김경종
91 lines
3.9 KiB
Markdown
91 lines
3.9 KiB
Markdown
# Toolchain Notes
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This document summarizes the researched toolchain choices and local compatibility decisions.
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## Verified Environment
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- OS: Windows 10
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- GPU: NVIDIA GeForce GTX 1070 Ti
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- VRAM: 8 GB
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- NVIDIA driver: 577.00
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- `nvidia-smi` CUDA runtime capability: 12.9
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- User-installed CUDA toolkit: 12.4
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- Python: 3.11.15 in repo-local `venv`
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- Environment manager: Conda / Miniforge
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## Python Dependencies
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Use one repo-local `venv` and install from `requirements.txt`.
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Key pins:
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- `torch==2.7.1+cu126`
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- `torchvision==0.22.1+cu126`
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- `marker-pdf==1.10.2`
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- `nougat-ocr==0.1.17`
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- `transformers==4.57.6`
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- `albumentations==1.3.1`
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- `pymupdf==1.27.2.3`
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- `pandas==3.0.2`
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- `pytest==9.0.3`
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- `pypdfium2==4.30.0`
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- `opencv-python-headless==4.11.0.86`
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- `Pillow==10.4.0`
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- `fsspec==2026.2.0`
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## PyTorch / CUDA Decision
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- `torch==2.11.0+cu128` imports on this machine but does not support GTX 1070 Ti `sm_61` at runtime.
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- `torch==2.7.1+cu126` satisfies Marker `torch>=2.7.0` and successfully runs CUDA tensor operations on GTX 1070 Ti.
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- Keep this pin unless a newer official PyTorch wheel is verified to support `sm_61`.
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## Marker
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- Marker is the primary document parser.
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- It handles layout, OCR/layout, reading order, body text, headings, tables, figures, captions, and semantic block roles.
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- It should be consumed through structured output or adapter APIs where possible, not by scraping final Markdown text.
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## Nougat
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- Nougat is used only for formulas and mathematical expressions.
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- `nougat-ocr==0.1.17` has loose dependency bounds, so the project pins compatible versions.
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- `transformers 5.x` breaks Nougat imports.
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- `albumentations 2.x` breaks Nougat transform initialization.
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- Nougat failure must fall back to Marker source text.
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## PyMuPDF
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- PyMuPDF is used for lightweight page analysis, page counts, text-layer quality checks, OCR intervention planning, chunk planning, and low-level PDF/page operations.
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- It is not the primary document parser.
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## Comparison Baselines
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These tools are useful for research or quality comparison but are not the primary architecture:
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- PyMuPDF4LLM
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- Docling
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- MinerU
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- MarkItDown
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Do not switch the primary parser without updating `docs/ADR.md`, `docs/ARCHITECTURE.md`, and `docs/CONVERSION_POLICY.md`.
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## Reference Links
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- Marker PyPI: https://pypi.org/project/marker-pdf/
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- Nougat GitHub: https://github.com/facebookresearch/nougat
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- PyMuPDF documentation: https://pymupdf.readthedocs.io/
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- PyTorch previous versions: https://docs.pytorch.org/get-started/previous-versions/
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- GitHub Flavored Markdown spec: https://github.github.io/gfm/
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- MathJax TeX delimiters: https://docs.mathjax.org/en/latest/input/tex/delimiters.html
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- Docling GitHub: https://github.com/docling-project/docling
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- MinerU GitHub: https://github.com/opendatalab/MinerU
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## Markdown And Math Rendering
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- Markdown table output should target GitHub Flavored Markdown where possible.
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- Complex tables may use limited HTML `<table>`.
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- Math output uses `$ ... $` for inline formulas and `$$ ... $$` for block formulas.
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- `$...$` can conflict with ordinary dollar signs, so delimiter validation and repair are required.
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## Model Cache
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- Use explicit local cache paths for Marker/Nougat/Hugging Face model downloads.
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- README should include model pre-download and offline execution instructions before the engine is released.
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- Default project-local model cache path is `.models/`.
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- `PDFTOMD_MODEL_CACHE` can override the default cache root.
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- The runtime cache policy exposes Hugging Face cache environment variables from that root without downloading models during validation.
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- Runtime logs and resume state are runtime artifacts under `output/.pdftomd-runtime/<document-slug>/`, not generated document sidecars.
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## Licensing Notes
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- Current user context is personal use.
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- Before redistribution or commercial use, revisit Marker GPL and model-weight license implications.
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- Process or API isolation can reduce coupling risk, but it is not a substitute for legal review.
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