multiprocess | better multiprocessing and multithreading in python | Natural Language Processing library

 by   uqfoundation Python Version: 0.70.16 License: Non-SPDX

kandi X-RAY | multiprocess Summary

kandi X-RAY | multiprocess Summary

multiprocess is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. multiprocess has build file available and it has high support. However multiprocess has 11469 bugs, it has 24 vulnerabilities and it has a Non-SPDX License. You can install using 'pip install multiprocess' or download it from GitHub, PyPI.

`multiprocess` is a fork of `multiprocessing`, and is developed as part of `pathos`: `multiprocessing` is a package for the Python language which supports the spawning of processes using the API of the standard library’s `threading` module. `multiprocessing` has been distributed in the standard library since python 2.6. `multiprocess` is part of `pathos`, a python framework for heterogeneous computing. `multiprocess` is in active development, so any user feedback, bug reports, comments, or suggestions are highly appreciated. A list of issues is located at with a legacy list maintained at

            kandi-support Support

              multiprocess has a highly active ecosystem.
              It has 485 star(s) with 62 fork(s). There are 19 watchers for this library.
              There were 1 major release(s) in the last 6 months.
              There are 31 open issues and 106 have been closed. On average issues are closed in 484 days. There are 2 open pull requests and 0 closed requests.
              It has a negative sentiment in the developer community.
              The latest version of multiprocess is 0.70.16

            kandi-Quality Quality

              multiprocess has 11469 bugs (7 blocker, 15 critical, 11326 major, 121 minor) and 3637 code smells.

            kandi-Security Security

              multiprocess has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              multiprocess code analysis shows 24 unresolved vulnerabilities (0 blocker, 24 critical, 0 major, 0 minor).
              There are 186 security hotspots that need review.

            kandi-License License

              multiprocess has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              multiprocess releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              multiprocess saves you 125637 person hours of effort in developing the same functionality from scratch.
              It has 132442 lines of code, 9438 functions and 599 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed multiprocess and discovered the below as its top functions. This is intended to give you an instant insight into multiprocess implemented functionality, and help decide if they suit your requirements.
            • Test the calculation
            • Test the lock
            • Estimate the pipe through the connection
            • Test the condition speed
            • Bootstrap the child process
            • This method is called after a fork is finished
            • Run afterforkers
            • Feed data into pipe
            • Returns True if the session is currently running
            • Connect fds to new process
            • Free a block
            • Cleanup tests
            • Get the README as rst file
            • Decrement the value of an object
            • Create a proxy for a given token
            • Send a shutdown message to the manager
            • Write an info file
            • Receive data into a buffer
            • Run setup
            • Handle the results from the output queue
            • Terminate the worker pool
            • Process the tasks in the queue
            • Register a new proxy
            • A worker function that iterates through inqueue and returns the result
            • Launch a process object
            • Handle a request
            Get all kandi verified functions for this library.

            multiprocess Key Features

            No Key Features are available at this moment for multiprocess.

            multiprocess Examples and Code Snippets

            No Code Snippets are available at this moment for multiprocess.

            Community Discussions


            Find the number of elements in a DataFrame in the last 30 minutes
            Asked 2022-Mar-14 at 01:37

            I have a dataframe that contains for a specific timestamp, the number of items on a specific event.



            Answered 2022-Mar-14 at 01:37

            DataFrame.rolling is what you are looking for. The function only works if your dataframe's index is a Timestamp series:



            Colab: (0) UNIMPLEMENTED: DNN library is not found
            Asked 2022-Feb-08 at 19:27

            I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:



            Answered 2022-Feb-07 at 09:19

            It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason



            PyInstaller executable is not working if using multiprocessing in pyqt5
            Asked 2022-Jan-22 at 17:22

            I was working in my project related to pyqt5 GUI. I used multiprocessing to make it faster. When I run my program in editor, it works fine. But when I converted my program into executable using pyinstaller, then while running the program through this executable, it's not working. GUI opens, but close once it comes to the multiprocessing portion of the code (I get to know this by putting some print statement)

            I have also tried mutiprocessing.freeze_support(), still it's not worked.

            if I remove the multiprocessing, program through executable works fine, But I need to use multiprocessing to make it faster.

            Any suggestion?



            Answered 2022-Jan-21 at 14:12

            I had the same problem a while ago, and I recommend using Nuitka hence it supports Multiprocessing. If the problem lasts, try to use the threading library:



            Multiprocess inherently shared memory in no longer working on python 3.10 (coming from 3.6)
            Asked 2022-Jan-03 at 23:30

            I understand there are a variety of techniques for sharing memory and data structures between processes in python. This question is specifically about this inherently shared memory in python scripts that existed in python 3.6 but seems to no longer exist in 3.10. Does anyone know why and if it's possible to bring this back in 3.10? Or what this change that I'm observing is? I've upgraded my Mac to Monterey and it no longer supports python 3.6, so I'm forced to upgrade to either 3.9 or 3.10+.

            Note: I tend to develop on Mac and run production on Ubuntu. Not sure if that factors in here. Historically with 3.6, everything behaved the same regardless of OS.

            Make a simple project with the following python files




            Answered 2022-Jan-03 at 23:30

            In short, since 3.8, CPython uses the spawn start method on MacOs. Before it used the fork method.

            On UNIX platforms, the fork start method is used which means that every new multiprocessing process is an exact copy of the parent at the time of the fork.

            The spawn method means that it starts a new Python interpreter for each new multiprocessing process. According to the documentation:

            The child process will only inherit those resources necessary to run the process object’s run() method.

            It will import your program into this new interpreter, so starting processes et cetera sould only be done from within the if __name__ == '__main__':-block!

            This means you cannot count on variables from the parent process being available in the children, unless they are module level constants which would be imported.

            So the change is significant.

            What can be done?

            If the required information could be a module-level constant, that would solve the problem in the simplest way.

            If that is not possible (e.g. because the data needs to be generated at runtime) you could have the parent write the information to be shared to a file. E.g. in JSON format and before it starts other processes. Then the children could simply read this. That is probably the next simplest solution.

            Using a multiprocessing.Manager would allow you to share a dict between processes. There is however a certain amount of overhead associated with this.

            Or you could try calling multiprocessing.set_start_method("fork") before creating processes or pools and see if it doesn't crash in your case. That would revert to the pre-3.8 method on MacOs. But as documented in this bug, there are real problems with using the fork method on MacOs. Reading the issue indicates that fork might be OK as long as you don't use threads.



            Periodically restart Python multiprocessing pool
            Asked 2021-Nov-10 at 13:56

            I have a Python multiprocessing pool doing a very long job that even after a thorough debugging is not robust enough not to fail every 24 hours or so, because it depends on many third-party, non-Python tools with complex interactions. Also, the underlying machine has certain problems that I cannot control. Note that by failing I don't mean the whole program crashing, but some or most of the processes becoming idle because of some errors, and the app itself either hanging or continuing the job just with the processes that haven't failed.

            My solution right now is to periodically kill the job, manually, and then just restart from where it was.

            Even if it's not ideal, what I want to do now is the following: restart the multiprocessing pool periodically, programatically, from the Python code itself. I don't really care if this implies killing the pool workers in the middle of their job. Which would be the best way to do that?

            My code looks like:



            Answered 2021-Nov-10 at 13:56

            The problem with your current code is that it iterates the multiprocessed results directly, and that call will block. Fortunately there's an easy solution: use apply_async exactly as suggested in the docs. But because of how you describe the use-case here and the failure, I've adapted it somewhat. Firstly, a mock task:



            How to use pytest to simulate full reboot
            Asked 2021-Nov-08 at 16:04

            How do I test that my program is robust to unexpected shut-downs?

            My python code will run on a microcontroller that shuts off unexpectedly. I would like to test each part of the code rebooting unexpectedly and verify that it handles this correctly.

            Attempt: I tried putting code into its own process, then terminating it early, but this doesn't work because MyClass calls 7zip from the command line which continues even after process dies:



            Answered 2021-Nov-07 at 17:44

            Your logic starts a process wrapped within the MyClass object which itself spawns a new process via the os.system call.

            When you terminate the MyClass process, you kill the parent process but you leave the 7zip process running as orphan.

            Moreover, the process.terminate method sends a SIGTERM signal to the child process. The child process can intercept said signal and perform some cleanup routines before terminating. This is not ideal if you want to simulate a situation where there is no chance to clean up (a power loss). You most likely want to send a SIGKILL signal instead (on Linux).

            To kill the parent and child process, you need to address the entire process group.



            multithreading or multiprocessing for encrypting multiple files
            Asked 2021-Nov-08 at 12:09

            i have created a function enc()



            Answered 2021-Nov-07 at 12:03

            You need to rework your function.

            Python isn’t smart enough to know which part of the code you need multiprocessed.

            Most likely it’s the for loop right, you want to encrypt the files in parallel. So you can try something like this.

            Define the function which needs to be run for each loop, then, create the for loop outside. Then use multiprocessing like this.



            Deduplication/merging of mutable data in Python
            Asked 2021-Oct-21 at 00:04
            High-level view of the problem

            I have X sources that contain info about assets (hostname, IPs, MACs, os, etc.) in our environment. The sources contain anywhere from 1500 to 150k entries (at least the ones I use now). My script is supposed to query each of them, gather that data, deduplicate it by merging info about the same assets from different sources, and return unified list of all entries. My current implementation does work, but it's slow for bigger datasets. I'm curious if there is better way to accomplish what I'm trying to do.

            Universal problem
            Deduplication of data by merging similar entries with the caveat that merging two assets might change whether the resulting asset will be similar to the third asset that was similar to the first two before merging.
            ~ similarity, + merging
            (before) A ~ B ~ C
            (after) (A+B) ~ C or (A+B) !~ C

            I tried looking for people having the same issue, I only found What is an elegant way to remove duplicate mutable objects in a list in Python?, but it didn't include merging of data which is crucial in my case.

            The classes used

            Simplified for ease of reading and understanding with unneeded parts removed - general functionality is intact.



            Answered 2021-Oct-21 at 00:04

            Summary: we define two sketch functions f and g from entries to sets of “sketches” such that two entries e and e′ are similar if and only if f(e) ∩ g(e′) ≠ ∅. Then we can identify merges efficiently (see the algorithm at the end).

            I’m actually going to define four sketch functions, fos, faddr, gos, and gaddr, from which we construct

            • f(e) = {(x, y) | x ∈ fos(e), y ∈ faddr(e)}
            • g(e) = {(x, y) | x ∈ gos(e), y ∈ gaddr(e)}.

            fos and gos are the simpler of the four. fos(e) includes

            • (1, e.os), if e.os is known
            • (2,), if e.os is known
            • (3,), if e.os is unknown.

            gos(e) includes

            • (1, e.os), if e.os is known
            • (2,), if e.os is unknown
            • (3,).

            faddr and gaddr are more complicated because there are prioritized attributes, and they can have multiple values. Nevertheless, the same trick can be made to work. faddr(e) includes

            • (1, h) for each h in e.hostname
            • (2, m) for each m in e.mac, if e.hostname is nonempty
            • (3, m) for each m in e.mac, if e.hostname is empty
            • (4, i) for each i in e.ip, if e.hostname and e.mac are nonempty
            • (5, i) for each i in e.ip, if e.hostname is empty and e.mac is nonempty
            • (6, i) for each i in e.ip, if e.hostname is nonempty and e.mac is empty
            • (7, i) for each i in e.ip, if e.hostname and e.mac are empty.

            gaddr(e) includes

            • (1, h) for each h in e.hostname
            • (2, m) for each m in e.mac, if e.hostname is empty
            • (3, m) for each m in e.mac
            • (4, i) for each i in e.ip, if e.hostname is empty and e.mac is empty
            • (5, i) for each i in e.ip, if e.mac is empty
            • (6, i) for each i in e.ip, if e.hostname is empty
            • (7, i) for each i in e.ip.

            The rest of the algorithm is as follows.

            • Initialize a defaultdict(list) mapping a sketch to a list of entry identifiers.

            • For each entry, for each of the entry’s f-sketches, add the entry’s identifier to the appropriate list in the defaultdict.

            • Initialize a set of edges.

            • For each entry, for each of the entry’s g-sketches, look up the g-sketch in the defaultdict and add an edge from the entry’s identifiers to each of the other identifiers in the list.

            Now that we have a set of edges, we run into the problem that @btilly noted. My first instinct as a computer scientist is to find connected components, but of course, merging two entries may cause some incident edges to disappear. Instead you can use the edges as candidates for merging, and repeat until the algorithm above returns no edges.



            Why can't add file handler with the form of self.fh in the init method?
            Asked 2021-Oct-20 at 03:28

            os and python info:



            Answered 2021-Oct-12 at 01:57

            I did some investigation, but it does not fully answer the question. I am going to post the results here in case if they help somebody else.

            First, if the subprocess fails, there is no traceback. So I added the additional line to display the output of subprocesses. It should be None if no errors occur. The new code:



            Why to call multiprocessing module with Process can create same instance?
            Asked 2021-Oct-18 at 11:07

            My platform info:



            Answered 2021-Oct-18 at 11:07

            As already explained in this answer, id implementation is platform specific and is not a good method to guarantee unique identifiers across multiple processes.

            In CPython specifically, id returns the pointer of the object within its own process address space. Most of modern OSes abstract the computer memory using a methodology known as Virtual Memory.

            What you are observing are actual different objects. Nevertheless, they appear to have the same identifiers as each process allocated that object in the same offset of its own memory address space.

            The reason why this does not happen in the pool is most likely due to the fact the Pool is allocating several resources in the worker process (pipes, counters, etc..) before running the task function. Hence, it randomizes the process address space utilization enough such that the object IDs appear different across their sibling processes.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install multiprocess

            You can install using 'pip install multiprocess' or download it from GitHub, PyPI.
            You can use multiprocess like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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