ipyparallel | IPython Parallel : Interactive Parallel Computing in Python | Performance Testing library

by ipython Jupyter Notebook Version: 8.6.1 License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(6)Vulnerabilities Install Support

kandi X-RAY | ipyparallel Summary

ipyparallel is a Jupyter Notebook library typically used in Testing, Performance Testing, Jupyter applications. ipyparallel has no vulnerabilities and it has medium support. However ipyparallel has 4 bugs and it has a Non-SPDX License. You can download it from GitHub.

IPython Parallel (ipyparallel) is a Python package and collection of CLI scripts for controlling clusters of IPython processes, built on the Jupyter protocol.

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Security

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Support

ipyparallel has a medium active ecosystem.

It has 2409 star(s) with 977 fork(s). There are 121 watchers for this library.

It had no major release in the last 6 months.

There are 51 open issues and 286 have been closed. On average issues are closed in 246 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of ipyparallel is 8.6.1

Quality

ipyparallel has 4 bugs (0 blocker, 0 critical, 4 major, 0 minor) and 494 code smells.

Security

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

ipyparallel code analysis shows 0 unresolved vulnerabilities.

There are 22 security hotspots that need review.

License

ipyparallel 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.

Reuse

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

Installation instructions, examples and code snippets are available.

It has 25014 lines of code, 1847 functions and 168 files.

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

Top functions reviewed by kandi - BETA

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

Map f onto a sequence of sequences
Display a single result
Display the output of the engine
Display the last result
Solve the model
Update internal boundary bounding box
Returns an iterable of integers
Dispatch IOPub messages
Unwrap an exception
Sort a list of lists
This function is called when the IO stream is received
Establish a connection to the hub
Start n engines
Creates a Dask worker on the Hub
Map a function over sequences
Get results from client
Save a task result message
Handle an execute reply message
Register a new engine
Dispatch a task to the monitor
Internal function
Update internal boundary buffer
Launch the broadcast scheduler
Runs on the given template
Update internal bounding box based on solution_array
Resubmit a task

Get all kandi verified functions for this library.

ipyparallel Key Features

No Key Features are available at this moment for ipyparallel.

ipyparallel Examples and Code Snippets

No Code Snippets are available at this moment for ipyparallel.

Community Discussions

Trending Discussions on ipyparallel

mamba fails to create env

Simultaneously running jupyter notebook cells?

RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. GPU not detected by pytorch

Python modules "Processing", "Multiprocessing" and other concurrency modules: what are the differences?

Ipywidgets (Vbox) not showing up on Jupyter notebook

What exactly does '[:]' mean in the example of ipyparallel?

QUESTION

mamba fails to create env

Asked 2021-Aug-04 at 05:11

I had just installed Anaconda from anaconda.com. The installation proceeded smoothly. After that, I was trying to create a new environment from this environment.yml file. (nbdev.yml)

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ANSWER

Answered 2021-Aug-04 at 05:11

After a lot of research, I stumbled on to Mamba doesn't find a solution when mixing conda forge defaults and not specifying Python explicitly 1102. So I just edited nbdev.yml from

Source https://stackoverflow.com/questions/68645697

QUESTION

Simultaneously running jupyter notebook cells?

Asked 2021-Mar-26 at 23:23

I am trying to run 2 jupyter notebook cells simultaneously.

First, I define

...

ANSWER

Answered 2021-Mar-26 at 23:23

You can use multiprocess module in order to run parallel cells

You probably need to use multiprocess Manager's list and Value in order to get shared variables between threads

Source https://stackoverflow.com/questions/66824853

QUESTION

RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. GPU not detected by pytorch

Asked 2021-Mar-21 at 16:24

Having trouble with CUDA + Pytorch this is the error. I reinstalled CUDA and cudnn multiple times.

Conda env is detecting GPU but its giving errors with pytorch and certain cuda libraries. I tried with Cuda 10.1 and 10.0, and cudnn version 8 and 7.6.5, Added cuda to path and everything.

However anaconda is showing cuda tool kit 9.0 is installed, whilst I clearly installed 10.0, so I am not entirely sure what's the deal with that.

...

ANSWER

Answered 2021-Mar-20 at 10:44

From the list of libraries, it looks like you've installed CPU only version of the Pytorch.

Source https://stackoverflow.com/questions/66711799

QUESTION

Python modules "Processing", "Multiprocessing" and other concurrency modules: what are the differences?

Asked 2021-Feb-27 at 02:15

I am starting to read up over possible ways to parallelise Python code.

DISCLAIMER. This is NOT a question about Multiprocessing vs Multithreading.

At this link https://ipyparallel.readthedocs.io/en/latest/demos.html one finds references to several concurrency packages for Python to avoid the GIL: https://scipy.github.io/old-wiki/pages/ParallelProgramming

-IPython1

-mpi4py

-parallel python

-Numba

There is also a multiprocessing package: https://docs.python.org/3/library/multiprocessing.html

And another one called processing: https://pypi.org/project/processing/

First of all, it is not at all clear to me the difference between the latter two above; what is the difference in using between the multiprocessing module and the processing module?.

In general, I fail to understand the differences between those all -- which must be there, given some developers made the effort to create a mpi4py version for the MPI used in C++. I guess this is not just about the dualism between "threading" and "multiprocessing" approaches, where in one case the memory is shared while the other has each process with its own memory and interpreter, something more must be different between all of those different packages out there.

Thanks to all of those who will dedicate time to answer this!

...

ANSWER

Answered 2021-Feb-27 at 02:15

The difference is that the last version of processing was released in April of 2008 and multiprocessing was added in Python 2.6 in October 2008.

processing was a library that was used before multiprocessing was distributed with Python.

As far as the specific difference between other modules designed for multiprocessing: The scipy page you linked says that "This is a subject for graduate courses in computer science, and I'm not going to address it here....there are some python tools you can use to implement the things you learn in that graduate course." While they admit that may be a bit of an exaggeration, independent study of multiprocessing in general will be required to discern the difference between these libraries, you should probably just stick to the built in multiprocessing module for your initial experiments while you learn how it works. One you're more comfortable with multiprocessing, you might want to check out the pathos framework.

But here are the basics for the packages you mention:

Numba adds decorators that automatically compile functions to make them run faster, it isn't really a multiprocessing tool as much as a JIT compiling tool.

Parallel Python overcomes the GIL to utilize multiple cores or multiple computers, it's designed to be easy to use and to handle all the complex stuff behind the scenes.

MPI for Python is like Paralell Python with less emphasis on simplicity.

IPython is a toolkit with many features, including a shell and Jupyter kernel, it's also not really a multiprocessing tool.

Keep in mind that plenty of libraries/modules do the same thing, there doesn't need to be a reason more than one exists. Use whatever works for you.

Source https://stackoverflow.com/questions/66358816

QUESTION

Ipywidgets (Vbox) not showing up on Jupyter notebook

Asked 2021-Feb-25 at 16:31

I am running into this error

...

ANSWER

Answered 2021-Feb-25 at 16:31

Solved- plotlywidgets used to render FigureWidget was not installed correctly.

I used jupyter nbextension install --py plotlywidget --user and then jupyter nbextension enable plotlywidget --user --py

Source https://stackoverflow.com/questions/66371006

QUESTION

What exactly does '[:]' mean in the example of ipyparallel?

Asked 2020-Apr-05 at 08:32

Looking at an example of ipyparallel HERE I see the following code example:

...

ANSWER

Answered 2020-Apr-05 at 08:32

Indexing or slicing an ipyparallel.Client instance creates an ipyparallel.DirectView over the execution engines selected by the index or slice. [:] selects all engines. The resulting DirectView can be used to execute code on the selected engines.

This is covered in one of the later sections of the tutorial.

Source https://stackoverflow.com/questions/61039850

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

Vulnerabilities

No vulnerabilities reported

Install ipyparallel

This will install and enable the IPython Parallel extensions for Jupyter Notebook and (as of 7.0) Jupyter Lab 3.0.

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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