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How to use multi-threading to speed up Python imports? [duplicate]

It all began last night when I was making a script that required 8 or so packages including pygame.mixer which on my computer importing this takes a few seconds.
This meant that before the script even started I had to wait 10 or so seconds for all the imports to load. Because I want the script to obviously be as fast as possible could I start running the script while getting the imports with something like this:
import threading
def import_modules():
import tkinter as tk
from pygame import mixer
import json
import webbrowser
print('imports finished')
a = threading.Thread(target=import_modules)
a.start()
for i in range(10000):
print('Getting Modules')
So my question is:
Is this considered bad practice and will it cause problems?
If so are there alternatives I could use?
Or is it OK to do this?

If you are using CPython, this might not yield as much improvement as you'd expect.
CPython has a Global Interpreter Lock ("GIL") that ensures that only one thread at a time can be executing Python bytecode.
So whenever the import thread is executing Python code, the other thread is not running. The GIL is released by a thread when it is e.g. waiting on I/O. So there will be some time savings because of that.
There is a difference of opinion as to whether tkinter is truly thread-safe. It is still considered wise to run the tkinter main loop in the original thread, and to not invoke tkinter calls from other threads, because that can lead to crashes.
The GIL also can cause problems for GUI programs. If you are using a second thread for a long-running calculation, the user interface might become less responsive. There are at least two possible solutions. The first one is to split the long-running calculation up into small pieces which are each executed by a after method. The second is to run the calculation in a different process.
Follow-up questions from the comments:
is there anything else to speed up execution time?
The first thing you must to do is measure; what exactly causes the problem. Then you can look into the problem areas and try to improve them.
For example module load times. Run your app under a profiler to see how long the module loads take and why.
If pygame.mixer takes too long to load, you could use your platform's native mixer. UNIX-like operating systems generally have a /dev/mixer device, while ms-windows has different API's for it. Using those definitely won't take 10 seconds.
There is a cost associated with this: you will loose portability between operating systems.
What are the alternatives
Using multiple cores is a usual tactic to try and speed things up. Currently on CPython the only general way get code to run in parallel on multiple cores is with multiprocessing or concurrent.futures.
However it depends on the nature of your problem if this tactic can work.
If your problem involves doing the same calculations over a huge set of data, that is relatively easy to parallelize. In that case you can expect a maximal speedup roughly equivalent to the numbers of cores you use.
It could be that your problem consists of multiple steps, each of which depends on the result of a previous step. Such problems are serial in nature and are much harder to execute in parallel.
Other ways to possible speed things up could be to use another Python implementation like Pypy. Or you could use cython together with type hints to convert performance-critical parts to compiled C code.

I understand this is an old thread but i was looking for a way to minimize the loading time of my application, and wanted the user to see the gui so he can interact with it while other module being imported in background
i have read some answers suggesting a lazy import techniques, which i found complicated "for me", then i stumbled here with a suggest to use threading to import modules in background, then i gave it a shot, and found out it is the most brilliant idea that fits my needs
below is a code for an example gui application using PySimpleGUI which ask the user to enter a url and it will open it in the default browser window, the only module required to do so is webbrowser, so this job could be done while other modules loading
I added comments in this code to explain mostly all parts, hope it will help someone,
tested on python 3.6, windows10.
please note: this is just a dummy code as a showcase.
# import essentials first
import PySimpleGUI as sg
import time, threading
# global variable names to reference to the imported modules, this way will
# solve the problem of importing inside a function local namespace
pg = None
js = None
wb = None
progress = 0 # for our progress bar
def importer():
# we will simulate a time consuming modules by time.sleep()
global progress
progress = 10
start = time.time()
global pg, js, wb
import pygame as pg
time.sleep(3)
print(f'done importing pygame mixer in {time.time()-start} seconds')
progress = 40
start = time.time()
import webbrowser as wb
time.sleep(2)
print(f'done importing webbrowser in {time.time()-start} seconds')
progress = 70
start = time.time()
import json as js
time.sleep(10)
print(f'done importing json in {time.time()-start} seconds')
progress = 100
print('imports finished')
# start our importer in a separate thread
threading.Thread(target=importer).start()
# main app
def main():
# window layout
layout = [[sg.Text('Enter url:', size=(15,1)), sg.Input(default_text='https://google.com', size=(31, 1), key='url')],
[sg.Text('Loading modules:', size=(15,1), key='status'),
sg.ProgressBar(max_value=100, orientation='horizontal', size=(20,10), key='progress')],
[sg.Button('Open url', disabled=True, key='open_url'), sg.Button('joysticks', disabled=True, key='joysticks'), sg.Cancel()]]
window = sg.Window('test application for lazy imports', layout=layout) # our window
while True: # main application loop
event, values = window.Read(timeout=10) # non blocking read from our gui
if event in [None, 'Cancel']:
window.Close()
break
elif event == 'open_url':
wb.open(values['url'])
print('helllooooooooooooo')
elif event == 'joysticks':
# show joystics number currently connected
pg.init()
n = pg.joystick.get_count() # Get count of joysticks
sg.Popup(f'joysticks number currently connected to computer = {n}')
# open url button is disabled by default and will be enabled once our webbrowser module imported
if wb:
window.Element('open_url').Update(disabled= False)
if pg:
window.Element('joysticks').Update(disabled= False)
# progress bar
window.Element('progress').UpdateBar(progress)
if progress >= 100:
window.Element('status').Update('Loading completed', background_color='green')
main()

Is reusing same process name in loop situation possibly generate zombie process?

My script has to run over a day and its core cycle runs 2-3 times per a minute. I used multiprocessing to give a command simultaneously and each of them will be terminated/join within one cycle.
But in reality I found the software end up out of swap memory or computer freezing situation, I guess this is caused by accumulated processes. I can see on another session while running program, python PID abnormally increasing by time. So I just assume this must be something process thing. What I don't understand is how it happens though I made sure each cycle's process has to be finished on that cycle before proceed the next one.
so I am guessing, actual computing needs more time to progress 'terminate()/join()' job, so I should not "reuse" same object name. Is this proper guessing or is there other possibility?
def function(a,b):
try:
#do stuff # audio / serial things
except:
return
flag_for_2nd_cycle=0
for i in range (1500): # main for running long time
#do something
if flag_for_2nd_cycle==1:
while my_process.is_alive():
if (timecondition) < 30: # kill process if it still alive
my_process.terminate()
my_process.join()
flag_for_2nd_cycle=1
my_process=multiprocessing.process(target=function, args=[c,d])
my_process.start()
#do something and other process jobs going on, for example
my_process2 = multiprocessing.process() ##*stuff
my_process2.terminate()
my_process2.join()

Based on your comment, you are controlling three projectors over serial ports.
The simplest way to do that would be to simply open three serial connections (using pySerial). Then run a loop where you check for available data each of the connections and if so, read and process it. Then you send commands to each of the projectors in turn.
Depending on the speed of the serial link you might not need more than this.

How to run tasks periodically without interrupting the whole program

I have a program that constantly runs if it receives an input, it'll do a task then go right back to awaiting input. I'm attempting to add a feature that will ping a gaming server every 5 minutes, and if the results every change, it will notify me. Problem is, if I attempt to implement this, the program halts at this function and won't go on to the part where I can then input. I believe I need multithreading/multiprocessing, but I have no experience with that, and after almost 2 hours of researching and wrestling with it, I haven't been able to figure it out.
I have tried to use the recursive program I found here but haven't been able to adapt it properly, but I feel this is where I was closest. I believe I can run this as two separate scripts, but then I have to pipe the data around and it would become messier. It would be best for the rest of the program to keep everything on one script.
'''python
def regular_ping(IP):
last_status = None
while True:
present_status = ping_status(IP) #ping_status(IP) being another
#program that will return info I
#need
if present_status != last_status:
notify_output(present_status) #notify_output(msg) being a
#program that will notify me of
# a change
last_status = present_status
time.sleep(300)
'''
I would like this bit of code to run on its own, notifying me of a change (if there is one) every 5 minutes, while the rest of my program also runs and accepts inputs. Instead, the program stops at this function and won't run past it. Any help would be much appreciated, thanks!

You can use a thread or a process for this. But since this is not a CPU bound operation, overhead of dedicating a process is not worth it. So a thread would be enough. You can implement it as follows:
import threading
thread = threading.Thread(target=regular_ping, args=(ip,))
thread.start()
# Rest of the program
thread.join()

Python - Multithreads for calling the same function to run in parallel and independently

I'm new in Python and I'm struggling a lot trying to solve a problem. I have three programs running. One of them has the objective to send data, the other to receive and the third one works in the middle (transparently). The difficulty is happening with this third one, which I'm calling delay_loss.py.
It has to emulate delay of packets before delivering them to the receiving program.
Searching a lot I have found a solution (multithreading), which I'm not sure is the best one. Since delay_loss.py can receive a lot of packets "at once" and has to select for each a random time to emulate a delay in the network, I have to be able to send each packet to the receiving program after the random time selected for this packet, independently of the others.
I'm trying to use multithread for this, and I think I'm not using it correctly because all the packets are sent at the same time after some time. The threads seem to not be running the function send_up() independently.
Part of the code of delay_loss.py is shown below:
import threading
import time
from multiprocessing.dummy import Pool as ThreadPool
...
pool = ThreadPool(window_size)
def send_up (pkt, time_delay, id_pkt):
time.sleep(time_delay)
sock_server.sendto(pkt, (C_IP, C_PORT))
def delay_pkt(pkt_recv_raw, rtt, average_delay, id_pkt):
x = random.expovariate(1/average_delay)
time_delay = rtt/(2+x)
pool.apply_async(send_up, [pkt_recv_raw, time_delay, id_pkt])
...
delay_pkt(pkt_recv_raw, rtt, average_delay, id_pkt_recv)
id_pkt_recv += 1
If anyone has some idea of what am I doing wrong. Or just to say don't take this approach of multithreads for doing this task, it would be of much help!
Thanks in advance :)

I have found a solution to my problem. I was using the pool without necessity.
It is much simpler to just use the threading.Timer() function, as shown below:
t = threading.Timer(time_delay, send_up, [pkt_recv_raw, id_pkt])
t.start()

live plotting in matplotlib while performing a measurement that takes time

I would like to perform a measurement and plot a graph while the measurement is
running. This measurements takes quite some time in python (it has to retrieve data over a slow connection). The problem is that the graph freezes when measuring. The measurement
consists of setting a center wavelength, and then measuring some signal.
My program looks something like this:
# this is just some arbitrary library that has the functions set_wavelength and
# perform_measurement
from measurement_module import set_wavelength, perform_measurement
from pylab import *
xdata = np.linspace(600,1000,30) # this will be the x axis
ydata = np.zeros(len(xdata)) # this will be the y data. It will
for i in range(len(xdata)):
# this call takes approx 1 s
set_wavelength(xdata[i])
# this takes approx 10 s
ydata[i] = perform_measurement(xdata)
# now I would like to plot the measured data
plot(xdata,ydata)
draw()
This will work when it is run in IPython with the -pylab module switched on,
but while the measurement is running the figure will freeze. How can modify
the behaviour to have an interactive plot while measuring?
You cannot simply use pylab.ion(), because python is busy while performing the measurements.
regards,
Dirk

You can, though maybe a bit awkward, run the data-gathering as a serparate process. I find Popen in the subprocess module quite handy. Then let that data-gathering script save what it does to disk somewhere and you use
Popen.poll()
To check if it has completed.
It ought to work.

I recommend buffering the data in large chunks and render/re-render when the buffer fills up. If you want it to be nonblocking look at greenlets.
from gevent.greenlet import Greenlet
import copy
def render(buffer):
'''
do rendering stuff
'''
pass
buff = ''
while not_finished:
buff = connection.read()
g = Greenlet(render, copy.deepcopy(buff))
g.start()

Slow input and output is the perfect time to use threads and queues in Python. Threads have there limitations, but this is the case where they work easily and effectively.
Outline of how to do this:
Generally the GUI (e.g., the matplotlib window) needs to be in the main thread, so do the data collection in a second thread. In the data thread, check for new data coming in (and if you do this in some type of infinite polling loop, put in a short time.sleep to release the thread occasionally). Then, whenever needed, let the main thread know that there's some new data to be processed/displayed. Exactly how to do this depends on details of your program and your GUI, etc. You could just use a flag in the data thread that you check for from the main thread, or a theading.Event, or, e.g., if you have a wx backend for matplotlib wx.CallAfter is easy. I recommend looking through one of the many Python threading tutorials to get a sense of it, and also threading with a GUI usually has a few issues too so just do a quick google on threading with your particular backend. This sounds cumbersome as I explain it so briefly, but it's really pretty easy and powerful, and will be smoother than, e.g., reading and writing to the same file from different processes.

Take a look at Traits and Chaco, Enthought's type system and plotting library. They provide a nice abstraction to solve the problem you're running into. A Chaco plot will update itself whenever any of its dependencies change.