I am trying to return values from subprocesses but these values are unfortunately unpicklable. So I used global variables in threads module with success but have not been able to retrieve updates done in subprocesses when using multiprocessing module. I hope I'm missing something.
The results printed at the end are always the same as initial values given the vars dataDV03 and dataDV04. The subprocesses are updating these global variables but these global variables remain unchanged in the parent.
import multiprocessing
# NOT ABLE to get python to return values in passed variables.
ants = ['DV03', 'DV04']
dataDV03 = ['', '']
dataDV04 = {'driver': '', 'status': ''}
def getDV03CclDrivers(lib): # call global variable
global dataDV03
dataDV03[1] = 1
dataDV03[0] = 0
# eval( 'CCL.' + lib + '.' + lib + '( "DV03" )' ) these are unpicklable instantiations
def getDV04CclDrivers(lib, dataDV04): # pass global variable
dataDV04['driver'] = 0 # eval( 'CCL.' + lib + '.' + lib + '( "DV04" )' )
if __name__ == "__main__":
jobs = []
if 'DV03' in ants:
j = multiprocessing.Process(target=getDV03CclDrivers, args=('LORR',))
jobs.append(j)
if 'DV04' in ants:
j = multiprocessing.Process(target=getDV04CclDrivers, args=('LORR', dataDV04))
jobs.append(j)
for j in jobs:
j.start()
for j in jobs:
j.join()
print 'Results:\n'
print 'DV03', dataDV03
print 'DV04', dataDV04
I cannot post to my question so will try to edit the original.
Here is the object that is not picklable:
In [1]: from CCL import LORR
In [2]: lorr=LORR.LORR('DV20', None)
In [3]: lorr
Out[3]: <CCL.LORR.LORR instance at 0x94b188c>
This is the error returned when I use a multiprocessing.Pool to return the instance back to the parent:
Thread getCcl (('DV20', 'LORR'),)
Process PoolWorker-1:
Traceback (most recent call last):
File "/alma/ACS-10.1/casa/lib/python2.6/multiprocessing/process.py", line 232, in _bootstrap
self.run()
File "/alma/ACS-10.1/casa/lib/python2.6/multiprocessing/process.py", line 88, in run
self._target(*self._args, **self._kwargs)
File "/alma/ACS-10.1/casa/lib/python2.6/multiprocessing/pool.py", line 71, in worker
put((job, i, result))
File "/alma/ACS-10.1/casa/lib/python2.6/multiprocessing/queues.py", line 366, in put
return send(obj)
UnpickleableError: Cannot pickle <type 'thread.lock'> objects
In [5]: dir(lorr)
Out[5]:
['GET_AMBIENT_TEMPERATURE',
'GET_CAN_ERROR',
'GET_CAN_ERROR_COUNT',
'GET_CHANNEL_NUMBER',
'GET_COUNT_PER_C_OP',
'GET_COUNT_REMAINING_OP',
'GET_DCM_LOCKED',
'GET_EFC_125_MHZ',
'GET_EFC_COMB_LINE_PLL',
'GET_ERROR_CODE_LAST_CAN_ERROR',
'GET_INTERNAL_SLAVE_ERROR_CODE',
'GET_MAGNITUDE_CELSIUS_OP',
'GET_MAJOR_REV_LEVEL',
'GET_MINOR_REV_LEVEL',
'GET_MODULE_CODES_CDAY',
'GET_MODULE_CODES_CMONTH',
'GET_MODULE_CODES_DIG1',
'GET_MODULE_CODES_DIG2',
'GET_MODULE_CODES_DIG4',
'GET_MODULE_CODES_DIG6',
'GET_MODULE_CODES_SERIAL',
'GET_MODULE_CODES_VERSION_MAJOR',
'GET_MODULE_CODES_VERSION_MINOR',
'GET_MODULE_CODES_YEAR',
'GET_NODE_ADDRESS',
'GET_OPTICAL_POWER_OFF',
'GET_OUTPUT_125MHZ_LOCKED',
'GET_OUTPUT_2GHZ_LOCKED',
'GET_PATCH_LEVEL',
'GET_POWER_SUPPLY_12V_NOT_OK',
'GET_POWER_SUPPLY_15V_NOT_OK',
'GET_PROTOCOL_MAJOR_REV_LEVEL',
'GET_PROTOCOL_MINOR_REV_LEVEL',
'GET_PROTOCOL_PATCH_LEVEL',
'GET_PROTOCOL_REV_LEVEL',
'GET_PWR_125_MHZ',
'GET_PWR_25_MHZ',
'GET_PWR_2_GHZ',
'GET_READ_MODULE_CODES',
'GET_RX_OPT_PWR',
'GET_SERIAL_NUMBER',
'GET_SIGN_OP',
'GET_STATUS',
'GET_SW_REV_LEVEL',
'GET_TE_LENGTH',
'GET_TE_LONG_FLAG_SET',
'GET_TE_OFFSET_COUNTER',
'GET_TE_SHORT_FLAG_SET',
'GET_TRANS_NUM',
'GET_VDC_12',
'GET_VDC_15',
'GET_VDC_7',
'GET_VDC_MINUS_7',
'SET_CLEAR_FLAGS',
'SET_FPGA_LOGIC_RESET',
'SET_RESET_AMBSI',
'SET_RESET_DEVICE',
'SET_RESYNC_TE',
'STATUS',
'_HardwareDevice__componentName',
'_HardwareDevice__hw',
'_HardwareDevice__stickyFlag',
'_LORRBase__logger',
'__del__',
'__doc__',
'__init__',
'__module__',
'_devices',
'clearDeviceCommunicationErrorAlarm',
'getControlList',
'getDeviceCommunicationErrorCounter',
'getErrorMessage',
'getHwState',
'getInternalSlaveCanErrorMsg',
'getLastCanErrorMsg',
'getMonitorList',
'hwConfigure',
'hwDiagnostic',
'hwInitialize',
'hwOperational',
'hwSimulation',
'hwStart',
'hwStop',
'inErrorState',
'isMonitoring',
'isSimulated']
In [6]:
When you use multiprocessing to open a second process, an entirely new instance of Python, with its own global state, is created. That global state is not shared, so changes made by child processes to global variables will be invisible to the parent process.
Additionally, most of the abstractions that multiprocessing provides use pickle to transfer data. All data transferred using proxies must be pickleable; that includes all the objects that a Manager provides. Relevant quotations (my emphasis):
Ensure that the arguments to the methods of proxies are picklable.
And (in the Manager section):
Other processes can access the shared objects by using proxies.
Queues also require pickleable data; the docs don't say so, but a quick test confirms it:
import multiprocessing
import pickle
class Thing(object):
def __getstate__(self):
print 'got pickled'
return self.__dict__
def __setstate__(self, state):
print 'got unpickled'
self.__dict__.update(state)
q = multiprocessing.Queue()
p = multiprocessing.Process(target=q.put, args=(Thing(),))
p.start()
print q.get()
p.join()
Output:
$ python mp.py
got pickled
got unpickled
<__main__.Thing object at 0x10056b350>
The one approach that might work for you, if you really can't pickle the data, is to find a way to store it as a ctype object; a reference to the memory can then be passed to a child process. This seems pretty dodgy to me; I've never done it. But it might be a possible solution for you.
Given your update, it seems like you need to know a lot more about the internals of a LORR. Is LORR a class? Can you subclass from it? Is it a subclass of something else? What's its MRO? (Try LORR.__mro__ and post the output if it works.) If it's a pure python object, it might be possible to subclass it, creating a __setstate__ and a __getstate__ to enable pickling.
Another approach might be to figure out how to get the relevant data out of a LORR instance and pass it via a simple string. Since you say that you really just want to call the methods of the object, why not just do so using Queues to send messages back and forth? In other words, something like this (schematically):
Main Process Child 1 Child 2
LORR 1 LORR 2
child1_in_queue -> get message 'foo'
call 'foo' method
child1_out_queue <- return foo data string
child2_in_queue -> get message 'bar'
call 'bar' method
child2_out_queue <- return bar data string
#DBlas gives you a quick url and reference to the Manager class in an answer, but I think its still a bit vague so I thought it might be helpful for you to just see it applied...
import multiprocessing
from multiprocessing import Manager
ants = ['DV03', 'DV04']
def getDV03CclDrivers(lib, data_dict):
data_dict[1] = 1
data_dict[0] = 0
def getDV04CclDrivers(lib, data_list):
data_list['driver'] = 0
if __name__ == "__main__":
manager = Manager()
dataDV03 = manager.list(['', ''])
dataDV04 = manager.dict({'driver': '', 'status': ''})
jobs = []
if 'DV03' in ants:
j = multiprocessing.Process(
target=getDV03CclDrivers,
args=('LORR', dataDV03))
jobs.append(j)
if 'DV04' in ants:
j = multiprocessing.Process(
target=getDV04CclDrivers,
args=('LORR', dataDV04))
jobs.append(j)
for j in jobs:
j.start()
for j in jobs:
j.join()
print 'Results:\n'
print 'DV03', dataDV03
print 'DV04', dataDV04
Because multiprocessing actually uses separate processes, you cannot simply share global variables because they will be in completely different "spaces" in memory. What you do to a global under one process will not reflect in another. Though I admit that it seems confusing since the way you see it, its all living right there in the same piece of code, so "why shouldn't those methods have access to the global"? Its harder to wrap your head around the idea that they will be running in different processes.
The Manager class is given to act as a proxy for data structures that can shuttle info back and forth for you between processes. What you will do is create a special dict and list from a manager, pass them into your methods, and operate on them locally.
Un-pickle-able data
For your specialize LORR object, you might need to create something like a proxy that can represent the pickable state of the instance.
Not super robust or tested much, but gives you the idea.
class LORRProxy(object):
def __init__(self, lorrObject=None):
self.instance = lorrObject
def __getstate__(self):
# how to get the state data out of a lorr instance
inst = self.instance
state = dict(
foo = inst.a,
bar = inst.b,
)
return state
def __setstate__(self, state):
# rebuilt a lorr instance from state
lorr = LORR.LORR()
lorr.a = state['foo']
lorr.b = state['bar']
self.instance = lorr
When using multiprocess, the only way to pass objects between processes is to use Queue or Pipe; globals are not shared. Objects must be pickleable, so multiprocess won't help you here.
You could also use a multiprocessing Array. This allows you to have a shared state between processes and is probably the closest thing to a global variable.
At the top of main, declare an Array. The first argument 'i' says it will be integers. The second argument gives the initial values:
shared_dataDV03 = multiprocessing.Array ('i', (0, 0)) #a shared array
Then pass this array to the process as an argument:
j = multiprocessing.Process(target=getDV03CclDrivers, args=('LORR',shared_dataDV03))
You have to receive the array argument in the function being called, and then you can modify it within the function:
def getDV03CclDrivers(lib,arr): # call global variable
arr[1]=1
arr[0]=0
The array is shared with the parent, so you can print out the values at the end in the parent:
print 'DV03', shared_dataDV03[:]
And it will show the changes:
DV03 [0, 1]
I use p.map() to spin off a number of processes to remote servers and print the results when they come back at unpredictable times:
Servers=[...]
from multiprocessing import Pool
p=Pool(len(Servers))
p.map(DoIndividualSummary, Servers)
This worked fine if DoIndividualSummary used print for the results, but the overall result was in unpredictable order, which made interpretation difficult. I tried a number of approaches to use global variables but ran into problems. Finally, I succeeded with sqlite3.
Before p.map(), open a sqlite connection and create a table:
import sqlite3
conn=sqlite3.connect('servers.db') # need conn for commit and close
db=conn.cursor()
try: db.execute('''drop table servers''')
except: pass
db.execute('''CREATE TABLE servers (server text, serverdetail text, readings text)''')
conn.commit()
Then, when returning from DoIndividualSummary(), save the results into the table:
db.execute('''INSERT INTO servers VALUES (?,?,?)''', (server,serverdetail,readings))
conn.commit()
return
After the map() statement, print the results:
db.execute('''select * from servers order by server''')
rows=db.fetchall()
for server,serverdetail,readings in rows: print serverdetail,readings
May seem like overkill but it was simpler for me than the recommended solutions.
I have some Tornado's coroutine related problem.
There is some python-model A, which have the abbility to execute some function. The function could be set from outside of the model. I can't change the model itself, but I can pass any function I want. I'm trying to teach it to work with Tornado's ioloop through my function, but I couldn't.
Here is the snippet:
import functools
import pprint
from tornado import gen
from tornado import ioloop
class A:
f = None
def execute(self):
return self.f()
pass
#gen.coroutine
def genlist():
raise gen.Return(range(1, 10))
#gen.coroutine
def some_work():
a = A()
a.f = functools.partial(
ioloop.IOLoop.instance().run_sync,
lambda: genlist())
print "a.f set"
raise gen.Return(a)
#gen.coroutine
def main():
a = yield some_work()
retval = a.execute()
raise gen.Return(retval)
if __name__ == "__main__":
pprint.pprint(ioloop.IOLoop.current().run_sync(main))
So the thing is that I set the function in one part of code, but execute it in the other part with the method of the model.
Now, Tornado 4.2.1 gave me "IOLoop is already running" but in Tornado 3.1.1 it works (but I don't know how exactly).
I know next things:
I can create new ioloop but I would like to use existent ioloop.
I can wrap genlist with some function which knows that genlist's result is Future, but I don't know, how to block execution until future's result will be set inside of synchronous function.
Also, I can't use result of a.execute() as an future object because a.execute() could be called from other parts of the code, i.e. it should return list instance.
So, my question is: is there any opportunity to execute asynchronous genlist from the synchronous model's method using current IOLoop?
You cannot restart the outer IOLoop here. You have three options:
Use asynchronous interfaces everywhere: change a.execute() and everything up to the top of the stack into coroutines. This is the usual pattern for Tornado-based applications; trying to straddle the synchronous and asynchronous worlds is difficult and it's better to stay on one side or the other.
Use run_sync() on a temporary IOLoop. This is what Tornado's synchronous tornado.httpclient.HTTPClient does, which makes it safe to call from within another IOLoop. However, if you do it this way the outer IOLoop remains blocked, so you have gained nothing by making genlist asynchronous.
Run a.execute on a separate thread and call back to the main IOLoop's thread for the inner function. If a.execute cannot be made asynchronous, this is the only way to avoid blocking the IOLoop while it is running.
executor = concurrent.futures.ThreadPoolExecutor(8)
#gen.coroutine
def some_work():
a = A()
def adapter():
# Convert the thread-unsafe tornado.concurrent.Future
# to a thread-safe concurrent.futures.Future.
# Note that everything including chain_future must happen
# on the IOLoop thread.
future = concurrent.futures.Future()
ioloop.IOLoop.instance().add_callback(
lambda: tornado.concurrent.chain_future(
genlist(), future)
return future.result()
a.f = adapter
print "a.f set"
raise gen.Return(a)
#gen.coroutine
def main():
a = yield some_work()
retval = yield executor.submit(a.execute)
raise gen.Return(retval)
Say, your function looks something like this:
#gen.coroutine
def foo():
# does slow things
or
#concurrent.run_on_executor
def bar(i=1):
# does slow things
You can run foo() like so:
from tornado.ioloop import IOLoop
loop = IOLoop.current()
loop.run_sync(foo)
You can run bar(..), or any coroutine that takes args, like so:
from functools import partial
from tornado.ioloop import IOLoop
loop = IOLoop.current()
f = partial(bar, i=100)
loop.run_sync(f)
I'm trying to share a composite structure through a multiprocessing manager but I felt in trouble with a "RuntimeError: maximum recursion depth exceeded" when trying to use just one of the Composite class methods.
The class is token from code.activestate and tested by me before inclusion into the manager.
When retrieving the class into a process and invoking its addChild() method I kept the RunTimeError, while outside the process it works.
The composite class inheritates from a SpecialDict class, that implements a ** ____getattr()____ **
method.
Could be possible that while calling addChild() the interpreter of python looks for a different ** ____getattr()____ ** because the right one is not proxied by the manager?
If so It's not clear to me the right way to make a proxy to that class/method
The following code reproduce exactly this condition:
1) this is the manager.py:
from multiprocessing.managers import BaseManager
from CompositeDict import *
class PlantPurchaser():
def __init__(self):
self.comp = CompositeDict('Comp')
def get_cp(self):
return self.comp
class Manager():
def __init__(self):
self.comp = QueuePurchaser().get_cp()
BaseManager.register('get_comp', callable=lambda:self.comp)
self.m = BaseManager(address=('127.0.0.1', 50000), authkey='abracadabra')
self.s = self.m.get_server()
self.s.serve_forever()
2) I want to use the composite into this consumer.py:
from multiprocessing.managers import BaseManager
class Consumer():
def __init__(self):
BaseManager.register('get_comp')
self.m = BaseManager(address=('127.0.0.1', 50000), authkey='abracadabra')
self.m.connect()
self.comp = self.m.get_comp()
ret = self.comp.addChild('consumer')
3) run all launching by a controller.py:
from multiprocessing import Process
class Controller():
def __init__(self):
for child in _run_children():
child.join()
def _run_children():
from manager import Manager
from consumer import Consumer as Consumer
procs = (
Process(target=Manager, name='Manager' ),
Process(target=Consumer, name='Consumer'),
)
for proc in procs:
proc.daemon = 1
proc.start()
return procs
c = Controller()
Take a look this related questions on how to do a proxy for CompositeDict() class
as suggested by AlberT.
The solution given by tgray works but cannot avoid race conditions
Is it possible there is a circular reference between the classes? For example, the outer class has a reference to the composite class, and the composite class has a reference back to the outer class.
The multiprocessing manager works well, but when you have large, complicated class structures, then you are likely to run into an error where a type/reference can not be serialized correctly. The other problem is that errors from multiprocessing manager are very cryptic. This makes debugging failure conditions even more difficult.
I think the problem is that you have to instruct the Manager on how to manage you object, which is not a standard python type.
In other worlds you have to create a proxy for you CompositeDict
You could look at this doc for an example: http://ruffus.googlecode.com/svn/trunk/doc/html/sharing_data_across_jobs_example.html
Python has a default maximum recursion depth of 1000 (or 999, I forget...). But you can change the default behavior thusly:
import sys
sys.setrecursionlimit(n)
Where n is the number of recursions you wish to allow.
Edit:
The above answer does nothing to solve the root cause of this problem (as pointed out in the comments). It only needs to be used if you are intentionally recursing more than 1000 times. If you are in an infinite loop (like in this problem), you will eventually hit whatever limit you set.
To address your actual problem, I re-wrote your code from scratch starting as simply as I could make it and built it up to what I believe is what you want:
import sys
from multiprocessing import Process
from multiprocessing.managers import BaseManager
from CompositDict import *
class Shared():
def __init__(self):
self.comp = CompositeDict('Comp')
def get_comp(self):
return self.comp
def set_comp(self, c):
self.comp = c
class Manager():
def __init__(self):
shared = Shared()
BaseManager.register('get_shared', callable=lambda:shared)
mgr = BaseManager(address=('127.0.0.1', 50000), authkey='abracadabra')
srv = mgr.get_server()
srv.serve_forever()
class Consumer():
def __init__(self, child_name):
BaseManager.register('get_shared')
mgr = BaseManager(address=('127.0.0.1', 50000), authkey='abracadabra')
mgr.connect()
shared = mgr.get_shared()
comp = shared.get_comp()
child = comp.addChild(child_name)
shared.set_comp(comp)
print comp
class Controller():
def __init__(self):
pass
def main(self):
m = Process(target=Manager, name='Manager')
m.daemon = True
m.start()
consumers = []
for i in xrange(3):
p = Process(target=Consumer, name='Consumer', args=('Consumer_' + str(i),))
p.daemon = True
consumers.append(p)
for c in consumers:
c.start()
for c in consumers:
c.join()
return 0
if __name__ == '__main__':
con = Controller()
sys.exit(con.main())
I did this all in one file, but you shouldn't have any trouble breaking it up.
I added a child_name argument to your consumer so that I could check that the CompositDict was getting updated.
Note that there is both a getter and a setter for your CompositDict object. When I only had a getter, each Consumer was overwriting the CompositDict when it added a child.
This is why I also changed your registered method to get_shared instead of get_comp, as you will want access to the setter as well as the getter within your Consumer class.
Also, I don't think you want to try joining your manager process, as it will "serve forever". If you look at the source for the BaseManager (./Lib/multiprocessing/managers.py:Line 144) you'll notice that the serve_forever() function puts you into an infinite loop that is only broken by KeyboardInterrupt or SystemExit.
Bottom line is that this code works without any recursive looping (as far as I can tell), but let me know if you still experience your error.