pathos | parallel graph management and execution | Architecture library
kandi X-RAY | pathos Summary
kandi X-RAY | pathos Summary
`pathos` is a framework for heterogeneous computing. It provides a consistent high-level interface for configuring and launching parallel computations across heterogeneous resources. `pathos` provides configurable launchers for parallel and distributed computing, where each launcher contains the syntactic logic to configure and launch jobs in an execution environment. Examples of launchers that plug into `pathos` are: a queue-less MPI-based launcher (in `pyina`), a ssh-based launcher (in `pathos`), and a multi-process launcher (in `multiprocess`). `pathos` provides a consistent interface for parallel and/or distributed versions of `map` and `apply` for each launcher, thus lowering the barrier for users to extend their code to parallel and/or distributed resources. The guiding design principle behind `pathos` is that `map` and `apply` should be drop-in replacements in otherwise serial code, and thus switching to one or more of the `pathos` launchers is all that is needed to enable code to leverage the selected parallel or distributed computing resource. This not only greatly reduces the time to convert a code to parallel, but it also enables a single code-base to be maintained instead of requiring parallel, serial, and distributed versions of a code. `pathos` maps can be nested, thus hierarchical heterogeneous computing is possible by merely selecting the desired hierarchy of `map` and `pipe` (`apply`) objects.
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Top functions reviewed by kandi - BETA
- Map a function over a sequence of processes
- Serve the pool
- Clear the pool
- Map a function f
- Map a function over the worker pool
- Get the pid of a process
- Return the response
- Register a handler for reading
- Registers a handler function to be called when idle
- Map a function f over the workers
- Get all children of a process
- Optimizes the solver using the cost function
- Return the response as a string
- Get the PID of a process
- Connect to given host and port
- Handle a POST request
- Spawn a fork on the child process
- Wrapper function for _map_map_map
- Test if a function is ready
- Write info file
- Serve a bash script
- Copy source to destination
- Return the README as rst file
- Parse PCP Example
- Spawn a new child process
- Perform a multiprocessing operation
- Return a Chebyshev cost function for a given target
- Returns the stats for the pool
pathos Key Features
pathos Examples and Code Snippets
Lines of Code : 14License : Strong Copyleft (CC BY-SA 4.0)
Copydef function_maker(start, end):
def function(x):
return x[:, start:end]
return function
class Slicer:
def __init__(self, start, end):
self.start = start
self.end = end
def __
import multiprocessing
...
def dothejob():
...
def start():
# code to setup and start multiprocessing workers:
# like:
worker1 = multiprocessing.Process(target=dothejob)
...
worker1.start()
...
worker1.join()
if from pathos.helpers import mp as multiprocess
a = multiprocess.Array('i', 2) # Declares an integer array of size 2
import os
import math
from multiprocessing import Manager
from pathos.multiprocessing import ProcessingPool
class MyComplex:
def __init__(self, x):
self._z = x * x
def me(self):
return math.sqrt(self._z)
classAttributeError: Can't pickle local object 'MyClass.mymethod..mymethod'
import pathos
import os
class SomeClass:
def __init__(self):
self.words = ["a", "b", "c"]
def some_method(self):
sudo apt-get install libboost-locale-dev
sudo apt-get install libboost-all-dev
>>> import pathos as pa
>>> import multiprocess as mp
>>> mp.Manager is pa.helpers.mp.Manager
True
>>> import pathos as pa
>>> pa.helpers.cpu_count()
8
from tqdm import tqdm
import multiprocessing
import threading
# will hold (Processor, example set) for process_all_examples_multi
_process_this = None
_process_this_lock = threading.Lock()
class Processor:
def __init__(self, arg1, ar**file 1: my_methods.py**
def f(x):
return x.count()
**file 2: main.py or your jupyter notebook, in the same directory here**
import multiprocessing as mp
from my_methods import f
def parallelize(df, func, n_cores=4):
Community Discussions
Trending Discussions on pathos
QUESTION
I have a function with a list of objects, two list of int and an int (an ID) as parameters, which returns a tuple of two list of int. this function works very well but when my list of ID grows, it takes a lot of time. Having already used multiprocessing in other projects, it seemed to me that the situation was appropriate for the use of multiprocessing Pool.
However, I get an error _pickle.PicklingError when launching it.
I have spent the past days looking for alternatives ways of doing this : I discovered pathos ProcessPool that runs forever with no indication of the problem. I have tried ThreadingPool as an accepted answer sugested, but it is obviously not adapted to my issue since it does not use multiple CPUs and doesnt speed up the process.
Here is a sample of my function, it is not a reproductible example since it is specific to my case. But I believe the function is pretty clear : It returns a tuple of two lists, created in a for loop.
...ANSWER
Answered 2022-Mar-24 at 11:43If anyone stumble upon this question, the reason this error happened even with a very simplist function is because of the way I was running the python script. As it is well explained in the comments by ShadowRanger, the function needs to be defined at the top level. Within PyCharm, "Run File in Python Console" does not simply run it, but puts a wrapper around.
By running the file the proper way, or calling python myscript.py, theres no raised error.
QUESTION
When I try to execute this code:
...ANSWER
Answered 2021-Dec-16 at 16:00I'm the pathos author. First off, you are using a ParallelPool, which uses ppft... which uses dill.source to convert objects to source code, and then passes the source code to the new process to then build a new object and execute. You may want to try a ProcessPool, which uses multiprocess, which uses dill, which uses a more standard serialization of objects (like pickle). Also, when you are serializing code (either with dill or dill.source) you should take care to make sure the code is as self-encapsulated as possible. What I mean is that:
QUESTION
I want use a parallel downloading videos from youtube, but my code ending with exception "PicklingError". Can you help guys with code, how it should be, please.
Another fixed variant:
...ANSWER
Answered 2021-Nov-21 at 15:39You've got the wrong side of the stick. Take a look at multiprocessing module documents. As it says, calling Pool method is for running multiple instance of same function simultaneously (in parallel). So call Pool method as many numbers you want, meanwhile your method does not any parameters, call it without any arguments:
QUESTION
I'm pretty new to Python, this question probably shows that. I'm working on multiprocessing part of my script, couldn't find a definitive answer to my problem.
I'm struggling with one thing. When using multiprocessing, part of the code has to be guarded with if __name__ == "__main__". I get that, my pool is working great. But I would love to import that whole script (making it a one big function that returns an argument would be the best). And here is the problem. First, how can I import something if part of it will only run when launched from the main/source file because of that guard? Secondly, if I manage to work it out and the whole script will be in one big function, pickle can't handle that, will use of "multiprocessing on dill" or "pathos" fix it?
Thanks!
...ANSWER
Answered 2021-Jun-08 at 22:10You are probably confused with the concept. The if __name__ == "__main__" guard in Python exists exactly in order for it to be possible for all Python files to be importable.
Without the guard, a file, once imported, would have the same behavior as if it were the "root" program - and it would require a lot of boyler plate and inter-process comunication (like writting a "PID" file at a fixed filesystem location) to coordinate imports of the same code, including for multiprocessing.
Just leave under the guard whatever code needs to run for the root process. Everything else you move into functions that you can call from the importing code.
If you'd run "all" the script, even the part setting up the multiprocessing workers would run, and any simple job would create more workers exponentially until all machine resources were taken (i.e.: it would crash hard and fast, potentially taking the machine to an unresponsive state).
So, this is a good pattern - th "dothejob" function can call all other functions you need, so you just need to import and call it, either from a master process, or from any other project importing your file as a Python module.
QUESTION
I'm trying to figure out multiprocessing and I've run into something I entirely don't understand.
I'm using pathos.multiprocessing for better pickling. The following code creates a list of objects which I want to iterate through. However, when I run it, it prints several different lists despite referring to the same variable?
...ANSWER
Answered 2021-May-23 at 18:42When using multiprocessing, the library spawns multiple different processes. Each process has its own address space. This means that each of those processes has its own copy of the variable, and any change in one process will not reflect in other processes.
In order to use shared memory, you need special constructs to define your global variables. For pathos.multiprocessing, from this comment, it seems you can declare multiprocessing type shared variables by simply importing the following:
QUESTION
I am wondering how I could write streaming data to different MySQL tables in a parallel way?
I have the following code: where the GetStreaming() returns a list of tuple [(tbName,data1,data2),(tbName,data1,data2),...] available at the time of call.
ANSWER
Answered 2021-May-05 at 16:04Each "parallel" insertion process needs its own connector and cursor. You can't share them across any sort of thread.
You can use connection pooling to make faster the allocation and release of connections.
There's no magic in MySQL (or any DBMS costing less than the GDP of a small country) that lets it scale up to handle large scale data insertion on ~100 connections simultaneously. Paradoxically, more connections can have lower throughput than fewer connections, because of contention between them. You may want to rethink your system architecture so you can make it work OK with a few connections.
In other words: fewer bigger tables perform much better than many small tables.
Finally, read about ways of speeding up bulk inserts. For example this sort of multirow insert
QUESTION
Trying to use multiple TensorFlow models in parallel using pathos.multiprocessing.Pool
Error is:
...ANSWER
Answered 2021-Mar-29 at 12:13I'm the author of pathos. Whenever you see self._value in the error, what's generally happening is that something you tried to send to another processor failed to serialize. The error and traceback is a bit obtuse, admittedly. However, what you can do is check the serialization with dill, and determine if you need to use one of the serialization variants (like dill.settings['trace'] = True), or whether you need to restructure your code slightly to better accommodate serialization. If the class you are working with is something you can edit, then an easy thing to do is to add a __reduce__ method, or similar, to aid serialization.
QUESTION
I am trying to use pathos for triggering multiprocessing within a function. I notice, however, an odd behaviour and don't know why:
...ANSWER
Answered 2021-Mar-25 at 23:09Instead of
from pathos.multiprocessing import ProcessPool as Pool, I used
from multiprocess import Pool, which is essentially the same thing. Then I tried some alternative approaches.
So:
QUESTION
I am attempting to use multiprocessing for the generation of complex, unpickable, objects as per the following code snippet:
...ANSWER
Answered 2021-Feb-28 at 12:46So I have resolved this issue. I would still be great if someone like mmckerns or someone else with more knowledge than me on multiprocessing could comment on why this is a solution.
The issue seemed to have been that the Manager().list() was declared in __init__. The following code works without any issues:
QUESTION
ANSWER
Answered 2021-Jan-11 at 20:49Boost is not installed. You can try this
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install pathos
You can use pathos 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.
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