mp_shared_memory | A shared-memory wrapper backwards compatible with Python

by chunglabmit Python Version: Current License: MIT

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kandi X-RAY | mp_shared_memory Summary

mp_shared_memory is a Python library. mp_shared_memory has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

A shared-memory wrapper backwards compatible with Python < 3.8. This shared memory wrapper makes it efficient to share memory between processes using the pickling mechanisms that underly the multiprocess pool functional serialization / deserialization protocol. Python 3.8 has excellent multiprocess shared memory support, but this package will work on Linux for Python < 3.8 and its lifecycle model is a little more automatic to use than multiprocess.shared_memory.

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Quality

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Support

mp_shared_memory has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

mp_shared_memory has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of mp_shared_memory is current.

Quality

mp_shared_memory has no bugs reported.

Security

mp_shared_memory has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

mp_shared_memory is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

mp_shared_memory releases are not available. You will need to build from source code and install.

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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mp_shared_memory Key Features

No Key Features are available at this moment for mp_shared_memory.

mp_shared_memory Examples and Code Snippets

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Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install mp_shared_memory

You can download it from GitHub.
You can use mp_shared_memory 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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