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Shut down kernels in parallel...
Shut down kernels in parallel When stopping the notebook server, it currently sends a shutdown request to each kernel and then waits for the process to finish. This can be slow if you have several kernels running. This makes it issues all the shutdown requests before waiting on the processes, so shutdown happens in parallel. KernelManager (and MultiKernelManager) gain three new public API methods to allow this: * request_shutdown (promoted from a private method) * wait_shutdown (refactored out of shutdown_kernel) * cleanup (refactored out of shutdown_kernel)

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test_kernel.py
95 lines | 3.2 KiB | text/x-python | PythonLexer
#-------------------------------------------------------------------------------
# Copyright (C) 2012 The IPython Development Team
#
# Distributed under the terms of the BSD License. The full license is in
# the file COPYING, distributed as part of this software.
#-------------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
from __future__ import print_function
# Standard library imports
import sys
import unittest
# Local imports
from IPython.kernel.inprocess.blocking import BlockingInProcessKernelClient
from IPython.kernel.inprocess.manager import InProcessKernelManager
from IPython.kernel.inprocess.ipkernel import InProcessKernel
from IPython.testing.decorators import skipif_not_matplotlib
from IPython.utils.io import capture_output
from IPython.utils import py3compat
if py3compat.PY3:
from io import StringIO
else:
from StringIO import StringIO
#-----------------------------------------------------------------------------
# Test case
#-----------------------------------------------------------------------------
class InProcessKernelTestCase(unittest.TestCase):
def setUp(self):
self.km = InProcessKernelManager()
self.km.start_kernel()
self.kc = BlockingInProcessKernelClient(kernel=self.km.kernel)
self.kc.start_channels()
@skipif_not_matplotlib
def test_pylab(self):
""" Does pylab work in the in-process kernel?
"""
kc = self.kc
kc.execute('%pylab')
msg = get_stream_message(kc)
self.assert_('matplotlib' in msg['content']['data'])
def test_raw_input(self):
""" Does the in-process kernel handle raw_input correctly?
"""
io = StringIO('foobar\n')
sys_stdin = sys.stdin
sys.stdin = io
try:
if py3compat.PY3:
self.kc.execute('x = input()')
else:
self.kc.execute('x = raw_input()')
finally:
sys.stdin = sys_stdin
self.assertEqual(self.km.kernel.shell.user_ns.get('x'), 'foobar')
def test_stdout(self):
""" Does the in-process kernel correctly capture IO?
"""
kernel = InProcessKernel()
with capture_output() as io:
kernel.shell.run_cell('print("foo")')
self.assertEqual(io.stdout, 'foo\n')
kc = BlockingInProcessKernelClient(kernel=kernel)
kernel.frontends.append(kc)
kc.shell_channel.execute('print("bar")')
msg = get_stream_message(kc)
self.assertEqual(msg['content']['data'], 'bar\n')
#-----------------------------------------------------------------------------
# Utility functions
#-----------------------------------------------------------------------------
def get_stream_message(kernel_client, timeout=5):
""" Gets a single stream message synchronously from the sub channel.
"""
while True:
msg = kernel_client.get_iopub_msg(timeout=timeout)
if msg['header']['msg_type'] == 'stream':
return msg
if __name__ == '__main__':
unittest.main()