Accessing celery worker instance inside the task

I want to use jupyter kernels in side the celery worker. There will be one Jupyter Kernel for each Celery Worker.

To achieve it I am overriding the default Worker class of the celery, at the initialisation of the worker I am starting the jupyter kernel and with the stop method I am shutting down the jupyter kernel.

The current problem I am facing is how can I access that kernel instance inside the task while the task is running ?

Is there any better way to override the Worker class definition for the celery application than app.Worker = CustomWorker ?

Here is the celery config with the Custom Worker.

from __future__ import absolute_import, unicode_literals
from celery import Celery
from jupyter_client import MultiKernelManager

app = Celery('proj',
    broker='redis://',
    backend='redis://',
    include=['tasks'])

app.conf.update(
    result_expires=3600
)

class CustomWorker(app.Worker):
    def __init__(self, *args, **kwargs):
        self.km = MultiKernelManager()
        self.kernel_id = self.km.start_kernel()
        print("Custom initializing")
        self.kernel_client = km.get_kernel(kernel_id).client()
        super(CustomWorker, self).__init__(*args, **kwargs)

    def on_close(self):
        self.km.shutdown_kernel(self.kernel_id)
        super(CustomWorker, self).on_close()

app.Worker = CustomWorker

if __name__ == '__main__':
    app.start()

Here is a skeleton of tasks.py

from __future__ import absolute_import, unicode_literals
from celery import app

from celery import Task
from tornado import gen
from jupyter_client import MultiKernelManager
from zmq.eventloop import ioloop
from zmq.eventloop.zmqstream import ZMQStream
ioloop.install()

reply_futures = {}

# This is my celery task where I pass the arbitary python code to execute on
# some celery worker(actually to the corresponding kernel)
@app.task
def pythontask(code):
    # I don't know how to get the kernel_client for current celery worker !!?
    kernel_client = self.get_current_worker().kernel_client
    mid = kernel_client.execute(code)

    # defining the callback which will be executed when message arrives on
    # zmq stream
    def reply_callback(session, stream, msg_list):
        idents, msg_parts = session.feed_identities(msg_list)
        reply = session.deserialize(msg_parts)
        parent_id = reply['parent_header'].get('msg_id')
        reply_future = reply_futures.get(parent_id)
        if reply_future:
            reply_future.set_result(reply)

    @gen.coroutine
    def execute(kernel_client, code):
        msg_id = kernel_client.execute(code)
        f = reply_futures[msg_id] = Future()
        yield f
        raise gen.Return(msg_id)

    # initializing the zmq streams and attaching the callback to receive message
    # from the kernel
    shell_stream = ZMQStream(kernel_client.shell_channel.socket)
    iopub_stream = ZMQStream(kernel_client.iopub_channel.socket)
    shell_stream.on_recv_stream(partial(reply_callback, kernel_client.session))
    iopub_stream.on_recv_stream(partial(reply_callback, kernel_client.session))

    # create a IOLoop
    loop = ioloop.IOLoop.current()
    # listen on the streams
    msg_id = loop.run_sync(lambda: execute(kernel_client,code))
    print(reply_msgs[msg_id])
    reply_msgs[msg_id] = []

    # Disable callback and automatic receiving.
    shell_stream.on_recv_stream(None)
    iopub_stream.on_recv_stream(None)

Adding that worker instance information to the request object solved my problem. to do that I overrode the _process_task method of the worker class.

def _process_task(self, req):
  try:
    req.kwargs['kernel_client'] = self.kernel_client
    print("printing from _process_task {}".format(req.kwargs))
    req.execute_using_pool(self.pool)
  except TaskRevokedError:
    try:
      self._quick_release()   # Issue 877
    except AttributeError:
      pass
  except Exception as exc:
    logger.critical('Internal error: %r\n%s',exc, traceback.format_exc(), exc_info=True)

Here is my task where I access the kernel_client

@app.task(bind=True)
def pythontask(self,code, kernel_client=None):

    mid = kernel_client.execute(code)

    print("{}".format(kernel_client))
    print("{}".format(mid))

This thing works only when I start workers in solo mode otherwise not it throws some pickling error. Anyways using solo workers is my requirement so this solution works for me

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