multiprocessing_generator | Prefetch elements from a Python generator

 by   wetneb Python Version: 0.3 License: MIT

kandi X-RAY | multiprocessing_generator Summary

kandi X-RAY | multiprocessing_generator Summary

multiprocessing_generator is a Python library. multiprocessing_generator has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install multiprocessing_generator' or download it from GitHub, PyPI.

A library to prefetch items from a Python generator in the background, using a separate process. Up to 100 elements ahead of what is consumed will be fetched by the generator in the background, which is useful when the producer and the consumer do not use the same resources (for instance network vs. CPU). The generator handles exceptions and more serious failures transparently. Distributed under the MIT license.
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            kandi-support Support

              multiprocessing_generator has a low active ecosystem.
              It has 24 star(s) with 10 fork(s). There are 1 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 2 open issues and 2 have been closed. On average issues are closed in 7 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of multiprocessing_generator is 0.3

            kandi-Quality Quality

              multiprocessing_generator has 0 bugs and 0 code smells.

            kandi-Security Security

              multiprocessing_generator has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              multiprocessing_generator code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              multiprocessing_generator is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              multiprocessing_generator 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.

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            multiprocessing_generator Key Features

            No Key Features are available at this moment for multiprocessing_generator.

            multiprocessing_generator Examples and Code Snippets

            No Code Snippets are available at this moment for multiprocessing_generator.

            Community Discussions

            Trending Discussions on multiprocessing_generator

            QUESTION

            multiprocessing_generator modules triggers a permission error
            Asked 2019-Jan-02 at 14:33

            I'm found the module multiprocessing_generator. I tried that module with the code below :

            ...

            ANSWER

            Answered 2019-Jan-02 at 14:33

            multiprocessing_generator relies on the multiprocessing module using the 'fork' start method, because it assumes that the wrapped nested function (defined in ParallelGenerator's __init__) can be passed as the target of a multiprocessing.Process object. On a forking based platform, this is fine; the child process inherits the complete state of the parent (with a few small exceptions like threads), so it has equal access to the wrapped nested function (it inherited an exact copy after all).

            Problem is, on Windows, the only available start method is 'spawn', which requires the target (and all arguments) to be pickleable (it pickles them, sends them to the child over IPC, and reconstructs them there), and nested functions are never pickleable (pickleing a function involves pickling its qualified name to be imported and used on the other side, and the qualified name of a nested function involves unimportable components, in this case, pickleing ParallelGenerator.__init__..wrapped fails because is clearly not an importable name).

            Basically, multiprocessing_generator only works on UNIX-like systems, and only if you're using the default start method ('fork'); if you called set_start_method with some other value ('forkserver' or 'spawn'), multiprocessing_generator can't be made to work.

            While this is a bug, it's not a particularly critical bug in most cases; there is very little benefit to the module if the generator must have its values pickled, because most such generators either aren't pickleable themselves (e.g. most file-like objects) or pickleing them involves running them to completion (in which case you've lost all your parallelism).

            Sorry, but the simple answer here is: Don't use multiprocessing_generator on Windows.

            That said, if your generator is I/O-bound, you might be able to benefit from the module by importing it, then immediately monkey-patching it to replace all multiprocessing components it relies on with their equivalent multiprocessing.dummy names (which are backed by threads, and don't rely on pickling), e.g.

            Source https://stackoverflow.com/questions/53986154

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

            Vulnerabilities

            No vulnerabilities reported

            Install multiprocessing_generator

            You can install using 'pip install multiprocessing_generator' or download it from GitHub, PyPI.
            You can use multiprocessing_generator 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.

            Support

            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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