service

Install the MLX-VLM inference framework:

git clone https://github.com/Blaizzy/mlx-vlm.git
cd mlx-vlm
pip install -e .

Start the MLX-VLM inference service:

mlx_vlm.server --port 8111

3.2 Client Usage Method

3.2.1 Command Line Usage

You can specify the backend type (mlx-vlm-server) via --vl_rec_backend, the service address via --vl_rec_server_url, and the huggingface repo id or server-side model weights path via --vl_rec_api_model_name. For example:

paddleocr doc_parser \
  --input paddleocr_vl_demo.png \
  --vl_rec_backend mlx-vlm-server \
  --vl_rec_server_url http://localhost:8111/ \
  --vl_rec_api_model_name PaddlePaddle/PaddleOCR-VL-1.5

3.2.2 Python Script Integration

When creating a PaddleOCRVL object, specify the backend type via vl_rec_backend, the service address via the vl_rec_server_url parameter, and the huggingface repo id or server-side model weights path via vl_rec_api_model_name. For example:

pipeline = PaddleOCRVL(
    vl_rec_backend="mlx-vlm-server", 
    vl_rec_server_url="http://localhost:8111/",
    vl_rec_api_model_name="PaddlePaddle/PaddleOCR-VL-1.5",
)

3.3 Performance Tuning

Please refer to the corresponding section in the PaddleOCR-VL Usage Tutorial.

4. Service Deployment

Currently, only manual deployment is supported. Please refer to Section 4.2 Method 2: Manual Deployment in the PaddleOCR-VL Usage Tutorial.

4.3 Client Invocation Methods

Please refer to the corresponding section in the PaddleOCR-VL Usage Tutorial.

4.4 Pipeline Configuration Adjustment Instructions

Please refer to the corresponding section in the PaddleOCR-VL Usage Tutorial.

5. Model Fine-Tuning

Please refer to the corresponding section in the PaddleOCR-VL Usage Tutorial.

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