Deploying the development environment is mainly divided into the following steps:
- Creating a Python 3.12 virtual environment with conda and activating it
- Cloning the GitHub repository and installing PyTorch (to match the CUDA version) and other dependencies
- Download the model weights via a dedicated script, note that the save path cannot contain period characters
- Optional use of Docker images to circumvent environment configuration issues
Key considerations include the need to precisely specify the version when installing PyTorch (e.g., torch==2.7.0), and that model weights are downloaded by default to the . /weights/DotsOCR directory. The official recommendation is to deploy with vLLM for best performance, but the HuggingFace inference solution is also available.
This answer comes from the articledots.ocr: a unified visual-linguistic model for multilingual document layout parsingThe
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