Best practices for using SQLAlchemy with multiprocessing

Question

I have a class which creates a SQLAlchemy engine object. (I've left out some of the config args and replaced with <> tags).

class DBClient:
    def __init__(self, isolation_level='AUTOCOMMIT'):
        logging.info('Creating DB connection')
        engine_string = sqlalchemy.engine.url.URL(
            "postgresql+psycopg2",
            username= <username>
            password= <password>
            database= <database>
            port= <port>
            host= <host>
        )

        self._engine = sqlalchemy.create_engine(
            engine_string,
            pool_size=1,
            isolation_level=isolation_level,
            pool_pre_ping=True,
            connect_args={
                "keepalives": 1,
                "keepalives_idle": 30,
                "keepalives_interval": 10,
                "keepalives_count": 5,
            }
        )

I have multiple processes which read from a database using this client class. Would it be preferable for me to instantiate a new instance of this class inside each process or to find a way to pass it into the multiprocess pool map? I'm currently seeing the following error when trying to pass an instance of this class to a pool map, but am not sure if I should look for a way around it or continue with an engine per process.

    return self._map_async(func, iterable, mapstar, chunksize).get()
  File "/usr/lib/python3.6/multiprocessing/pool.py", line 644, in get
    raise self._value
  File "/usr/lib/python3.6/multiprocessing/pool.py", line 424, in _handle_tasks
    put(task)
  File "/usr/lib/python3.6/multiprocessing/connection.py", line 206, in send
    self._send_bytes(_ForkingPickler.dumps(obj))
  File "/usr/lib/python3.6/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
TypeError: can't pickle _thread.RLock objects