在这种情况下如何使用龙卷风协程？

Question

我创建了龙卷风服务器，它接受python和matlab代码并执行它。这是服务器代码。

from zmq.eventloop.zmqstream import ZMQStream
from zmq.eventloop import ioloop
ioloop.install()

from functools import partial
from tornado import web, gen, escape
from tornado import options, httpserver
from tornado.concurrent import Future

settings = dict()
settings['autoreload'] = True 
settings['debug'] = True

from jupyter_client import MultiKernelManager

reply_futures = {}
kids = []

class MainHandler(web.RequestHandler):
    def get(self):
        self.write("Hello")

class ExecuteHandler(web.RequestHandler):
    def get(self):
        self.write("approaching execute")

    def post(self):
        data = escape.json_decode(self.request.body)
        print data
        self.application.execute_code(data)

class Application(web.Application):

    def __init__(self):

        handlers = []
        handlers.append((r"/", MainHandler))
        handlers.append((r"/execute", ExecuteHandler))
        web.Application.__init__(self, handlers, **settings)
        self.km = MultiKernelManager()
        self.setup_kernels()

    def setup_kernels(self):

        matlab_kernel_id = self.km.start_kernel(kernel_name="matlab")
        python_kernel_id = self.km.start_kernel(kernel_name="python")

        self.matlab_kernel_id = matlab_kernel_id
        self.python_kernel_id = python_kernel_id

        matkab_kernel_client = self.km.get_kernel(matlab_kernel_id).client()
        matkab_kernel_client.start_channels()

        python_kernel_client = self.km.get_kernel(python_kernel_id).client()
        python_kernel_client.start_channels()

        self.matkab_kernel_client = matkab_kernel_client
        self.python_kernel_client = python_kernel_client

        matlab_iopub_stream = ZMQStream(matkab_kernel_client.iopub_channel.socket)
        matlab_shell_stream = ZMQStream(matkab_kernel_client.shell_channel.socket)

        python_iopub_stream = ZMQStream(python_kernel_client.iopub_channel.socket)
        python_shell_stream = ZMQStream(python_kernel_client.shell_channel.socket)

        matlab_iopub_stream.on_recv_stream(partial(self.reply_callback, matkab_kernel_client.session))
        matlab_shell_stream.on_recv_stream(partial(self.reply_callback, matkab_kernel_client.session))

        python_iopub_stream.on_recv_stream(partial(self.reply_callback, python_kernel_client.session))
        python_shell_stream.on_recv_stream(partial(self.reply_callback, python_kernel_client.session))

    def reply_callback(self, session, stream, msg_list):
        idents, msg_parts = session.feed_identities(msg_list)
        reply = session.deserialize(msg_parts)

        if "stream" == reply["msg_type"]:
            print reply["content"]["text"]
        parent_id = reply['parent_header'].get('msg_id')
        reply_future = reply_futures.get(parent_id)
        if reply_future:
            reply_future.set_result(reply)

    def execute_code(self, data):

        matlab_code = data['matlab_code']
        python_code = data['python_code']

        self.execute_matlab_then_python(matlab_code, python_code)

    @gen.coroutine
    def execute_matlab_then_python(self, matlab_code, python_code):

        print "executing matlab code"
        parent_id1 = self.matkab_kernel_client.execute(matlab_code)
        f1 = reply_futures[parent_id1] = Future()
        yield f1    

        print "executing python code"
        parent_id2 = self.python_kernel_client.execute(python_code)
        f2 = reply_futures[parent_id2] = Future()
        yield f2

    def shutdown_kernels(self):
        self.km.get_kernel(self.matlab_kernel_id).cleanup_connection_file()
        self.km.shutdown_kernel(self.matlab_kernel_id)

        self.km.get_kernel(self.python_kernel_id).cleanup_connection_file()
        self.km.shutdown_kernel(self.python_kernel_id)

if __name__ == '__main__':

    options.parse_command_line()
    app = Application()
    server = httpserver.HTTPServer(app)
    server.listen(8888)

    try:
        ioloop.IOLoop.current().start()
    except KeyboardInterrupt:
        print 'going down'
    finally:    
        app.shutdown_kernels()

我用来访问的客户代码是，这里

import json
import requests

matlab_code = "a=magic(3);disp(a)"
python_code = "print 'Hello World!!'"

data = {}
data['matlab_code'] = matlab_code
data['python_code'] = python_code

r = requests.post('http://0.0.0.0:8888/execute', json.dumps(data))

我关心的是保持执行的顺序，这样python代码只有在matlab完成后才能执行。我使用jupyter_client来执行matlab/python代码。我在这里使用python27。问题是当我提交代码时，它抛出< code > type error:' NoneType ' object is not iterable 。这里有一个堆栈-跟踪它。

[E 160809 15:17:51 ioloop:633] Exception in callback None
    Traceback (most recent call last):
      File "/usr/local/lib/python2.7/dist-packages/tornado/ioloop.py", line 887, in start
        handler_func(fd_obj, events)
      File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 275, in null_wrapper
        return fn(*args, **kwargs)
      File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
        self._handle_recv()
      File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
        self._run_callback(callback, msg)
      File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
        callback(*args, **kwargs)
      File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 275, in null_wrapper
        return fn(*args, **kwargs)
      File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 191, in 
        self.on_recv(lambda msg: callback(self, msg), copy=copy)
      File "tornado_test.py", line 86, in reply_callback
        reply_future.set_result(reply)
      File "/usr/local/lib/python2.7/dist-packages/tornado/concurrent.py", line 276, in set_result
        self._set_done()
      File "/usr/local/lib/python2.7/dist-packages/tornado/concurrent.py", line 320, in _set_done
        for cb in self._callbacks:
    TypeError: 'NoneType' object is not iterable

我不明白这里有什么问题？

Answer 1

不幸的是，神秘的错误消息“'NoneType'对象不可迭代”意味着您在同一个 Future 对象上多次调用set_result。我对 zmq 或 jupyter 内核接口了解不多，但我的猜测是，要么两个不同内核的执行方法返回的 ID 重叠，要么每次执行调用（来自两个不同的流？