---

name: conversion-cli-api description: "Convert documents with Marker through safe CLI and Python API workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Marker conversion CLI/API

Use this sub-skill when a user needs local document conversion with Marker: single files, folders, chunked multi-GPU runs, table-only conversion, OCR-only conversion, output parsing, or Python API examples.

Quick route

• For one file, use marker_single with --output_format, --page_range, --output_dir, and conversion flags. See CLI reference.
• For a folder, use marker with the same common options plus --workers, --skip_existing, --max_files, --chunk_idx, and --num_chunks. See CLI reference.
• For multi-GPU shell orchestration, use marker_chunk_convert only when the installed environment and GPU topology are ready. See chunk conversion.
• For programmatic conversion, use PdfConverter, TableConverter, or OCRConverter with create_model_dict, ConfigParser, text_from_rendered, and save_output. See Python API.
• For output interpretation, metadata, images, JSON trees, or chunks, see output formats.
• For install, device, worker, page range, converter class, optional dependency, and debug failures, see troubleshooting.

Core commands

marker_single input.pdf --output_format markdown --output_dir output
marker_single input.pdf --page_range "0,5-10" --disable_image_extraction --output_format json --output_dir output
marker input_folder --output_format json --page_range "0,5-10" --skip_existing --workers 2 --output_dir output
marker_single input.pdf --converter_cls marker.converters.table.TableConverter --force_layout_block Table --output_format json
marker_single input.pdf --converter_cls marker.converters.ocr.OCRConverter --keep_chars --output_dir output

Conversion can load models and consume CPU/GPU/VRAM. For planning, environment checks, or CI smoke tests, run scripts/marker_cli_smoke.py; it only calls --help on installed console scripts.

Python pattern

from marker.config.parser import ConfigParser
from marker.converters.pdf import PdfConverter
from marker.models import create_model_dict
from marker.output import save_output, text_from_rendered

options = {"output_format": "json", "page_range": "0,5-10", "disable_image_extraction": True}
config = ConfigParser(options)
converter = PdfConverter(
    artifact_dict=create_model_dict(),
    config=config.generate_config_dict(),
    processor_list=config.get_processors(),
    renderer=config.get_renderer(),
    llm_service=config.get_llm_service(),
)
rendered = converter("input.pdf")
text, extension, images = text_from_rendered(rendered)
save_output(rendered, "output/input", "input")

For a runnable guarded template, copy or adapt scripts/marker_conversion_skeleton.py. It requires a real input path and never starts conversion just by being imported.

Stay in scope

• Route LLM credentials, --use_llm, --llm_service, structured extraction, and LLM-specific processors to the llm-extraction-services sub-skill.
• Route custom processors, renderers, providers, converter internals, config crawling, and schema extension to the configuration-extension sub-skill.
• Route marker_server, marker_gui, marker_extract, FastAPI clients, Streamlit apps, and Modal deployment to the server-deployment sub-skill.