Installing and setting up DeepSeek-OCR is a straightforward process for developers familiar with Python and modern deep learning frameworks such as PyTorch. The model is open-source and available on GitHub under an MIT license, which allows for both local and production deployments without commercial restrictions. To begin, you’ll need a system running Linux or Windows with a compatible GPU—ideally an NVIDIA A100, H100, or an equivalent model with at least 16 GB of VRAM. Start by creating a new Python environment (using tools like conda or venv) and installing the required dependencies. The key packages include torch, transformers, timm, and flash-attn for efficient attention computation. Once your environment is ready, clone the official DeepSeek-OCR repository and download the pretrained model weights provided in the release section of the project’s GitHub page.
After installation, the typical workflow involves running an inference script that takes document images or PDFs as input. DeepSeek-OCR supports multiple compression modes, so you can choose between higher accuracy or faster throughput depending on your application. For example, running the command
python run_ocr.py --input sample.pdf --output output.json --compression 10x
will process the file with a 10× compression ratio and output structured text in JSON format. Developers can also enable Markdown or HTML output to preserve layout and formatting. The model can be integrated into Python applications directly through its API or wrapped inside a REST endpoint for remote inference. For containerized deployment, Dockerfiles are provided, making it easy to spin up the system in a cloud or on-premises environment. To optimize performance, it’s important to configure GPU settings and batch sizes according to your hardware. For local testing, a single GPU can handle hundreds of pages per minute, while multi-GPU setups or distributed frameworks like DeepSpeed or Ray can scale to millions of pages daily. Developers processing large archives may also want to integrate preprocessing steps—like de-skewing, noise reduction, or grayscale conversion—to improve accuracy. In summary, setting up DeepSeek-OCR involves standard Python-based installation steps, a simple inference command for testing, and optional scaling configurations for enterprise use. Once running, it provides a flexible, high-performance OCR pipeline capable of handling a wide range of document workloads efficiently.
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