Mẹo về Python share data between scripts 2022 Chi Tiết

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Kinh Nghiệm Hướng dẫn Python share data between scripts Mới Nhất

Quý khách đang tìm kiếm từ khóa Python share data between scripts được Update vào lúc : 2022-10-02 01:20:27 . Với phương châm chia sẻ Kinh Nghiệm Hướng dẫn trong nội dung nội dung bài viết một cách Chi Tiết 2022. Nếu sau khi Read Post vẫn ko hiểu thì hoàn toàn hoàn toàn có thể lại Comments ở cuối bài để Ad lý giải và hướng dẫn lại nha.

Hope it’s OK to jot down my notes about this issue here.

Nội dung chính

    How do I share data between two python scripts?
    How do you link python scripts together?
    How do I share global variables across modules python?
    How do you call a variable from another file in python?

First of all, I appreciate the example in the OP a lot, because that is where I started as well – although it made me think shared is some built-in Python module, until I found a complete example [Tutor] Global Variables between Modules ??.

However, when I looked for “sharing variables

between scripts” (or processes) – besides the case when a Python script needs to use variables defined in other Python source files (but not necessarily running processes) – I mostly stumbled upon two other use cases:

    A script forks itself into multiple child processes, which then run in parallel (possibly on multiple processors) on the same PC
    A script spawns multiple other child processes, which then run in parallel (possibly on multiple processors) on the same PC

As such, most hits regarding “shared variables” and “interprocess communication” (IPC) discuss cases like these two; however, in both of these cases one can observe a “parent”, to which the “children” usually have a reference.

What I am interested in, however, is running multiple invocations of the same script, ran independently, and sharing data between those (as in Python: how to share an object instance across

multiple invocations of a script), in a singleton/single instance mode. That kind of problem is not really addressed by the above two cases – instead, it essentially reduces to the example in OP (sharing variables across two scripts).

Now, when dealing with this problem in Perl, there is IPC::Shareable; which “allows you to tie a variable to shared memory”, using “an integer number or 4

character string[1] that serves as a common identifier for data across process space”. Thus, there are no temporary files, nor networking setups – which I find great for my use case; so I was looking for the same in Python.

However, as accepted answer by @Drewfer notes: “You’re not going to be able to do what you want without storing the information somewhere external to the two instances of the

interpreter”; or in other words: either you have to use a networking/socket setup – or you have to use temporary files (ergo, no shared RAM for “totally separate python sessions”).

Now, even with these considerations, it is kinda difficult to find working examples (except for pickle) – also in the docs for mmap and

multiprocessing. I have managed to find some other examples – which also describe some pitfalls that the docs do not mention:

    Usage of mmap: working code in two different scripts Sharing Python data between processes using mmap | schmichael’s

    blog

      Demonstrates how both scripts change the shared value
      Note that here a temporary file is created as storage for saved data – mmap is just a special interface for accessing this temporary file

    Usage of multiprocessing: working code :

      Python multiprocessing RemoteManager under a multiprocessing.Process – working example of SyncManager (via manager.start()) with shared Queue;

      server(s) writes, clients read (shared data)

      Comparison of the multiprocessing module and pyro? – working example of BaseManager (via server.serve_forever()) with shared custom class; server writes, client reads and writes
      How to synchronize a python dict with multiprocessing – this answer has a great explanation of multiprocessing pitfalls, and is a working

      example of SyncManager (via manager.start()) with shared dict; server does nothing, client reads and writes

Thanks to these examples, I came up with an example, which essentially does the same as the mmap example, with approaches from the “synchronize a python dict” example – using BaseManager (via manager.start() through file path address) with shared list; both server and client read and write (pasted below). Note that:

In addition to all the pitfalls in the “synchronize a python dict” post, there are additional ones in case of a list. That post notes:

All manipulations of the dict must be done with methods and not dict assignments (syncdict[“blast”] = 2 will fail miserably because of the way multiprocessing shares custom objects)

The workaround to dict[‘key’] getting and setting, is the use of the dict public methods get and

update. The problem is that there are no such public methods as alternative for list[index]; thus, for a shared list, in addition we have to register __getitem__ and __setitem__ methods (which are private for list) as exposed, which means we also have to re-register all the public methods for list as well :/

Well, I think those were the most critical things; these are the two scripts – they can just be ran in separate terminals (server first); note developed on Linux

with Python 2.7:

a.py (server):

import multiprocessing

import multiprocessing.managers

import logging

logger = multiprocessing.log_to_stderr()

logger.setLevel(logging.INFO)

class MyListManager(multiprocessing.managers.BaseManager):

pass

syncarr = []

def get_arr():

return syncarr

def main():

# print dir([]) # cannot do `exposed = dir([])`!! manually:

MyListManager.register(“syncarr”, get_arr, exposed=[‘__getitem__’, ‘__setitem__’, ‘__str__’, ‘append’, ‘count’, ‘extend’, ‘index’, ‘insert’, ‘pop’, ‘remove’, ‘reverse’, ‘sort’])

manager = MyListManager(address=(‘/tmp/mypipe’), authkey=”)

manager.start()

# we don’t use the same name as `syncarr` here (although we could);

# just to see that `syncarr_tmp` is actually

# so we also have to expose `__str__` method in order to print its list values!

syncarr_tmp = manager.syncarr()

print(“syncarr (master):”, syncarr, “syncarr_tmp:”, syncarr_tmp)

print(“syncarr initial:”, syncarr_tmp.__str__())

syncarr_tmp.append(140)

syncarr_tmp.append(“hello”)

print(“syncarr set:”, str(syncarr_tmp))

raw_input(‘Now run b.py and press ENTER’)

print

print ‘Changing [0]’

syncarr_tmp.__setitem__(0, 250)

print ‘Changing [1]’

syncarr_tmp.__setitem__(1, “foo”)

new_i = raw_input(‘Enter a new int value for [0]: ‘)

syncarr_tmp.__setitem__(0, int(new_i))

raw_input(“Press any key (NOT Ctrl-C!) to kill server (but kill client first)”.center(50, “-“))

manager.shutdown()

if __name__ == ‘__main__’:

main()

b.py (client)

import time

import multiprocessing

import multiprocessing.managers

import logging

logger = multiprocessing.log_to_stderr()

logger.setLevel(logging.INFO)

class MyListManager(multiprocessing.managers.BaseManager):

pass

MyListManager.register(“syncarr”)

def main():

manager = MyListManager(address=(‘/tmp/mypipe’), authkey=”)

manager.connect()

syncarr = manager.syncarr()

print “arr = %s” % (dir(syncarr))

# note here we need not bother with __str__

# syncarr can be printed as a list without a problem:

print “List start:”, syncarr

print “Changing from client”

syncarr.append(30)

print “List now:”, syncarr

o0 = None

o1 = None

while 1:

new_0 = syncarr.__getitem__(0) # syncarr[0]

new_1 = syncarr.__getitem__(1) # syncarr[1]

if o0 != new_0 or o1 != new_1:

print ‘o0: %s => %s’ % (str(o0), str(new_0))

print ‘o1: %s => %s’ % (str(o1), str(new_1))

print “List is:”, syncarr

print ‘Press Ctrl-C to exit’

o0 = new_0

o1 = new_1

time.sleep(1)

if __name__ == ‘__main__’:

main()

As a final remark, on Linux /tmp/mypipe is created – but is 0 bytes, and has attributes srwxr-xr-x (for a socket); I guess this makes me happy, as I neither have to worry about network ports, nor about temporary files as such 🙂

Other related questions:

    Python: Possible to share in-memory data between 2 separate processes

    (very good explanation)

    Efficient Python to Python IPC
    Python: Sending a variable to another script

How do I share data between two python scripts?

you can use multiprocessing module to implement a Pipe between the two modules. Then you can start one of the modules as a Process and use the Pipe to communicate with it. The best part about using pipes is you can also pass python objects like dict,list through it.

There are multiple ways to make one Python file run another.. Use it like a module. import the file you want to run and run its functions. … . You can use the exec command. execfile(‘file.py’) … . You can spawn a new process using the os. system command..

How do I share global variables across modules python?

How do I share global variables across modules?. x = 0 # Default value of the ‘x’ configuration setting. mod.py:. import config config. x = 1. main.py:. import config import mod print(config. x) Note that using a module is also the basis for implementing the Singleton design pattern, for the same reason..

How do you call a variable from another file in python?

Example import variable from another file in Python. main.py. var = “Hello main file” Test.py import main print(main.var) Output: Hello main file. … . main.py var = “Hello main file” Test.py from main import var print(var) Output: … . main.py from main import * print(var) print(num) Test.py var = “Hello main file” num = 10. Output:.

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Python global file

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