Flask API wrapper around marker — converts PDFs, DOCX, XLSX, PPTX, EPUB, images (PNG/JPG/BMP/GIF/TIFF/WEBP/HEIC), and HTML to Markdown (JSON/HTML/chunks).

Quick Start

Container (recommended)

# Build and run with Podman/Docker
podman build -t marker-api -f Containerfile .
podman run -d --rm \
  --name marker-api \
  -p 8000:8000 \
  -e AMD_COMPUTE=false \
  -e TORCH_DEVICE=cpu \
  marker-api

# Or use docker-compose
docker-compose up -d

Bare-metal

git clone https://github.com/your-org/marker-api.git /app/marker-api
pip install -e /app/marker-api/marker[full] flask gunicorn
PORT=8000 python /app/marker-api/app.py

Health Check

curl http://localhost:8000/health
# {"status":"ok","ollama":"http://localhost:11434","torch_device":"cpu","supported_formats":["docx","epub","html","jpg",...]}

Endpoints

`GET /`

HTML documentation page listing all endpoints and parameters.

`GET /health`

Returns server status, torch device, supported file formats, and configuration.

`POST /marker`

Convert a single file. Accepts multipart/form-data or application/json.

Multipart form-data

curl -X POST http://localhost:8000/marker \
  -F "file=@document.pdf" \
  -F "output_format=markdown" \
  -F "force_ocr=false"

When output_format=markdown, the response is the raw .md content as a file download. All other formats return JSON.

JSON body (base64)

curl -X POST http://localhost:8000/marker \
  -H "Content-Type: application/json" \
  -d '{
    "file_b64": "<base64>",
    "filename": "document.pdf",
    "output_format": "markdown",
    "force_ocr": false
  }'

`POST /v1/conversions`

Same as /marker but always returns JSON, wrapping the result with an id (UUID) field and filename.

`POST /v1/files/convert`

Same as /marker but always returns JSON with filename, format, output, images_b64, and metadata.

Parameters

Parameter	Type	Default	Description
`file` / `file_b64`	file / string	required	The document to convert
`output_format`	string	`markdown`	`markdown`, `json`, `html`, `chunks`
`force_ocr`	bool	`false`	Force OCR on all pages (fixes garbled text)
`paginate_output`	bool	`false`	Separate pages with horizontal rules
`page_range`	string	all	Comma-separated pages/ranges: `"0,5-10"`
`disable_image_extraction`	bool	`false`	Skip embedded image extraction
`processors`	string	auto	Comma-separated full module paths for custom processors
`config_json`	string	none	Path to JSON config file
`converter_cls`	string	auto	Full module path of converter (e.g. `marker.converters.table.TableConverter`)
`use_llm`	bool	`false`	Use an LLM to improve accuracy
`llm_service`	string	`marker.services.ollama.OllamaService`	LLM service class: `gemini`, `vertex`, `claude`, `openai`, `azure_openai`, `ollama`
`block_correction_prompt`	string	none	Custom prompt for LLM block correction
`redo_inline_math`	bool	`false`	Re-process inline math with LLM
`strip_existing_ocr`	bool	`false`	Remove existing OCR text and re-OCR
`debug`	bool	`false`	Enable debug logging

Supported Formats

PDF, DOCX, XLSX, PPTX, EPUB, PNG, JPG, BMP, GIF, TIFF, WEBP, HEIC, HTML, HTM.

Environment Variables

Variable	Default	Description
`OLLAMA_HOST`	`http://localhost:11434`	Ollama instance for OCR fallback / LLM
`DEESEEK_OCR_MODEL`	`deepseek-ocr`	OCR model name in Ollama
`AMD_COMPUTE`	`false`	Enable AMD ROCm GPU support
`TORCH_DEVICE`	auto	PyTorch device: `rocm`, `cuda`, `cpu`
`MODEL_DTYPE`	`float32`	Model dtype: `float32`, `bfloat16`
`PORT`	`8000`	Listening port
`HOST`	`0.0.0.0`	Listening host

PowerShell Batch Script

A PowerShell script (marker-convert-powershell/marker-convert.ps1) is provided for batch conversion.

Usage

.\marker-convert-powershell\marker-convert.ps1 -TargetFolder .\documents

# With options
.\marker-convert-powershell\marker-convert.ps1 .\documents `
  -Force -MaxConcurrency 8 -OutputFormat json -UseLlm -LlmService marker.services.ollama.OllamaService

# Remote API
.\marker-convert-powershell\marker-convert.ps1 .\documents -ApiUrl http://10.0.0.5:8000/marker

Parameters

Parameter	Type	Default	Description
`-TargetFolder`	string	(required)	Folder to scan recursively
`-ApiUrl`	uri	`http://localhost:8000/marker`	Marker API endpoint
`-OutputFormat`	string	`markdown`	`markdown`, `json`, `html`, `chunks`
`-Force`	switch	off	Overwrite existing `.md` files
`-MaxConcurrency`	int	4	Concurrent workers (1-32)
`-Timeout`	int	300	HTTP timeout in seconds (30-1800)
`-PageRange`	string	""	Page range e.g. `"0,5-10"`
`-ForceOcr`	switch	off	Force OCR on all pages
`-DisableImageExtraction`	switch	off	Skip image extraction
`-UseLlm`	switch	off	Enable LLM enhancement
`-LlmService`	string	`marker.services.ollama.OllamaService`	LLM service class
`-Processors`	string	""	Custom processor module paths
`-ConfigJson`	string	""	Path to JSON config file
`-ConverterCls`	string	""	Custom converter class path

The script writes a CSV report (_marker_convert_results.csv) next to each converted file.

Deployment

AMD GPU (ROCm)

Set AMD_COMPUTE=true and TORCH_DEVICE=cuda in the container environment.

Vega 20 / GFX906 Support (e.g., powermac)

For older AMD GPU architectures like Vega 20 (GFX906), AMD dropped standard ROCm PyTorch support. This project fully supports GFX906 out of the box by using a patched GFX906 PyTorch base image (mixa3607/pytorch-gfx906:v2.7.1-rocm-6.3.3).

To run the container on GFX906 GPUs with hardware acceleration:

docker run -d --name marker-api \
  --network=host \
  --device /dev/kfd --device /dev/dri \
  --group-add 984 --group-add 988 \
  -e AMD_COMPUTE=true \
  -e TORCH_DEVICE=cuda \
  -e CUDA_VISIBLE_DEVICES=1 \
  -e HSA_OVERRIDE_GFX_VERSION=9.0.6 \
  marker-api:latest \
  gunicorn --bind 0.0.0.0:8000 --workers 1 --timeout 300 --worker-class gthread --threads 4 app:app_instance

Note on first run: The very first conversion on a ROCm GPU will take 2-3 minutes as MIOpen compiles convolution kernels for your exact GPU. Subsequent runs are fully cached and take ~4-5 seconds per document. Note on memory: If other services (e.g., llama-servers) are utilizing GPU 0, restrict marker-api to GPU 1 (CUDA_VISIBLE_DEVICES=1) and use a single worker (--workers 1) to prevent HIP Out-Of-Memory errors.

CPU-only

TORCH_DEVICE=cpu AMD_COMPUTE=false podman run ... marker-api

GPU (CUDA)

Override the PyTorch index URL during build, or use the existing ROCm image with TORCH_DEVICE=cuda.

Response Codes

Code	Meaning
`200`	Successful conversion (markdown returned as file download, others as JSON)
`400`	Missing file or invalid body
`500`	Conversion error (details in JSON body)

Error responses have the shape:

{"success": false, "error": "error message"}