Multithreaded Flask application causes stack error in rpy2 R process

rlock is an instance of a Python's threading.Rlock. It should take care of multithreading issues.

However, multitprocessing can cause a similar issue if the embedded R is shared across child processes. The code for this demo script showing parallel processing with R and Python processes illustrate this: https://github.com/rpy2/rpy2/blob/master/doc/_static/demos/multiproc_lab.py

I think that the way around this is to configure Flask, or most likely your wsgi layer, to create isolated child processes, or have all of your Flask processes delegate R calculations to a secondary process (created on the fly, or in a pool of processes waiting for tasks to perform).

As other answers for similar questions have implied, Flask users will need to initialize and run rpy2 outside of the WSGI context to prevent the embedded R process from crashing. I accomplished this with Celery, where workers provide an environment separate from Flask to handle requests made in R.

I used the low-level rinterface library as mentioned in the question, and wrote Celery tasks using classes

import rpy2.rinterface as rinterfacefrom celery import Celerycelery = Celery('tasks', backend='redis://', broker='redis://')class Rpy2Task(Task):       def __init__(self):        self.name = "rpy2"    def run(self, args):            rinterface.initr()        r_func = rinterface.baseenv['source']('your_R_script.R')        r_func[0](args)         passRpy2Task = celery.register_task(Rpy2Task())async_result = Rpy2Task.delay(args)

Calling rinterface.initr() anywhere but in the body of the task run by the worker results in the aforementioned crash. Celery is usually packaged with redis, and I found this a useful way to support exchanging information between R and Python, but of course Rpy2 also provides flexible ways of doing this.