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https://github.com/RapidAI/RapidOCR.git
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locviettriandClaude Opus 4.6
1f78b76eb4
Replace the single legacy Dockerfile (rapidocr_api only) with 7 per-engine development images covering all supported inference backends: - onnxruntime-cpu: ONNX Runtime on CPU (python:3.10-slim-bookworm) - onnxruntime-gpu: ONNX Runtime with CUDA (nvidia/cuda:12.4.1) - tensorrt: NVIDIA TensorRT (nvcr.io/nvidia/deepstream:7.0-gc-triton-devel) - paddle: PaddlePaddle CPU - openvino: Intel OpenVINO - pytorch: PyTorch CPU - mnn: Alibaba MNN Infrastructure: - Dockerfile.base: shared foundation for CPU variants - docker-compose.yaml: all 7 services with shared model volume - Makefile: make build-*/test-*/shell-* convenience commands - .dockerignore: excludes .git, models, build artifacts Key design decisions: - PYTHONPATH-based imports (setup.py incompatible with pip editable install) - Named volume (rapidocr-models) for persistent model cache across rebuilds - Source bind-mounted at runtime for live editing - tensorrt pinned to 8.6.x + cuda-python 12.x (matching DeepStream 7.0) All 7 images built and tested with full OCR pipeline inference. Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
8 lines
190 B
Docker
8 lines
190 B
Docker
FROM rapidocr-base:latest
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# PyTorch CPU from official index
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RUN pip install --no-cache-dir torch torchvision torchaudio \
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--index-url https://download.pytorch.org/whl/cpu
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CMD ["bash"]
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