Docling Python Quickstart & CLI Tutorial
Step-by-step developer tutorial for installing IBM's Docling, running local batch conversions, enabling CUDA GPU acceleration, and exporting to clean Markdown.
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1. System Requirements & Installation
Docling requires Python 3.10 or higher. We recommend creating a clean virtual environment:
# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate
# Install basic package
pip install docling
# Or install with full OCR engine support
pip install "docling[ocr]"2. Command Line Interface (CLI) Usage
Once installed, you can convert local files or remote URLs directly from your terminal:
# Convert a local PDF
docling sample_paper.pdf
# Convert an online arXiv paper directly
docling https://arxiv.org/pdf/2408.09869
# Export directly to custom output directory
docling sample.pdf --output ./dist --to md3. Python Script Example
Here is how to integrate DocumentConverter in your Python backend:
from pathlib import Path
from docling.document_converter import DocumentConverter
# Initialize converter
converter = DocumentConverter()
# Convert PDF or image
source = Path("financial_statement.pdf")
conversion_result = converter.convert(source)
# Export to clean Markdown
markdown_output = conversion_result.document.export_to_markdown()
# Save output
with open("output.md", "w", encoding="utf-8") as f:
f.write(markdown_output)
print("Conversion complete!")4. Common Issues & Pitfalls
Issue: Out of Memory (OOM) on Large PDFs
Processing 50+ page PDFs locally can consume 8GB+ of RAM. If running in a memory-constrained container, consider processing in page chunks or using SheetParser's cloud queue.
Issue: Missing OpenCV System Dependencies
On Debian/Ubuntu, you may encounter ImportError: libGL.so.1: cannot open shared object file. Fix with: sudo apt-get install ffmpeg libsm6 libxext6.