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Facilitating cross-process programming.

Project description

remoteobj

Interacting with objects across processes.

This uses multiprocessing pipes to send messages and operations from the main process to the remote process and send back the return value.

Basically this lets do things like call .close() or .pause(), or .ready on an object that was sent to another process. But it also supports more than that!

What's included:

  • Proxy: remote proxy for communicating with forked objects. This was the initial purpose of this package
    class Blah:
        def __init__(self):
            self.remote = remoteobj.Proxy(self)
        def say_hi(self):
            print('hi from', mp.current_process().name)
        def run(self):  # remote process function
            with self.remote.listen_():
                while True:
                    ... # do other stuff
                    self.remote.process_requests()
    
    obj = Blah()
    mp.Process(target=obj.run).start()
    obj.remote.wait_until_listening()
    obj.remote.say_hi()
    # hi from Process-1
    ...
    
  • util.process: multiprocessing.Process but with fewer hurdles. Makes it more like you're writing normal python. Handles remote exceptions and return/yield values.
    def func(x):
        yield from range(x)
        raise ValueError('error from {}'.format(
           mp.current_process().name))
    
    try:
       with remoteobj.util.process(func, 4) as p:
           for i in p.result:
               print(i)
    except ValueError as e:
       print('woah!!', e)
    # prints 0, 1, 2, 3, woah!! error from Process-1
    
  • Except: context manager for capturing remote exceptions, assigning them to different groups, and sending them back to the main process
    exc = remoteobj.Except()
    
    def remote_func(exc):
        with exc(raises=False):
            try:
                with exc('init'):
                    do_init()
                with exc('work'):
                    do_work()
            finally:
                with exc('cleanup'):
                    do_cleanup()
    
    p = mp.Process(target=remote_func, args=(exc,))
    p.start()
    ...
    p.join()
    exc.raise_any('work')  # if none, its a noop
    exc.raise_any('cleanup')
    exc.raise_any()  # will raise the last caught exception
    
  • LocalExcept: the same context manager interface but without the inter-process communication
    exc = remoteobj.LocalExcept()
    
    with exc(raises=False):
        try:
            with exc('init'):
                do_init()
            do_other_stuff()
            with exc('work'):
                do_work()
        finally:
            with exc('cleanup'):
                do_cleanup()
    
    exc.raise_any('work')
    exc.raise_any('cleanup')
    exc.raise_any()
    

NOTE: This is still in alpha and the interface is still evolving (though things are starting to stabilize) so be sure to pin a version or stay up to date with changes. Also, let me know if you have thoughts/suggestions/requests! For info on how this works, see here

Install

pip install remoteobj

Full Example (Proxy)

Here we have an object that we want to run in a separate process. We want to be able to get/set the object's state as it's running so we wrap the object in a remote Proxy object which will run in the background of the remote process and execute the calls that we make in the main process.

import time
import remoteobj
import multiprocessing as mp

class Object:
    '''Remote object to do some counting.'''
    def __init__(self):
        self.remote = remoteobj.Proxy(self)

    count = 0
    on, stop = False, False
    def toggle(self, state=True):
        self.on = state

    def run(self):
        '''Remote process'''
        # starts thread to execute requests
        with self.remote.listen_(bg=True):
            while not self.stop and self.remote.listening_:
                if self.on:
                    self.count += 1
                time.sleep(0.1)

# start process

obj = Object()

p = mp.Process(target=obj.run)
p.start()
# make sure we've started up
# pass p so we aren't left hanging if the process dies
obj.remote.wait_until_listening(p)

# turn on, get starting count, count should be increasing

obj.remote.toggle(True)
x = obj.remote.count.get_()
time.sleep(1)
assert obj.remote.count.get_() > x  # 10 > 1

# turn off, count should stay the same

obj.remote.toggle(False)
x = obj.remote.count.get_()
time.sleep(1)
assert obj.remote.count.get_() == x  # 10 == 10

# turn back on, count should increase again

obj.remote.toggle(True)
x = obj.remote.count.__  # alias for get_()
time.sleep(1)
assert obj.remote.count.__ > x  # 20 > 10

# set the remote stop attribute to exit and join
obj.remote.stop = True  # you can set attrs too?? (゚ロ゚*)
p.join()

Basic Usage

Proxy

This explains the mechanics that are going on in more detail.

NOTE: to avoid name conflicts many of the proxy methods use underscore suffixes (i.e. get_()). This also applies to util.process arguments. See below.

import remoteobj

# building some arbitrary object
class Idk(list):
    something = 5
    another = []

obj = Idk()

# creating a remote proxy to interact with
# we want to make sure that the proxy gets
# sent along with it so we can handle remote
# requests.
r_obj = obj.remote = remoteobj.Proxy(obj)

# ... now send obj to mp.Process and start listener thread ...

# then meanwhile back in the *main* process

# call a method
r_obj.append(5)
# now the remote object has 5 appended to it
assert len(r_obj) == 1

# getting an attribute returns a proxy so you can chain
assert isinstance(r_obj.another.append, remoteobj.Proxy)

# calling will automatically resolve a proxy
r_obj.another.append(6)
# now another == [6]

# you can access keys, but we still allow chaining
# so they're proxies too
assert isinstance(r_obj[0], remoteobj.Proxy)
assert isinstance(r_obj.another[0], remoteobj.Proxy)

# to make the request and get the value, do

assert remoteobj.get( r_obj[0] ) == 5
# or more concisely
assert r_obj[0].__ == 5
# or if you prefer a less witch-y way
assert r_obj[0].get_() == 5

# you can even set keys and attributes on remote objects
r_obj[0] = 6
r_obj.something = 10


# len() is a special case, but in most cases, you can't
# just blindly pass a remote object to a function and
# expect it to work all the time.
assert str(r_obj) == <Remote <Idk object at ...> : (?)>
assert r_obj.passto(str) == <Idk object at ...>
# the first is the local instance, while the second is the
# remote instance. To pass an object to a function to execute
# on the remote side, use `passto` which will pickle the function
# reference.
# So you probably wouldn't want to pass an object method like that
# because it would pickle and unpickle the object instance which
# is most likely **not** what you want.
# but I'm not your mom.

NOTE: you cannot get/set attributes that begin with an underscore. All underscore attributes reference the proxy object itself.

Now on the remote side:

def run(obj):
    # indicate that we're listening
    with obj.remote:
        # do whatever nonsense you need
        value = 0
        while True:  # do nonsense
            for x in obj:
                value = x * obj.something
            for x, y in zip(obj, obj.another):
                value -= y / x * obj.something
            time.sleep(0.4)
            # this will make the requests
            obj.remote.process_requests()
    # after exiting, listening is set to false,
    # clients will fail or return their default
    # immediately because we have notified that
    # we will not be processing any more requests.

# Here's how to run the listener in a background thread

def run(obj):
    # start background thread to listen.
    with obj.remote.listen_():
        while obj.remote.listening_:
            ...
            # don't need to call obj.remote.process_requests()

Proxy Operations

These are the operations that a remote view can handle, which covers the main ways of accessing information from an object. Let me know if there are others that I'm missing.

NOTE: Any operation that says it returns a Proxy can be chained. If a remote method returns self, that will be caught and a remote proxy will be returned to allow for chaining as well.

  • __call__ (obj(*a, **kw)): retrieves return value.
    • to return a proxy instead, do either:
      • Proxy(obj, eager_proxy=True) to get all as proxies or
      • obj.method(_proxy=True) for a one-time thing
  • __getitem__ (obj[key]): returns proxy
  • __getattr__ (obj.attr): returns proxy
  • __setitem__ (obj[key] = value): evaluates
  • __setattr__ (obj.attr = value): evaluates
  • __delitem__ (del obj[key]): evaluates
  • __delattr__ (del obj.attr): evaluates
  • __contains__ (x in obj): evaluates
  • len (len(obj)): evaluates
  • passto (func(obj)): pass object to a function
    • e.g. obj.passto(str) is equivalent to str(obj)
    • you can also pass args: obj.passto(func, *a, **kw)

To resolve a proxy, you can do one of three equivalent styles:

  • remoteobj.get(obj.attr, default=False) - makes it clearer that obj.attr is being sent to the main process
  • obj.attr.get_(default='asdf') - access via chaining - convenient, somewhat clear
  • obj.attr.__ - an attempt at a minimalist interface, doesn't handle default value, not super clear. it's the easiest on the eyes once you know what it means, but I agree that the obscurity is a bit of an issue.

Enhanced Processes

Sometimes the multiprocessing.Process is a bit lacking in terms of interface so I wrote a lightweight wrapper that:

  • has a cleaner signature - process(func, *a, **kw) => func(*a, **kw)
    • other arguments, such as name, group have a underscore suffix (e.g. name_, group_, daemon_)
  • can be used as a context manager with process(...): which will join upon exiting.
  • pulls the process name from the function name by default
  • defaults to daemon_=True
  • will raise the remote exception (using remoteobj.Except())
  • sends back return and yield values via p.result
def remote_func(x, y):
    time.sleep(1)
    return 10

with remoteobj.util.process(remote_func, 5, 2) as p:
    assert p.name == 'remote_func-1'
    assert p.result == None  # called before return
    ... # do some other stuff

# now the process has joined
# and we can access the return value of the process!
assert p.result == 10


# now...
# wait for it....

def remote_func(x, y):
    for i in range(x, y):
        yield i

with remoteobj.util.process(remote_func, 5, 10) as p:
    # p.result returns a generator which will yield the values from the
    # remote side
    a_generator = p.result
    assert list(a_generator) == list(range(5, 10))

Sending Process Exceptions

Sending exceptions back from another process is always such a pain because you have to deal with all of the inter-process communication scaffolding, setting up queues, etc. and it can make your code messy.

The Except class lets you catch exceptions and add them to different named groups. This is useful if you need to separate out exceptions for setup errors, processing errors, or clean up errors.

How it works: Define an Except object. In your remote process use catch as a context manager and any matching exceptions raised in that context will be pickled with its traceback and appended to its queue.

# define an exception handler
catch = remoteobj.Except()
# or be more specific
catch = remoteobj.Except(ValueError, TypeError)

def remote_process(catch):
    with catch:
        raise ValueError('!!!')
    with catch('hi'):  # named exception contexts
        raise TypeError('hi')

p = mp.Process(target=remote_process, args=(catch,))
p.start()
p.join()
catch.raise_any('hi')  # will raise hi
# or
catch.raise_any()  # will raise any exception
# or
catch.raise_any(None)  # will raise any exception in the default context

Local Exceptions

We can use the same syntax and context mechanics without the inter-process communication to catch errors locally.

# define an exception handler
catch = remoteobj.LocalExcept(raises=True)

try:
    with catch:
        raise ValueError('!!!')
except:
    with catch('hi', raises=False):
        raise TypeError('hello')

catch.raise_any('hi')
catch.raise_any()

How Proxy works

We override basic python operators so that they return an object that represents a chain of operations (Proxy, View objects).

  • View objects represents a chain of operations - this is mainly an internal interface.
  • Proxy objects represents a chain of operations linked to an object.

When we go to resolve a chain of operations, we

  • first acquire a lock so that the listening state can't change and no other requests can be made at the same time.
  • check if the remote instance is listening
  • we send the set of operations over a pipe and then wait for the value to come out the other side
  • once it returns we check the return values, raise any exception, and return.

On the remote side, we:

  • poll the connection checking for op requests and once we find one:
  • acquire a write lock
  • evaluate the view on the proxy object
  • handle exceptions then place the result and exception in the pipe to be sent back

If there is no listening process, either a default value will be returned (if you provided one via get_(default=False)) or a RuntimeError will be raised.

It is useful to call proxy.wait_until_listening() while the remote process is starting up so that you don't get a RuntimeError due to the listener not having started up yet.

If a remote object gets called from the same process as the listening process then it will bypass the pipes and evaluate it directly. This means that if you use threads instead of processes, no data will be sent over pipes.

Advanced

import remoteobj

class A:
    def __init__(self):
        self.remote = remoteobj.Proxy(self)

    def asdf(self):
        return 5

class B(A):
    x = 0
    def asdf(self):
        return 6

    def chain(self):
        x += 1
        return self

obj = B()

Accessing super()

# call super method
assert obj.remote.asdf() == 6
assert obj.remote.super.asdf() == 6
# is equivalent to: super(type(obj), obj).asdf()

Remote methods that chain

A common pattern is to have a function return self so that you can chain methods together. But that doesn't work when you're sending an object back from another process because it'll get pickled and it'll no longer be the same object.

So there is a special-case - if the return value is self, it will mark it as such and on the other end, it will return the base proxy object.

# remote chaining
assert obj.remote.x.__ == 0  # check start value
assert obj.remote.chain().chain().chain().x.__ == 3

# equivalent to doing this locally
assert obj.x == 0
assert obj.chain().chain().chain().x == 3

Deadlocks

When dealing with concurrent programming, you always have to be concerned about dead-locking your program.

One area where deadlocking could be a problem is if a client process starts to request an operation as the listening process starts to clean up.

To prevent that, when the listening process is closing, it will either fulfill outstanding requests (default behavior) or refuse them (Proxy(fulfill_final=False)).

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