how to use parallel processing filter in python?

One of examples taken from sagemath is:

def pool_filter(pool, func, candidates):
   return [c
      for c, keep in zip(candidates, pool.map(func, candidates)) if keep
   ]

Suggestion : 2

I know that in Python, map is equivalent to anycodings_python parallel processing class of pool.map from anycodings_python multiprocessing module. Is there a filter anycodings_python equivalent in parallel programming for anycodings_python Python?,But it further depends on your anycodings_parallel-processing implementation.,One of examples taken from sagemath is: anycodings_parallel-processing ,Python has most of function orientated anycodings_parallel-processing programming paradigms that are anycodings_parallel-processing necessary. Such as map, filter and anycodings_parallel-processing reduce which you can find here

One of examples taken from sagemath is: anycodings_parallel-processing

def pool_filter(pool, func, candidates):
   return [c
      for c, keep in zip(candidates, pool.map(func, candidates)) if keep
   ]

Suggestion : 3

Last Updated : 27 Dec, 2019

Ipcluster shell commands are used to start the controller and engines.

$ ipcluster start

Direct Interface allows you to send commands explicitly to each of the computing units. This is flexible and easy to use. To interact with units, you need to start the engine and then an IPython session in a separate shell. You can establish a connection to the controller by creating a client. In the below code, we import the Client class and create an instance:

from IPython.parallel
import Client
rc = Client()
rc.ids

11._

dview = rc[0]

As a final step, you can execute commands by using the DirectView.execute method.

dview.execute(‘a = 1’)

The above command will be executed individually by each engine. Using the get method you can get the result in the form of an AsyncResult object.

dview.pull(‘a‘).get()
dview.push({
   ‘
   a’: 2
})

dview = rc[0]

dview = rc.direct_view(‘all’).

Suggestion : 4

October 31, 2018

The maximum number of processes you can run at a time is limited by the number of processors in your computer. If you don’t know how many processors are present in the machine, the cpu_count() function in multiprocessing will show it.

import multiprocessing as mp
print("Number of processors: ", mp.cpu_count())

The first problem is: Given a 2D matrix (or list of lists), count how many numbers are present between a given range in each row. We will work on the list prepared below.

import numpy as np
from time
import time

# Prepare data
np.random.RandomState(100)
arr = np.random.randint(0, 10, size = [200000, 5])
data = arr.tolist()
data[: 5]

3._

# Solution Without Paralleization

def howmany_within_range(row, minimum, maximum):
   ""
"Returns how many numbers lie within `maximum` and `minimum` in a given `row`"
""
count = 0
for n in row:
   if minimum <= n <= maximum:
   count = count + 1
return count

results = []
for row in data:
   results.append(howmany_within_range(row, minimum = 4, maximum = 8))

print(results[: 10])
# > [3, 1, 4, 4, 4, 2, 1, 1, 3, 3]

I know this is not a nice usecase of map(), but it clearly shows how it differs from apply().

# Parallelizing using Pool.map()
import multiprocessing as mp

# Redefine, with only 1 mandatory argument.
def howmany_within_range_rowonly(row, minimum = 4, maximum = 8):
   count = 0
for n in row:
   if minimum <= n <= maximum:
   count = count + 1
return count

pool = mp.Pool(mp.cpu_count())

results = pool.map(howmany_within_range_rowonly, [row
   for row in data
])

pool.close()

print(results[: 10])
# > [3, 1, 4, 4, 4, 2, 1, 1, 3, 3]

So effectively, Pool.starmap() is like a version of Pool.map() that accepts arguments.

# Parallelizing with Pool.starmap()
import multiprocessing as mp

pool = mp.Pool(mp.cpu_count())

results = pool.starmap(howmany_within_range, [(row, 4, 8) for row in data])

pool.close()

print(results[: 10])
# > [3, 1, 4, 4, 4, 2, 1, 1, 3, 3]

import multiprocessing as mp

list_a = [
   [1, 2, 3],
   [5, 6, 7, 8],
   [10, 11, 12],
   [20, 21]
]
list_b = [
   [2, 3, 4, 5],
   [6, 9, 10],
   [11, 12, 13, 14],
   [21, 24, 25]
]

def get_commons(list_1, list_2):
   return list(set(list_1).intersection(list_2))

pool = mp.Pool(mp.cpu_count())
results = [pool.apply(get_commons, args = (l1, l2)) for l1, l2 in zip(list_a, list_b)]
pool.close()
print(results[: 10])

import os
import multiprocessing as mp

processes = ('script1.py', 'script2.py', 'script3.py')

def run_python(process):
   os.system('python {}'.format(process))

pool = mp.Pool(processes = 3)
pool.map(run_python, processes)

import multiprocessing as mp

list_a = [
   [2, 3, 4, 5],
   [6, 9, 10, 12],
   [11, 12, 13, 14],
   [21, 24, 25, 26]
]

def normalize(mylist):
   mini = min(mylist)
maxi = max(mylist)
return [(i - mini) / (maxi - mini) for i in mylist]

pool = mp.Pool(mp.cpu_count())
results = [pool.apply(normalize, args = (l1, )) for l1 in list_a]
pool.close()
print(results[: 10])

Suggestion : 5

In order to recognize the advantages of parallelization we need an algorithm that is easy to parallelize, but still complex enough to take a few seconds of CPU time. To not scare away the interested reader, we need this algorithm to be understandable and, if possible, also interesting. We chose a classical algorithm for demonstrating parallel programming: estimating the value of number π.,Although we are performing the loops in a serial way in the snippet above, nothing avoids us from performing this calculation in parallel. The following example shows that parts of the computations can be done independently:,Before we start to parallelize this program, we need to do our best to make the inner function as efficient as we can. We show two techniques for doing this: vectorization using numpy and native code generation using numba.,Can you think of a task in your domain that is parallelizable? Can you also think of one that is fundamentally non-parallelizable?

x = [1, 2, 3, 4] # Write input

y = 0 # Initialize output

for i in range(len(x)):
   y += x[i] # Add each element to the output variable

print(y) # Print output

x = [1, 2, 4, 4]

chunk1 = x[: 2]
chunk2 = x[2: ]

sum_1 = sum(chunk1)
sum_2 = sum(chunk2)

result = sum_1 + sum_2

print(result)

y = [n ** 2
   for n in x
]

# Summation making use of numpy:
   import numpy as np
result = np.arange(10 ** 7).sum()

# The same summation, but using dask to parallelize the code.
# NB: the API
for dask arrays mimics that of numpy
import dask.array as da
work = da.arange(10 ** 7).sum()
result = work.compute()

% % time
np.arange(10 ** 7).sum()