Remarks

  • Define & start the Flow via an explicit function call inside if __name__ == '__main__'. For example

    from jina import Flow, Executor, requests
    
    class CustomExecutor(Executor):
        @requests
        def foo(self, **kwargs):
            ...
    
    def main():
        f = Flow().add(uses=CustomExecutor)
        with f:
            ...
    
    if __name__ == '__main__':
        main()
    
    from jina import Flow, Executor, requests
    
    class CustomExecutor(Executor):
        @requests
        def foo(self, **kwargs):
            ...
    
    f = Flow().add(uses=CustomExecutor)
    with f:
        ...
    
    """
    # error
    This probably means that you are not using fork to start your
    child processes and you have forgotten to use the proper idiom
    in the main module:
    
        if _name_ == '_main_':
            freeze_support()
            ...
    
    The "freeze_support()" line can be omitted if the program
    is not going to be frozen to produce an executable.
    
    """
    
  • Declare Executors on the top-level of the module

    class CustomExecutor(Executor):
        @requests
        def foo(self, **kwargs):
            ...
    
    def main():
        f = Flow().add(uses=Executor)
        with f:
            ...
    
    def main():
        class CustomExecutor(Executor):
            @requests
            def foo(self, **kwargs):
                ...
    
        f = Flow().add(uses=Executor)
        with f:
            ...
    
  • Always provide absolute path

    While passing filepaths to different jina arguments (e.g.- uses, py_modules), always pass the absolute path.

multiprocessing Spawn

Few cases require to use spawn start method for multiprocessing. (e.g.- Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the ‘spawn’ start method)

  • Please set JINA_MP_START_METHOD=spawn before starting the Python script to enable this.

    Hint

    There’s no need to set this for Windows, as it only supports spawn method for multiprocessing.

  • Avoid un-picklable objects

    Here’s a list of types that can be pickled in Python. Since spawn relies on pickling, we should avoid using code that cannot be pickled.

    Hint

    Here are a few errors which indicates that you are using some code that is not pickable.

    pickle.PicklingError: Can't pickle: it's not the same object
    AssertionError: can only join a started process
    

    Inline functions, such as nested or lambda functions are not picklable. Use functools.partial instead.

Debugging Executor in a Flow

Standard Python breakpoints will not work inside Executor methods when called inside a Flow context manager. Nevertheless, import epdb; epdb.set_trace() will work just as a native python breakpoint. Note that you need to pip install epdb to have acces to this type of breakpoints.

from jina import Flow, Executor, requests
 
class CustomExecutor(Executor):
    @requests
    def foo(self, **kwargs):
        a = 25
        import epdb; epdb.set_trace() 
        print(f'\n\na={a}\n\n')
 
def main():
    f = Flow().add(uses=CustomExecutor)
    with f:
        f.post(on='')

if __name__ == '__main__':
    main()
from jina import Flow, Executor, requests
 
class CustomExecutor(Executor):
    @requests
    def foo(self, **kwargs):
        a = 25
        breakpoint()
        print(f'\n\na={a}\n\n')
 
def main():
    f = Flow().add(uses=CustomExecutor)
    with f:
        f.post(on='')
 
if __name__ == '__main__':
    main()