a new thread for running a cell in ipython/jupyter notebook

Question

Sometimes it takes a long time to run a single cell, while it is running, I would like to write and run other cells in the same notebook, accessing the variables in the same

Answer 1

It may not be an answer, but rather the direction to it. I did not saw anything like that, still I'm interested in this too.

My current findings suggesting that one need to define it's own custom cell magic. Good references would be the custom cell magic section in the documentation and two examples that I would consider:

• memit: magic memory usage benching for IPython https://gist.github.com/vene/3022718
• Illustrating Python multithreading vs multiprocessing: http://nathangrigg.net/2015/04/python-threading-vs-processes/

Both those links wrapping the code in a thread. That could be a starting point.

UPDATE: ngcm-tutorial at github has description of background jobs class

##github.com/jupyter/ngcm-tutorial/blob/master/Day-1/IPython%20Kernel/Background%20Jobs.ipynb
from IPython.lib import backgroundjobs as bg
jobs = bg.BackgroundJobManager()

def printfunc(interval=1, reps=5):
    for n in range(reps):
        time.sleep(interval)
        print('In the background... %i' % n)
        sys.stdout.flush()
    print('All done!')
    sys.stdout.flush()

jobs.new('printfunc(1,3)')
jobs.status()

UPDATE 2: Another option:

from IPython.display import display
from ipywidgets import IntProgress

import threading

class App(object):
    def __init__(self, nloops=2000):
        self.nloops = nloops
        self.pb = IntProgress(description='Thread loops', min=0, max=self.nloops)

    def start(self):
        display(self.pb)
        while self.pb.value < self.nloops:
            self.pb.value += 1 
        self.pb.color = 'red'

app = App(nloops=20000)

t = threading.Thread(target=app.start)

t.start()
#t.join()

Answer 2

Here is a little snippet that I came up with

def jobs_manager():
    from IPython.lib.backgroundjobs import BackgroundJobManager
    from IPython.core.magic import register_line_magic
    from IPython import get_ipython

    jobs = BackgroundJobManager()

    @register_line_magic
    def job(line):
        ip = get_ipython()
        jobs.new(line, ip.user_global_ns)

    return jobs

It uses IPython builtin module IPython.lib.backgroundjobs . So code is small and simple and no new dependencies are introduced.

I use it like this:

jobs = jobs_manager()

%job [fetch_url(_) for _ in urls]  # saves html file to disk
Starting job # 0 in a separate thread.

Then you can monitor the state with:

jobs.status()

Running jobs:
1 : [fetch_url(_) for _ in urls]

Dead jobs:
0 : [fetch_url(_) for _ in urls]

If job fails you can inspect stack trace with

jobs.traceback(0)

There is no way to kill a job. So I carefully use this dirty hack:

def kill_thread(thread):
    import ctypes

    id = thread.ident
    code = ctypes.pythonapi.PyThreadState_SetAsyncExc(
        ctypes.c_long(id),
        ctypes.py_object(SystemError)
    )
    if code == 0:
        raise ValueError('invalid thread id')
    elif code != 1:
        ctypes.pythonapi.PyThreadState_SetAsyncExc(
            ctypes.c_long(id),
            ctypes.c_long(0)
        )
        raise SystemError('PyThreadState_SetAsyncExc failed')

It raises SystemError in a given thread. So to kill a job I do

kill_thread(jobs.all[1])

To kill all running jobs I do

for thread in jobs.running:
    kill_thread(thread)

I like to use %job with widget-based progress bar https://github.com/alexanderkuk/log-progress like this:

%job [fetch_url(_) for _ in log_progress(urls, every=1)]

http://g.recordit.co/iZJsJm8BOL.gif

One can even use %job instead of multiprocessing.TreadPool:

for chunk in get_chunks(urls, 3):
    %job [fetch_url(_) for _ in log_progress(chunk, every=1)]

http://g.recordit.co/oTVCwugZYk.gif

Some obvious problems with this code:

1. You can not use arbitrary code in %job. There can be no assignments and not prints for example. So I use it with routines that store results on hard drive

2. Sometimes dirty hack in kill_thread does not work. I think that is why IPython.lib.backgroundjobs does not have this functionality by design. If thread is doing some system call like sleep or read exception is ignored.

3. It uses threads. Python has GIL , so %job can not be used for some heavy computations that take in python byte code