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    "## Asynchronous Flow\n",
    "\n",
    "Synchronous from outside, Jina runs asynchronously underneath: it manages the eventloop(s) for scheduling the jobs. In some scenario, user wants more control over the eventloop, then `AsyncFlow` comes to use. In the example below, Jina is part of the integration where another heavy-lifting job is running concurrently:"
   ]
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    "#Run this command to install Jina in this notebook \n",
    "!pip install jina"
   ]
  },
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     "name": "#%%\n"
    }
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   "source": [
    "import asyncio\n",
    "from jina import AsyncFlow\n",
    "from jina.types.document.generators import from_ndarray\n",
    "\n",
    "\n",
    "async def run_async_flow_5s():  # WaitDriver pause 5s makes total roundtrip ~5s\n",
    "    with AsyncFlow().add(uses='- !WaitDriver {}') as f:\n",
    "        await f.index(from_ndarray(numpy.random.random([5, 4])), on_done=print)\n",
    "\n",
    "async def heavylifting():  # total roundtrip takes ~5s\n",
    "    print('heavylifting other io-bound jobs, e.g. download, upload, file io')\n",
    "    await asyncio.sleep(5)\n",
    "    print('heavylifting done after 5s')\n",
    "\n",
    "async def concurrent_main():  # about 5s; but some dispatch cost, can't be just 5s, usually at <7s\n",
    "    await asyncio.gather(run_async_flow_5s(), heavylifting())"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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     "name": "#%% md\n"
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   "source": [
    "`AsyncFlow` is very useful when using Jina inside Jupyter Notebook. As Jupyter/ipython already manages an eventloop and thanks to [`autoawait`](https://ipython.readthedocs.io/en/stable/interactive/autoawait.html), the following code can run out-of-the-box in Jupyter:"
   ]
  },
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   "execution_count": null,
   "metadata": {
    "collapsed": false,
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     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from jina.types.document.generators import from_ndarray\n",
    "from jina import AsyncFlow\n",
    "import numpy\n",
    "\n",
    "with AsyncFlow().add() as f:\n",
    "    f.index(from_ndarray(numpy.random.random([5, 4])), on_done=print)"
   ]
  }
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