nlpmultiprocessingpython-multiprocessingspacy

How can I share a complex spaCy NLP model across multiple Python processes to minimize memory usage?


I'm working on a multiprocessing python application where multiple processes need access to a large, pre-loaded spaCy NLP model (e.g., en_core_web_lg). Since the model is memory-intensive, I want to avoid loading it separately in each process, since I quickly run out of main memory and the object is read-only. Instead, I’d like to load it once in a shared location so that all processes can read from it without duplicating memory usage.

I have looked into multiprocessing.Manager and multiprocessing.shared_memory, but these approaches seem better suited to NumPy arrays, raw data buffers or simple objects, not complex objects with internal references like an NLP model. I have also looked into MPI's MPI.Win.Allocate_shared() but I ran into the same issues. Using a redis server and make rank 0 do all the processing works with MPI, but since all the processing is done by a single rank, it defeats the propose I had for using multiprocessing.

Any suggestions or examples would be greatly appreciated! Thank you!


Solution

  • I would strongly advise you not to treat NLP models like any other Python object. I would always prefer to load an NLP model using a microservice approach, which is more aligned with ML/software engineering best practices by separating the model logic from the main application.

    Instead of loading the model in each process (which can be memory-intensive), the model is loaded just once in a dedicated service. This setup allows the model to be used by multiple parts of the application without duplicating memory usage, making it efficient, modular, and scalable. Not only is your concern about memory efficiency addressed, but scalability and modularity are also improved.

    An example of implementing such a microservice using FastAPI + Docker could look like this:

    # main.py: FastAPI service with spaCy model
    from fastapi import FastAPI
    import spacy
    
    app = FastAPI()
    nlp = spacy.load("en_core_web_lg")  # Load model once
    
    @app.post("/process/")
    async def process_text(text: str):
        doc = nlp(text)
        return {"tokens": [(token.text, token.pos_) for token in doc]}
    

    To containerize above FastAPI service:

    # Dockerfile for the NLP model microservice
    FROM python:3.9-slim
    COPY requirements.txt .
    RUN pip install -r requirements.txt && python -m spacy download en_core_web_lg
    COPY . /app
    WORKDIR /app
    CMD ["gunicorn", "-w", "4", "-k", "uvicorn.workers.UvicornWorker", "main:app"]