To rapidly deploy production-grade dots.ocr services, the following technical solutions are recommended:
- Docker Program: Use the officially provided Docker image to solve the environment dependency problem and realize minute deployment.
- vLLM optimized deployment: Control GPU allocation by registering models to the vLLM framework using the CUDA_VISIBLE_DEVICES parameter for optimal inference performance
- API Servitization: Run the demo_vllm.py example to quickly set up a RESTful API service
- Interactive debugging: Support live testing by launching a visual demo interface (demo_gradio.py) via gradio.
When deploying, note that the model path cannot contain periods, and it is recommended to use the default DotsOCR folder name.
This answer comes from the articledots.ocr: a unified visual-linguistic model for multilingual document layout parsingThe
May not be reproduced without permission:AI productivity tools " Technical solution for rapid deployment of dots.ocr services
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