multiprocessing_on_dill | friendly fork of the Python Standard Library

by sixty-north Python Version: 3.5.0a4 License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | multiprocessing_on_dill Summary

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

A friendly fork of the Python Standard Library multiprocessing package which uses dill instead of pickle

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Quality

Security

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Support

multiprocessing_on_dill has a low active ecosystem.

It has 21 star(s) with 6 fork(s). There are 2 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 1 have been closed. On average issues are closed in 1 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_on_dill is 3.5.0a4

Quality

multiprocessing_on_dill has 0 bugs and 0 code smells.

Security

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

multiprocessing_on_dill code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

multiprocessing_on_dill does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

multiprocessing_on_dill saves you 2260 person hours of effort in developing the same functionality from scratch.

It has 4940 lines of code, 566 functions and 24 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed multiprocessing_on_dill and discovered the below as its top functions. This is intended to give you an instant insight into multiprocessing_on_dill implemented functionality, and help decide if they suit your requirements.

Feed data to pipe
Dispatch a method
Connect to the manager
Call a method on the server
Register a new proxy type
Make a copy of obj
Create a token
Launch a process
Close stdin
Terminate the worker pool
Checks if the child process is alive
Decorator for decrefs
Stop the resourceSharer thread
Start the manager
Close sys stdin
Allocates a block of memory
Create a proxy for the given serializer
Receive bytes into buffer
Sets up the main module for the given path
Handle the results from the output queue
Perform a worker thread
Process tasks from the queue
Handle a single request
Send shutdown message to manager
Map a function over an iterable
Free a block
Decrement the value of an object

Get all kandi verified functions for this library.

multiprocessing_on_dill Key Features

No Key Features are available at this moment for multiprocessing_on_dill.

multiprocessing_on_dill Examples and Code Snippets

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Community Discussions

Trending Discussions on multiprocessing_on_dill

Hyperparameter Optimization in sklearn and n_jobs > 1: Pickling

Python multiprocessing with dill.load

Python multiprocessing pool function not defined

QUESTION

Hyperparameter Optimization in sklearn and n_jobs > 1: Pickling

Asked 2018-Nov-09 at 16:31

I'm in a "pickle". Here's the structure of my code:

A base class that acts like an abstract class
A subclass that can be instantiated
- A method that sets up the parameters and calls RandomizedSearchCV or GridSearchCV with n_jobs=-1.
  - A local function, create_model, that creates the neural network model (see this tutorial) to be called by KerasClassifier or KerasRegressor

I get an error saying local object can't be pickled. If I change n_jobs=1, then no problems. So I suspect the issue is with the local function and parallel processing. Is there a fix to this? After googling a bit, it seems that the serializer dill could work here (I even found a package called multiprocessing_on_dill). But I'm currently relying on sklearn's packages.

...

ANSWER

Answered 2018-Mar-03 at 17:12

I found a "solution" to my problem. I was really puzzled why the examples here work with n_jobs=-1, but my code doesn't. It seems the issue is with the local function create_model that resides in a method of the subclass. If I make the local function a method of the subclass, I'm able to set n_jobs > 1.

So to recap, here's the structure of my code:

A base class that acts like an abstract class
A subclass that can be instantiated
- A method that sets up the parameters and calls RandomizedSearchCV or GridSearchCV with n_jobs=-1.
- A method, create_model, that creates the neural network model to be called by KerasClassifier or KerasRegressor

General idea of the code:

Source https://stackoverflow.com/questions/42843465

QUESTION

Python multiprocessing with dill.load

Asked 2018-Oct-09 at 14:46

I'm having great difficulties in getting functions executed in MultiProcessing Pool method that are loaded through

...

ANSWER

Answered 2018-Oct-09 at 14:46

SOLUTION: when dumping use this setting

Source https://stackoverflow.com/questions/52721543

QUESTION

Python multiprocessing pool function not defined

Asked 2017-May-10 at 14:32

I need to implement a multiprocessing pool that utilizes arbitrary packages for calculations. For this, I'm using Python and joblib 0.9.0. This code is basically the structure I want.

...

ANSWER

Answered 2017-May-10 at 14:32

I am not sure what the exact issue is, but it appears that there is some problem with transferring the global scope over to the subprocesses that run the task. You can potentially avoid name errors by binding the name np as a function parameter:

Source https://stackoverflow.com/questions/43862524

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

Vulnerabilities

No vulnerabilities reported

Install multiprocessing_on_dill

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