So I had a paragraph of code "train.py" that goes like
do something
print('log something...')
do something else
and I used pytorch's multiprocessing toolbox
import torch.multiprocessing as mp
to execute multithreads of "train.py", however, the print function works well under python3, but not python2. Why?
so in python 2 multiprocessing, new process creation is by default fork() method and forking a multithreaded process could be problematic.
Pytorch uses multithreaded process and to do it safely they use the below-mentioned functionality of python 3
import multiprocessing as mp
def foo(q):
q.put('hello')
if __name__ == '__main__':
mp.set_start_method('spawn')
Note that above snippet will only work with python 3. They use method "set_start_method" and ask python interpreter to start a new process by using 'spawn' rather than 'fork'
As I said the above method only works in python 3's multiprocessing module and because python 2's multiprocessing module does not have "set_start_method", your code
import torch.multiprocessing as mp
might not work as expected. It does not give you an error but the results of the computation are not reliable
Although there are more differences, one of the main differences is the use of the with statement. An easy way to understand the difference is to see how to use multiprocessing using with for both Python 2 and Python 3. If you add (in Python 2 or 3):
# For python 2/3 compatibility, define pool context manager
# to support the 'with' statement in Python 2
if sys.version_info[0] == 2:
from contextlib import contextmanager
#contextmanager
def multiprocessing_context(*args, **kwargs):
pool = multiprocessing.Pool(*args, **kwargs)
yield pool
pool.terminate()
else:
multiprocessing_context = multiprocessing.Pool
After that, you can use multiprocessing the regular Python 3 way, regardless of which version of Python you are using. For example:
def _function_to_run_for_each(x):
return x.lower()
with multiprocessing_context(processes=3) as pool:
results = pool.map(_function_to_run_for_each, ['Bob', 'Sue', 'Tim']) print(results)
will work in Python 2 or Python 3.
Related
I have a fairly large codebase written in numba, and I have noticed that when the cache is enabled for a function calling another numba compiled function in another file, changes in the called function are not picked up when the called function is changed. The situation occurs when I have two files:
testfile2:
import numba
#numba.njit(cache=True)
def function1(x):
return x * 10
testfile:
import numba
from tests import file1
#numba.njit(cache=True)
def function2(x, y):
return y + file1.function1(x)
If in a jupyter notebook, I run the following:
# INSIDE JUPYTER NOTEBOOK
import sys
sys.path.insert(1, "path/to/files/")
from tests import testfile
testfile.function2(3, 4)
>>> 34 # good value
However, if I change then change testfile2 to the following:
import numba
#numba.njit(cache=True)
def function1(x):
return x * 1
Then I restart the jupyter notebook kernel and rerun the notebook, I get the following
import sys
sys.path.insert(1, "path/to/files/")
from tests import testfile
testfile.function2(3, 4)
>>> 34 # bad value, should be 7
Importing both files into the notebook has no effect on the bad result. Also, setting cache=False only on function1 also has no effect. What does work is setting cache=False on all njit'ted functions, then restarting the kernel, then rerunning.
I believe that LLVM is probably inlining some of the called functions and then never checking them again.
I looked in the source and discovered there is a method that returns the cache object numba.caching.NullCache(), instantiated a cache object and ran the following:
cache = numba.caching.NullCache()
cache.flush()
Unfortunately that appears to have no effect.
Is there a numba environment setting, or another way I can manually clear all cached functions within a conda env? Or am I simply doing something wrong?
I am running numba 0.33 with Anaconda Python 3.6 on Mac OS X 10.12.3.
I "solved" this with a hack solution after seeing Josh's answer, by creating a utility in the project method to kill off the cache.
There is probably a better way, but this works. I'm leaving the question open in case someone has a less hacky way of doing this.
import os
def kill_files(folder):
for the_file in os.listdir(folder):
file_path = os.path.join(folder, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
except Exception as e:
print("failed on filepath: %s" % file_path)
def kill_numba_cache():
root_folder = os.path.realpath(__file__ + "/../../")
for root, dirnames, filenames in os.walk(root_folder):
for dirname in dirnames:
if dirname == "__pycache__":
try:
kill_files(root + "/" + dirname)
except Exception as e:
print("failed on %s", root)
This is a bit of a hack, but it's something I've used before. If you put this function in the top-level of where your numba functions are (for this example, in testfile), it should recompile everything:
import inspect
import sys
def recompile_nb_code():
this_module = sys.modules[__name__]
module_members = inspect.getmembers(this_module)
for member_name, member in module_members:
if hasattr(member, 'recompile') and hasattr(member, 'inspect_llvm'):
member.recompile()
and then call it from your jupyter notebook when you want to force a recompile. The caveat is that it only works on files in the module where this function is located and their dependencies. There might be another way to generalize it.
import multiprocessing as mul
def f(x):
return x**2
pool = mul.Pool(5)
rel = pool.map(f,[1,2,3,4,5,6,7,8,9,10])
print(rel)
When I run the program above, the application is stuck in a loop and can't stop.
I am using python 3.5 in windows, is there something wrong?
This is what I see on my screen:
I am new to finance data analysis; and I am trying to find out a way to solve the big data problem with parallel computing.
Its not working because you are typing the commands in a shell; try saving the code in a file and running it directly.
Don't forget to copy the code correctly, you were missing a very important if statement (see the documentation).
Save this to a file, for example example.py on the desktop:
import multiprocessing as mul
def f(x):
return x**2
if __name__ == '__main__':
pool = mul.Pool(5)
rel = pool.map(f,[1,2,3,4,5,6,7,8,9,10])
print(rel)
Then, open a command prompt and type:
python %USERPROFILE%\Desktop\example.py
All code written and tested on python 3.4 windows 7.
I was designing a console app and had a need to use stdin from command-line (win os) to issue commands and to change the operating mode of the program. The program depends on multiprocessing to deal with cpu bound loads to spread to multiple processors.
I am using stdout to monitor that status and some basic return information and stdin to issue commands to load different sub-processes based on the returned console information.
This is where I found a problem. I could no get the multiprocessing module to accept stdin inputs but stdout was working just fine. I think found the following help on stack So I tested it and found that with the threading module this all works great, except for the fact that all output to stdout is paused until each time stdin is cycled due to GIL lock with stdin blocking.
I will say I have been successful with a work around implemented with msvcrt.kbhit(). However, I can't help but wonder if there is some sort of bug in the multiprocessing feature that is making stdin not read any data. I tried numerous ways and nothing worked when using multiprocessing. Even attempted to use Queues, but I did not try pools, or any other methods from multiprocessing.
I also did not try this on my linux machine since I was focusing on trying to get it to work.
Here is simplified test code that does not function as intended (reminder this was written in Python 3.4 - win7):
import sys
import time
from multiprocessing import Process
def function1():
while True:
print("Function 1")
time.sleep(1.33)
def function2():
while True:
print("Function 2")
c = sys.stdin.read(1) # Does not appear to be waiting for read before continuing loop.
sys.stdout.write(c) #nothing in 'c'
sys.stdout.write(".") #checking to see if it works at all.
print(str(c)) #trying something else, still nothing in 'c'
time.sleep(1.66)
if __name__ == "__main__":
p1 = Process(target=function1)
p2 = Process(target=function2)
p1.start()
p2.start()
Hopefully someone can shed light on whether this is intended functionality, if I didn't implement it correctly, or some other useful bit of information.
Thanks.
When you take a look at Pythons implementation of multiprocessing.Process._bootstrap() you will see this:
if sys.stdin is not None:
try:
sys.stdin.close()
sys.stdin = open(os.devnull)
except (OSError, ValueError):
pass
You can also confirm this by using:
>>> import sys
>>> import multiprocessing
>>> def func():
... print(sys.stdin)
...
>>> p = multiprocessing.Process(target=func)
>>> p.start()
>>> <_io.TextIOWrapper name='/dev/null' mode='r' encoding='UTF-8'>
And reading from os.devnull immediately returns empty result:
>>> import os
>>> f = open(os.devnull)
>>> f.read(1)
''
You can work this around by using open(0):
file is either a string or bytes object giving the pathname (absolute or relative to the current working directory) of the file to be opened or an integer file descriptor of the file to be wrapped. (If a file descriptor is given, it is closed when the returned I/O object is closed, unless closefd is set to False.)
And "0 file descriptor":
File descriptors are small integers corresponding to a file that has been opened by the current process. For example, standard input is usually file descriptor 0, standard output is 1, and standard error is 2:
>>> def func():
... sys.stdin = open(0)
... print(sys.stdin)
... c = sys.stdin.read(1)
... print('Got', c)
...
>>> multiprocessing.Process(target=func).start()
>>> <_io.TextIOWrapper name=0 mode='r' encoding='UTF-8'>
Got a
I'm trying to use parallel computing from ipython parallel library. But I have little knowledge about it and I find the doc difficult to read from someone who knows nothing about parallel computing.
Funnily, all tutorials I found just re-use the example in the doc, with the same explanation, which from my point of view, is useless.
Basically what I'd like to do is running few scripts in background so they are executed in the same time. In bash it would be something like :
for my_file in $(cat list_file); do
python pgm.py my_file &
done
But bash interpreter of Ipython notebook doesn't handle the background mode.
It seems that solution was to use parallel library from ipython.
I tried :
from IPython.parallel import Client
rc = Client()
rc.block = True
dview = rc[:2] # I take only 2 engines
But then I'm stuck. I don't know how to run twice (or more) the same script or pgm at the same time.
Thanks.
One year later, I eventually managed to get what I wanted.
1) Create a function with what you want to do on the different cpu. Here it is just calling a script from the bash with the ! magic ipython command. I guess it would work with the call() function.
def my_func(my_file):
!python pgm.py {my_file}
Don't forget the {} when using !
Note also that the path to my_file should be absolute, since the clusters are where you started the notebook (when doing jupyter notebook or ipython notebook) which is not necessarily where you are.
2) Start your ipython notebook Cluster with the number of CPU you want.
Wait 2s and execute the following cell:
from IPython import parallel
rc = parallel.Client()
view = rc.load_balanced_view()
3) Get a list of file you want to process:
files = list_of_files
4) Map asynchronously your function with all your files to the view of your engines you just created. (not sure of the wording).
r = view.map_async(my_func, files)
While it's running you can do something else on the notebook (It runs in "background"!). You can also call r.wait_interactive() that enumerates interactively the number of files processed and the number of time spent so far and the number of files left. This will prevent you to run other cells (but you can interrupt it).
And if you have more files than engines, no worries, they will be processed as soon as an engine finishes with 1 file.
Hope this will help others !
This tutorial might be of some help:
http://nbviewer.ipython.org/github/minrk/IPython-parallel-tutorial/blob/master/Index.ipynb
Note also that I still have IPython 2.3.1, I don't know if it changed since Jupyter.
Edit: Still works with Jupyter, see here for difference and potential issues you may encounter
Note that if you use external libraries in your function, you need to import them on the different engines with:
%px import numpy as np
or
%%px
import numpy as np
import pandas as pd
Same with variable and other functions, you need to push them to the engine name space:
rc[:].push(dict(
foo=foo,
bar=bar))
If you're trying to executing some external scripts in parallel, you don't need to use IPython's parallel functionality. Replicating bash's parallel execution can be achieved with the subprocess module as follows:
import subprocess
procs = []
for i in range(10):
procs.append(subprocess.Popen(['ls', '/Users/shad/tmp/'], stdout=subprocess.PIPE))
results = []
for proc in procs:
stdout, _ = proc.communicate()
results.append(stdout)
Be wary that if your subprocess generates a lot of output, the process will block. If you print the output (results) you get:
print results
['file1\nfile2\n', 'file1\nfile2\n', 'file1\nfile2\n', 'file1\nfile2\n', 'file1\nfile2\n', 'file1\nfile2\n', 'file1\nfile2\n', 'file1\nfile2\n', 'file1\nfile2\n', 'file1\nfile2\n']
I would like to give IronPython and Mono a try. Specifically doing sysadmin tasks. Which often means running OS commands.
In CPython I use the subprocess module for such tasks.
But in IronPython (v2.0.1, Mono 2.4, Linux) there is no subprocess module. It seems there is not even an 'os' module. So I can't use os.system().
What would be the IronPython way of doing tasks you would normally use 'subprocess' or 'os.system()' for in CPython?
I have found an answer. Thanks to the "IronPython Cookbook".
One can find more information on this subject there:
http://www.ironpython.info/index.php/Launching_Sub-Processes
>>> from System.Diagnostics import Process
>>> p = Process()
>>> p.StartInfo.UseShellExecute = False
>>> p.StartInfo.RedirectStandardOutput = True
>>> p.StartInfo.FileName = 'uname'
>>> p.StartInfo.Arguments = '-m -r'
>>> p.Start()
True
>>> p.WaitForExit()
>>> p.StandardOutput.ReadToEnd()
'9.6.0 i386\n'
>>> p.ExitCode
0
>>>
You can use most of the standard os modules from within ironpython.
import sys
sys.path.append path('...pathtocpythonlib......')
import os
Consider this C# Interactive Shell too....not sure if it supports IronPhython in the shell, but Mono does as you know.
There is a partial subprocess module implementation here:
http://www.bitbucket.org/jdhardy/code/src/tip/subprocess.py
The module (at this time, June 2010) is only supports redirecting STDIO pipes (as in, you cannot provide your own file-like objects to be filled with output or to stream intput), but the basics are enough to get by.