I'm reading video File from opencv and store their frames in a list, Then I provide this list to face detection function which in turn store the face location in another list, the problem is that when I give an equal number of frame to multiprocessing code and single processing code, the performance is not very different. please check my code, suggest the possible solution. I am using python 3.5, the number of CPU core is 4. Multiprocessing code is supposed to give almost 4 times performance but it only gives few second gains.
My code:
import cv2,time,dlib,imutils
from multiprocessing import Pool
detector = dlib.get_frontal_face_detector()
vidcap=cv2.VideoCapture(r'/home/deeplearning/PycharmProjects
/sjtech/jurassic_park_intro.mp4')
count = 0
frame_list = []
def parallel_detection(f):
return detector(f,1)
success,image = vidcap.read()
while success:
print('Read a new frame: ', success)
frame_list.append(image)
count += 1
success,image = vidcap.read()
del frame_list[-1]
print("out of while")
p = Pool()
t1 = time.time()
#below is my multiprocessing code, on 40 frames it takes 42 seconds
face_location=p.map(parallel_detection,frame_list[900:940])
#below is single processing code, it takes 50 seconds
face_location=[detector(frame_list[x],1) for x in range(900,940)]
print(time.time()-t1)
Related
I've experienced some difficulties when using multiprocessing Pool in python3. I want to do BIG array calculation by using pool.map. Basically, I've a 3D array which I need to do computation for 10 times and it generates 10 output files sequentially. This task can be done 3 times i,e, in the output we get 3*10=30 output files(*.txt). To do this, I've prepared the following script for small array calculation (a sample problem). However, when I use this script for a BIG array calculation or array come out from a series of files, then this piece of code (maybe pool) capture the memory, and it does not save any .txt file at the destination directory. There is no error message when I run the file with command mpirun python3 sample_prob_func.py
Can anybody suggest what is the problem in the sample script and how to write code to get rid of stuck? I've not received any error message, but don't know where the problem occurs. Any help is appreciated. Thanks!
import numpy as np
import multiprocessing as mp
from scipy import signal
import matplotlib.pyplot as plt
import contextlib
import os, glob, re
import random
import cmath, math
import time
import pdb
#File Storing path
save_results_to = 'File saving path'
arr_x = [0, 8.49, 0.0, -8.49, -12.0, -8.49, -0.0, 8.49, 12.0]
arr_y = [0, 8.49, 12.0, 8.49, 0.0, -8.49, -12.0, -8.49, -0.0]
N=len(arr_x)
np.random.seed(12345)
total_rows = 5000
arr = np.reshape(np.random.rand(total_rows*N),(total_rows, N))
arr1 = np.reshape(np.random.rand(total_rows*N),(total_rows, N))
arr2 = np.reshape(np.random.rand(total_rows*N),(total_rows, N))
# Finding cross spectral density (CSD)
def my_func1(data):
# Do something here
return array1
t0 = time.time()
my_data1 = my_func1(arr)
my_data2 = my_func1(arr1)
my_data3 = my_func1(arr2)
print('Time required {} seconds to execute CSD--For loop'.format(time.time()-t0))
mydata_list = [my_data1,my_data3,my_data3]
def my_func2(data2):
# Do something here
return from_data2
start_freq = 100
stop_freq = 110
freq_range= np.around(np.linspace(start_freq,stop_freq,11)/10, decimals=2)
no_of_freq = len(freq_range)
list_arr =[]
def my_func3(csd):
list_csd=[]
for fr_count in range(start_freq, stop_freq):
csd_single = csd[:,:, fr_count]
list_csd.append(csd_single)
print('Shape of list is :', np.array(list_csd).shape)
return list_csd
def parallel_function(BIG_list_data):
with contextlib.closing(mp.Pool(processes=10)) as pool:
dft= pool.map(my_func2, BIG_list_data)
pool.close()
pool.join()
data_arr = np.array(dft)
print('shape of data :', data_arr.shape)
return data_arr
count_day = 1
count_hour =0
for count in range(3):
count_hour +=1
list_arr = my_func3(mydata_list[count]) # Load Numpy files
print('Array shape is :', np.array(arr).shape)
t0 = time.time()
data_dft = parallel_function(list_arr)
print('The hour number={} data is processing... '.format(count_hour))
print('Time in parallel:', time.time() - t0)
for i in range(no_of_freq-1): # (11-1=10)
jj = freq_range[i]
#print('The hour_number {} and frequency number {} data is processing... '.format(count_hour, jj))
dft_1hr_complx = data_dft[i,:,:]
np.savetxt(save_results_to + f'csd_Day_{count_day}_Hour_{count_hour}_f_{jj}_hz.txt', dft_1hr_complx.view(float))
As #JérômeRichard suggested,to aware your job scheduler you need to define the number of processors will engage to perform this task. So, the following command could help you: ncpus = int(os.getenv('SLURM_CPUS_PER_TASK', 1))
You need to use this line inside your python script. Also, inside the parallel_function use with contextlib.closing(mp.Pool(ncpus=10)) as pool: instead of with contextlib.closing(mp.Pool(processes=10)) as pool:. Thanks
I'm using a multiprocessing pool with 80 processes on a 16GB machine. The flow is as follows:
Read objects in batches from an input file
Send the entire batch to a multiprocessing pool, and record the time taken by the pool to process the batch
Write the time recorded in step 2 above to an output file
To achieve the above, I wrote code in 2 ways:
Way 1:
with open('input_file', 'r') as input_file, open('output_file', 'a') as of:
batch = read_next_batch_of_lines()
start_time = time.time()
call_api_for_each_item_in_batch(batch)
end_time = time.time()
of.write('{}\n'.format(end_time-start_time))
Way 2:
with open('input_file', 'r') as input_file:
batch = read_next_batch_of_lines()
start_time = time.time()
call_api_for_each_item_in_batch(batch)
end_time = time.time()
with open('output_file', 'a') as of:
of.write('{}\n'.format(end_time-start_time))
In the first case, nothing is being appended to the output file despite batches being processed. I'm unable to figure out the reason for this.
Details of call_api_for_each_item_in_batch():
def call_api_for_each_item_in_batch(batch):
intervals = get_intervals(batch, pool_size) #this gives intervals. Ex. if batch size is 10 and pool size is 3, then intervals would be (0, 4, 7, 10)
pool = mp.Pool(pool_size)
arguments = list(zip(intervals, intervals[1:]))
pool.starmap(call_api, arguments)
pool.close()
def call_api(start, end):
for i in range(start, end):
item = batch[i]
call_external_api(item)
How is Way 1 different from Way 2 when a pool.close() is called in the call_api_for_each_item_in_batch itself?
Also, I used pool.close() followed by pool.join(), but faced the same issue.
I run Windows 10, Python 3.7, and have a 6-core CPU. A single Python thread on my machine submits 1,000 inserts per second to grakn. I'd like to parallelize my code to insert and match even faster. How are people doing this?
My only experience with parellelization is on another project, where I submit a custom function to a dask distributed client to generate thousands of tasks. Right now, this same approach fails whenever the custom function receives or generates a grakn transaction object/handle. I get errors like:
Traceback (most recent call last):
File "C:\Users\dvyd\.conda\envs\activefiction\lib\site-packages\distributed\protocol\pickle.py", line 41, in dumps
return cloudpickle.dumps(x, protocol=pickle.HIGHEST_PROTOCOL)
...
File "stringsource", line 2, in grpc._cython.cygrpc.Channel.__reduce_cython__
TypeError: no default __reduce__ due to non-trivial __cinit__
I've never used Python's multiprocessing module directly. What are other people doing to parallelize their queries to grakn?
The easiest approach that I've found to execute a batch of queries is to pass a Grakn session to each thread in a ThreadPool. Within each thread you can manage transactions and of course do some more complex logic:
from grakn.client import GraknClient
from multiprocessing.dummy import Pool as ThreadPool
from functools import partial
def write_query_batch(session, batch):
tx = session.transaction().write()
for query in batch:
tx.query(query)
tx.commit()
def multi_thread_write_query_batches(session, query_batches, num_threads=8):
pool = ThreadPool(num_threads)
pool.map(partial(write_query_batch, session), query_batches)
pool.close()
pool.join()
def generate_query_batches(my_data_entries_list, batch_size):
batch = []
for index, data_entry in enumerate(my_data_entries_list):
batch.append(data_entry)
if index % batch_size == 0 and index != 0:
yield batch
batch = []
if batch:
yield batch
# (Part 2) Somewhere in your application open a client and a session
client = GraknClient(uri="localhost:48555")
session = client.session(keyspace="grakn")
query_batches_iterator = generate_query_batches(my_data_entries_list, batch_size)
multi_thread_write_query_batches(session, query_batches_iterator, num_threads=8)
session.close()
client.close()
The above is a generic method. As a concrete example, you can use the above (omitting part 2) to parallelise batches of insert statements from two files. Appending this to the above should work:
files = [
{
"file_path": f"/path/to/your/file.gql",
},
{
"file_path": f"/path/to/your/file2.gql",
}
]
KEYSPACE = "grakn"
URI = "localhost:48555"
BATCH_SIZE = 10
NUM_BATCHES = 1000
# Entry point where migration starts
def migrate_graql_files():
start_time = time.time()
for file in files:
print('==================================================')
print(f'Loading from {file["file_path"]}')
print('==================================================')
open_file = open(file["file_path"], "r") # Here we are assuming you have 1 Graql query per line!
batches = generate_query_batches(open_file.readlines(), BATCH_SIZE)
with GraknClient(uri=URI) as client: # Using `with` auto-closes the client
with client.session(KEYSPACE) as session: # Using `with` auto-closes the session
multi_thread_write_query_batches(session, batches, num_threads=16) # Pick `num_threads` according to your machine
elapsed = time.time() - start_time
print(f'Time elapsed {elapsed:.1f} seconds')
elapsed = time.time() - start_time
print(f'Time elapsed {elapsed:.1f} seconds')
if __name__ == "__main__":
migrate_graql_files()
You should also be able to see how you can load from a csv or any other file type in this way, but taking the values you find in that file and substitution them into Graql query string templates. Take a look at the migration example in the docs for more on that.
An alternative approach using multi-processing instead of multi-threading follows below.
We empirically found that multi-threading doesn't yield particularly large performance gains, compared to multi-processing. This is probably due to Python's GIL.
This piece of code assumes a file enumerating TypeQL queries that are independent of each other, so they can be parallelised freely.
from typedb.client import TypeDB, TypeDBClient, SessionType, TransactionType
import multiprocessing as mp
import queue
def batch_writer(database, kill_event, batch_queue):
client = TypeDB.core_client("localhost:1729")
session = client.session(database, SessionType.DATA)
while not kill_event.is_set():
try:
batch = batch_queue.get(block=True, timeout=1)
with session.transaction(TransactionType.WRITE) as tx:
for query in batch:
tx.query().insert(query)
tx.commit()
except queue.Empty:
continue
print("Received kill event, exiting worker.")
def start_writers(database, kill_event, batch_queue, parallelism=4):
processes = []
for _ in range(parallelism):
proc = mp.Process(target=batch_writer, args=(database, kill_event, batch_queue))
processes.append(proc)
proc.start()
return processes
def batch(iterable, n=1000):
l = len(iterable)
for ndx in range(0, l, n):
yield iterable[ndx:min(ndx + n, l)]
if __name__ == '__main__':
batch_size = 100
parallelism = 1
database = "<database name>"
# filePath = "<PATH TO QUERIES FILE - ONE QUERY PER NEW LINE>"
with open(file_path, "r") as file:
statements = file.read().splitlines()[:]
batch_statements = batch(statements, n=batch_size)
total_batches = int(len(statements) / batch_size)
if total_batches % batch_size > 0:
total_batches += 1
batch_queue = mp.Queue(parallelism * 4)
kill_event = mp.Event()
writers = start_writers(database, kill_event, batch_queue, parallelism=parallelism)
for i, batch in enumerate(batch_statements):
batch_queue.put(batch, block=True)
if i*batch_size % 10000 == 0:
print("Loaded: {0}/{1}".format(i*batch_size, total_batches*batch_size))
kill_event.set()
batch_queue.close()
batch_queue.join_thread()
for proc in writers:
proc.join()
print("Done loading")
In Python 3.6, I am running multiple processes in parallel, where each process pings a URL and returns a Pandas dataframe. I want to keep running the (2+) processes continually, I have created a minimal representative example as below.
My questions are:
1) My understanding is that since I have different functions, I cannot use Pool.map_async() and its variants. Is that right? The only examples of these I have seen were repeating the same function, like on this answer.
2) What is the best practice to make this setup to run perpetually? In my code below, I use a while loop, which I suspect is not suited for this purpose.
3) Is the way I am using the Process and Manager optimal? I use multiprocessing.Manager.dict() as the shared dictionary to return the results form the processes. I saw in a comment on this answer that using a Queue here would make sense, however the Queue object has no `.dict()' method. So, I am not sure how that would work.
I would be grateful for any improvements and suggestions with example code.
import numpy as np
import pandas as pd
import multiprocessing
import time
def worker1(name, t , seed, return_dict):
'''worker function'''
print(str(name) + 'is here.')
time.sleep(t)
np.random.seed(seed)
df= pd.DataFrame(np.random.randint(0,1000,8).reshape(2,4), columns=list('ABCD'))
return_dict[name] = [df.columns.tolist()] + df.values.tolist()
def worker2(name, t, seed, return_dict):
'''worker function'''
print(str(name) + 'is here.')
np.random.seed(seed)
time.sleep(t)
df = pd.DataFrame(np.random.randint(0, 1000, 12).reshape(3, 4), columns=list('ABCD'))
return_dict[name] = [df.columns.tolist()] + df.values.tolist()
if __name__ == '__main__':
t=1
while True:
start_time = time.time()
manager = multiprocessing.Manager()
parallel_dict = manager.dict()
seed=np.random.randint(0,1000,1) # send seed to worker to return a diff df
jobs = []
p1 = multiprocessing.Process(target=worker1, args=('name1', t, seed, parallel_dict))
p2 = multiprocessing.Process(target=worker2, args=('name2', t, seed+1, parallel_dict))
jobs.append(p1)
jobs.append(p2)
p1.start()
p2.start()
for proc in jobs:
proc.join()
parallel_end_time = time.time() - start_time
#print(parallel_dict)
df1= pd.DataFrame(parallel_dict['name1'][1:],columns=parallel_dict['name1'][0])
df2 = pd.DataFrame(parallel_dict['name2'][1:], columns=parallel_dict['name2'][0])
merged_df = pd.concat([df1,df2], axis=0)
print(merged_df)
Answer 1 (map on multiple functions)
You're technically right.
With map, map_async and other variations, you should use a single function.
But this constraint can be bypassed by implementing an executor, and passing the function to execute as part of the parameters:
def dispatcher(args):
return args[0](*args[1:])
So a minimum working example:
import multiprocessing as mp
def function_1(v):
print("hi %s"%v)
return 1
def function_2(v):
print("by %s"%v)
return 2
def dispatcher(args):
return args[0](*args[1:])
with mp.Pool(2) as p:
tasks = [
(function_1, "A"),
(function_2, "B")
]
r = p.map_async(dispatcher, tasks)
r.wait()
results = r.get()
Answer 2 (Scheduling)
I would remove the while from the script and schedule a cron job (on GNU/Linux) (on windows) so that the OS will be responsible for it's execution.
On Linux you can run cronotab -e and add the following line to make the script run every 5 minutes.
*/5 * * * * python /path/to/script.py
Answer 3 (Shared Dictionary)
yes but no.
To my knowledge using the Manager for data such as collections is the best way.
For Arrays or primitive types (int, floats, ecc) exists Value and Array which are faster.
As in the documentation
A manager object returned by Manager() controls a server process which holds > Python objects and allows other processes to manipulate them using proxies.
A manager returned by Manager() will support types list, dict, Namespace, Lock, > RLock, Semaphore, BoundedSemaphore, Condition, Event, Barrier, Queue, Value and > Array.
Server process managers are more flexible than using shared memory objects because they can be made to support arbitrary object types. Also, a single manager can be shared by processes on different computers over a network. They are, however, slower than using shared memory.
But you have only to return a Dataframe, so the shared dictionary it's not needed.
Cleaned Code
Using all the previous ideas the code can be rewritten as:
map version
import numpy as np
import pandas as pd
from time import sleep
import multiprocessing as mp
def worker1(t , seed):
print('worker1 is here.')
sleep(t)
np.random.seed(seed)
return pd.DataFrame(np.random.randint(0,1000,8).reshape(2,4), columns=list('ABCD'))
def worker2(t , seed):
print('worker2 is here.')
sleep(t)
np.random.seed(seed)
return pd.DataFrame(np.random.randint(0, 1000, 12).reshape(3, 4), columns=list('ABCD'))
def dispatcher(args):
return args[0](*args[1:])
def task_generator(sleep_time=1):
seed = np.random.randint(0,1000,1)
yield worker1, sleep_time, seed
yield worker2, sleep_time, seed + 1
with mp.Pool(2) as p:
results = p.map(dispatcher, task_generator())
merged = pd.concat(results, axis=0)
print(merged)
If the process of concatenation of the Dataframe is the bottleneck, An approach with imap might become optimal.
imap version
with mp.Pool(2) as p:
merged = pd.DataFrame()
for result in p.imap_unordered(dispatcher, task_generator()):
merged = pd.concat([merged,result], axis=0)
print(merged)
The main difference is that in the map case, the program first wait for all the process tasks to end, and then concatenate all the Dataframes.
While in the imap_unoredered case, As soon as a task as ended, the Dataframe is concatenated ot the current results.
That's a normal Python Code which is running normally
import pandas as pd
dataset=pd.read_csv(r'C:\Users\efthi\Desktop\machine_learning.csv')
registration = pd.read_csv(r'C:\Users\efthi\Desktop\studentVle.csv')
students = list()
result = list()
p=350299
i =749
interactions = 0
while i <8659:
student = dataset["id_student"][i]
print(i)
i +=1
while p <1917865:
if student == registration['id_student'][p]:
interactions += registration ["sum_click"][p]
p+=1
students.insert(i,student)
result.insert(i,interactions)
p=0
interactions = 0
st = pd.DataFrame(students)#create data frame
st.to_csv(r'C:\Users\efthi\Desktop\ttest.csv', index=False)#insert data frame to csv
st = pd.DataFrame(result)#create data frame
st.to_csv(r'C:\Users\efthi\Desktop\results.csv', index=False)#insert data frame to csv
This is supposed to be running in an even bigger dataset, which I think is more efficient to utilize the multiple cores of my pc
How can I implement it to use all 4 cores?
For performing any function in parallel you can something like:
import multiprocessing
import pandas as pd
def f(x):
# Perform some function
return y
# Load your data
data = pd.read_csv('file.csv')
# Look at docs to see why "if __name__ == '__main__'" is necessary
if __name__ == '__main__':
# Create pool with 4 processors
pool = multiprocessing.Pool(4)
# Create jobs
jobs = []
for group in data['some_group']:
# Create asynchronous jobs that will be submitted once a processor is ready
data_for_job = data[data.some_group == group]
jobs.append(pool.apply_async(f, (data_for_job, )))
# Submit jobs
results = [job.get() for job in jobs]
# Combine results
results_df = pd.concat(results)
Regardless of the function your performing, for multiprocessing you:
Create a pool with your desired number of processors
Loop through your data in whatever way you want to chunk it
Create a job with that chunk (using pool.apply_async() <- read the docs about this if it's confusing)
Submit your jobs with job.get()
Combine your results