Feature Request: Add PaddleOCR GPU support to Docker image

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Issue 类型
功能
描述清晰度
基本清楚
活跃度
冷清
技术栈
docker, python
领域
devops, infrastructure, machine-learning

调研方向

首先查看现有的 Dockerfile 和镜像发布配置，然后阅读 unstructured/partition/utils/ocr_models/paddle_ocr.py 和 OCR_AGENT_BUG_ISSUE.md。确定 GPU 镜像标签或构建参数是否在范围内，并验证所选镜像能否使用所需的 PaddleOCR 依赖项进行构建且支持 GPU 执行。

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描述

Feature Request: Add PaddleOCR GPU support to unstructured-api Docker image

Summary

The official unstructured-api Docker image includes Tesseract but not PaddleOCR. Users who want to use PaddleOCR with GPU acceleration must build a custom image. It would be valuable to have an official GPU-enabled image with PaddleOCR pre-installed.

Current Behavior

  • The unstructured-api:latest image only includes Tesseract OCR
  • PaddleOCR must be manually installed via pip install paddlepaddle unstructured-paddleocr
  • There is no GPU-enabled variant of the image
  • The OCR_AGENT environment variable is ignored (see related issue: OCR_AGENT_BUG_ISSUE.md)

Proposed Solution

Option 1: Provide GPU-enabled image tags

Publish additional Docker image variants:

unstructured-api:latest-gpu-cu118  # CUDA 11.8
unstructured-api:latest-gpu-cu126  # CUDA 12.6

These images would include:

  • paddlepaddle-gpu from the appropriate CUDA index
  • unstructured-paddleocr
  • NVIDIA CUDA runtime
Option 2: Add build args to existing Dockerfile

Add build arguments to allow users to build GPU-enabled images:

ARG USE_GPU=false
ARG CUDA_VERSION=cu118

RUN if [ "$USE_GPU" = "true" ]; then \
        pip install --no-cache-dir \
            paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/packages/stable/${CUDA_VERSION}/ \
            unstructured-paddleocr; \
    fi

Implementation Details

The unstructured library already supports GPU acceleration for PaddleOCR. In unstructured/partition/utils/ocr_models/paddle_ocr.py:

gpu_available = paddle.device.cuda.device_count() > 0
if gpu_available:
    logger.info(f"Loading paddle with GPU on language={language}...")

paddle_ocr = PaddleOCR(
    use_angle_cls=True,
    use_gpu=gpu_available,  # Auto-detects GPU
    lang=language,
    enable_mkldnn=True,
    show_log=False,
)

This means the library automatically uses GPU when available - the only requirement is installing paddlepaddle-gpu instead of paddlepaddle.

Workaround

Users can extend the official image:

FROM downloads.unstructured.io/unstructured-io/unstructured-api:latest

USER root

ARG USE_GPU=false
RUN if [ "$USE_GPU" = "true" ]; then \
        pip install --no-cache-dir \
            paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ \
            unstructured-paddleocr; \
    else \
        pip install --no-cache-dir \
            paddlepaddle \
            unstructured-paddleocr; \
    fi

USER notebook-user

ENV OCR_AGENT=unstructured.partition.utils.ocr_models.paddle_ocr.OCRAgentPaddle

Note: This also requires patching general.py to pass ocr_agent to partition() - see OCR_AGENT_BUG_ISSUE.md.

Benefits

  1. Performance: PaddleOCR with GPU is significantly faster than Tesseract for batch processing
  2. Accuracy: PaddleOCR (especially PP-OCRv4) provides better accuracy on many document types
  3. Ease of use: Official GPU images eliminate the need for custom Dockerfiles

Environment

  • unstructured-api: latest
  • unstructured: 0.18.18+
  • PaddlePaddle: 3.2.2
  • CUDA: 11.8 / 12.6

Related Issues

  • OCR_AGENT environment variable is ignored (see OCR_AGENT_BUG_ISSUE.md)

References

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环境准备

  • 提供 Dockerfile 或 Docker Compose 文件
  • 没有 Pull Request 模板
  • 阅读贡献指南

从这里开始

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  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

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