docling-mps 2.110.2

Modular version of the Docling package: SDK and CLI for parsing PDF, DOCX, HTML, and more, to a unified document representation for powering downstream workflows such as gen AI applications.

Docling-MPS

[!TIP] Docling-MPS is an optimized fork of Docling designed specifically for Apple Silicon (M-series Macs). By enabling native Metal Performance Shaders (MPS) execution for TableFormer V1 & V2 stages, it achieves up to 2x faster end-to-end processing speeds on macOS compared to standard CPU-bound execution.

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Installation & Verification

Docling-MPS can be installed directly from PyPI:

pip install docling-mps

Alternatively, to install from source in editable mode:

1. Clone this repository locally:

   git clone https://github.com/thomasf1/docling-mps.git
   cd docling-mps
   

2. Install the package in editable mode in your virtual environment:

   /path/to/your/venv/bin/pip install -e .
   

3. Verify that the MPS version is successfully loaded:

   /path/to/your/venv/bin/python -c "import importlib.metadata; print('Docling Version:', importlib.metadata.version('docling-slim'))"
   

   Expected Output: 2.110.0+mps (or similar +mps local suffix).

4. Convert a document using MPS acceleration:

   • Via Python API:

     from docling.datamodel.base_models import InputFormat
     from docling.datamodel.pipeline_options import PdfPipelineOptions
     from docling.document_converter import DocumentConverter, PdfFormatOption
     
     options = PdfPipelineOptions()
     options.accelerator_options.device = "mps" # Enables GPU acceleration
     
     converter = DocumentConverter(
         format_options={
             InputFormat.PDF: PdfFormatOption(pipeline_options=options)
         }
     )
     result = converter.convert("document.pdf")
     print(result.document.export_to_markdown())
     

   • Via CLI:

     uv run docling --device mps path/to/document.pdf
     

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What is Docling ?

Docling simplifies document processing by parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the generative AI ecosystem.

Features

• 🗂️ Parsing of multiple document formats including PDF, DOCX, PPTX, XLSX, HTML, EPUB, WAV, MP3, WebVTT, email formats (EML, MSG), images (PNG, TIFF, JPEG, ...), LaTeX, DocLang, plain text, and more
• 📑 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more
• 🧬 A unified, expressive DoclingDocument representation format
• ↪️ Various export formats and options, including Markdown, HTML, WebVTT, DocLang, DocTags and lossless JSON
• 📜 Support for several application-specific XML schemas including DocLang, USPTO patents, JATS articles, and XBRL financial reports.
• 🔒 Local execution capabilities for sensitive data and air-gapped environments
• 🤖 Plug-and-play integrations incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
• 🔍 Extensive OCR support for scanned PDFs and images
• 👓 Support for several Visual Language Models, such as (GraniteDocling)
• 🎙️ Audio support with Automatic Speech Recognition (ASR) models
• 🔌 Connect to any agent using the MCP server
• 🌐 Run Docling as a service with the API server (docling-serve)
• 💻 Simple and convenient CLI

What's new

• 📄 Parsing of ODF (OpenDocument Format) files for text documents (.odt), spreadsheets (.ods), and presentations (.odp)
• 💼 Parsing of XBRL (eXtensible Business Reporting Language) documents for financial reports
• 📧 Parsing of email files (.eml, .msg)
• 📚 Parsing of EPUB (Electronic Publication) files for e-books
• 📝 Parsing of plain-text files (.txt, .text) and Markdown supersets (.qmd, .Rmd)
• 📊 Chart understanding (Barchart, Piechart, LinePlot): convert them into tables or code and add detailed descriptions

Coming soon

• 📝 Metadata extraction, including title, authors, references & language
• 📝 Complex chemistry understanding (Molecular structures)

Quickstart

1. Install

pip install docling

Note: Python 3.9 support was dropped in docling version 2.70.0. Please use Python 3.10 or higher.

Works on macOS, Linux and Windows environments for both x86_64 and arm64 architectures.

More detailed installation instructions are available in the docs.

2. Convert a document (CLI)

docling https://arxiv.org/pdf/2206.01062

This generates a .md file in the current directory containing structured document content.

You can also use 🥚GraniteDocling and other VLMs via Docling CLI:

docling --pipeline vlm --vlm-model granite_docling https://arxiv.org/pdf/2206.01062

3. Python usage (recommended)

from docling.document_converter import DocumentConverter

source = "https://arxiv.org/pdf/2408.09869"  # a document via a local path or URL
converter = DocumentConverter()
result = converter.convert(source)
print(result.document.export_to_markdown())  # output: "## Docling Technical Report[...]"

More advanced usage and configuration options.

Documentation

Check out Docling's documentation for details on installation, usage, concepts, recipes, extensions, and more.

Examples

Go hands-on with our examples, demonstrating how to address different application use cases with Docling.

Integrations

To further accelerate your AI application development, check out Docling's native integrations with popular frameworks and tools.

Get help and support

Please feel free to connect with us using the discussion section.

Technical report

For more details on Docling's inner workings, check out the Docling Technical Report.

Contributing

Please read Contributing to Docling for details.

References

If you use Docling in your projects, please consider citing the following:

@techreport{Docling,
  author = {Deep Search Team},
  month = {8},
  title = {Docling Technical Report},
  url = {https://arxiv.org/abs/2408.09869},
  eprint = {2408.09869},
  doi = {10.48550/arXiv.2408.09869},
  version = {1.0.0},
  year = {2024}
}

Apple Silicon Optimizations & Benchmarks

This local version (2.107.0+mps) has been optimized to fully leverage Apple Silicon hardware (M-series chips) for document processing.

Optimizations

• MPS (Metal Performance Shaders) Enablement: Removed CPU fallbacks for TableFormer V1 & V2 models, enabling standard PyTorch operations to execute natively on the GPU instead of CPU.
• VLM Presets for Apple Silicon: Added GRANITEDOCLING and SMOLDOCLING constants that dynamically select highly-optimized local mlx-vlm models on Apple Silicon.

Benchmark Results (TableFormer V1 Accurate Mode)

Timings measured for processing financial reports in /Volumes/18T/Annual/scratch/comparison (156-page PDF documents) sequentially:

PDF Document	Configuration	Processing Time (s)	Output Chars	Speedup vs CPU	Speedup vs MPS
NASDAQ_LPCN_2023.pdf	PyTorch CPU	102.56s	604,940	1.00x	0.49x
	PyTorch MPS	50.46s	604,940	2.03x	1.00x
NYSE_ITT_2023.pdf	PyTorch CPU	121.35s	453,126	1.00x	0.49x
	PyTorch MPS	59.24s	453,126	2.05x	1.00x
OTC_SOMC_2023.pdf	PyTorch CPU	115.56s	340,292	1.00x	0.53x
	PyTorch MPS	61.19s	340,292	1.89x	1.00x

Concurrent Workload Benchmark (Parallel Processing)

Concurrently processing all 3 documents using parallel Python subprocesses:

• PyTorch CPU Parallel: 226.53 seconds (1.00x)
• PyTorch MPS Parallel: 110.61 seconds (2.05x speedup vs CPU)

CoreML & MLX Hardware Contention (Why they didn't pan out)

We also researched, converted, and benchmarked TableFormer V1's encoder to CoreML (linear quantized to INT8 for the Neural Engine) and MLX (GPU), and the Layout Model (RT-DETR) to ONNX running on Apple's CoreMLExecutionProvider. While successfully running with 100% numerical correctness, these backends did not outperform PyTorch MPS due to native platform architectural constraints:

1. Graph Partitioning & Context Switching Overhead (Layout Model):

   • Because RT-DETR contains custom post-processing nodes unsupported by CoreML, ONNX Runtime split the model into 28 separate CoreML partitions.
   • Transitioning between CoreML hardware execution and CPU fallback nodes required copying intermediate tensor data between ANE memory and system RAM 28 times per page. This context switching and memory copy overhead offset the ANE's raw processing speedup, making Layout CoreML (64.29s) slower than running natively on GPU via PyTorch MPS (50.46s).

2. Apple Neural Engine (ANE) Multi-Process Contention:

   • When attempting to run parallel document conversions concurrently under multiprocessing, Apple's E5/ANE runtime suffered from compilation cache and file-lock collisions on the compiled macho assets inside /var/folders/, throwing on-device model load failures (ANE model load has failed...).
   • This forced CoreML to fallback to silent CPU execution. The resulting core thread contention degraded parallel throughput significantly (137.53s wall time) compared to the hardware time-sliced GPU command queue multiplexing of PyTorch MPS (110.61s).

Thus, native PyTorch MPS (GPU) remains the most robust, performant, and scale-friendly backend for Docling on Apple Silicon.

License

The Docling codebase is under MIT license. For individual model usage, please refer to the model licenses found in the original packages.

LF AI & Data

Docling is hosted as a project in the LF AI & Data Foundation.

IBM ❤️ Open Source AI

The project was started by the AI for knowledge team at IBM Research Zurich.