ipyparallel | IPython Parallel : Interactive Parallel Computing in Python | Performance Testing library
kandi X-RAY | ipyparallel Summary
kandi X-RAY | ipyparallel Summary
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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Top functions reviewed by kandi - BETA
- 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
ipyparallel Key Features
ipyparallel Examples and Code Snippets
Community Discussions
Trending Discussions on ipyparallel
QUESTION
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)
...ANSWER
Answered 2021-Aug-04 at 05:11After 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
QUESTION
I am trying to run 2 jupyter notebook cells simultaneously.
First, I define
...ANSWER
Answered 2021-Mar-26 at 23:23You 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
QUESTION
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 10:44From the list of libraries, it looks like you've installed CPU only version of the Pytorch.
QUESTION
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 02:15The 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.
QUESTION
I am running into this error
...ANSWER
Answered 2021-Feb-25 at 16:31Solved- 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
QUESTION
Looking at an example of ipyparallel
HERE I see the following code example:
ANSWER
Answered 2020-Apr-05 at 08:32Indexing 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.
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