Hi, On Fri, 28 Sep 2018 17:07:33 -0400 Sean Harrington wrote:

In *short*, the implementation of the feature works as follows:

1. Exposes a kwarg on Pool.__init__ called `expect_initret`, that defaults to False. When set to True: 1. Capture the return value of the initializer kwarg of Pool 2. Pass this value to the function being applied, as a kwarg.

Again, in *short,* the motivation of the feature is to provide an explicit "flow of data" from parent process to worker process, and to avoid being *forced* to using the *global* keyword in initializer, or being *forced* to create global variables in the parent process.

Thanks for taking the time to explain your use case and write a proposal. My reactions to this are: 1. The proposed API is ugly. This basically allows you to pass an argument which changes with which arguments another function is later called... 2. A global variable seems like the adequate way to represent a process-global object (which is exactly your use case). 3. If you don't like globals, you could probably do something like lazily-initialize the resource when a function needing it is executed; this also avoids creating the resource if the child doesn't use it at all. Would that work for you? As a more general remark, I understand the desire to make the Pool object more flexible, but we can also not pile up features until it satisfies all use cases. As another general remark, concurrent.futures is IMHO the preferred API for the future, and where feature work should probably concentrate. Regards Antoine.

On Fri, Sep 28, 2018 at 2:11 PM Sean Harrington wrote:

kwarg on Pool.__init__ called `expect_initret`, that defaults to False. When set to True: Capture the return value of the initializer kwarg of Pool Pass this value to the function being applied, as a kwarg.

The parameter name you chose, "initret" is awkward, because nowhere else in Python does an initializer return a value. Initializers mutate an encapsulated scope. For a class __init__, that scope is an instance's attributes. For a subprocess managed by Pool, that encapsulated scope is its "globals". I'm using quotes to emphasize that these "globals" aren't shared. On Fri, Sep 28, 2018 at 4:39 PM Sean Harrington wrote:

On Fri, Sep 28, 2018 at 6:45 PM Antoine Pitrou wrote:

...

3. If you don't like globals, you could probably do something like lazily-initialize the resource when a function needing it is executed

if initializing the resource is expensive, we only want to do this ONE time per worker process.

We must have a different concept of "lazily-initialize". I understood Antoine's suggestion to be a one-time initialize per worker process. On Fri, Sep 28, 2018 at 4:39 PM Sean Harrington wrote:

My simple argument is that the developer should not be constrained to make the objects passed globally available in the process, as this MAY break encapsulation for large projects.

I could imagine someone switching from Pool to ThreadPool and getting into trouble, but in my mind using threads is caveat emptor. Are you worried about breaking encapsulation in a different scenario?

On Sat, Sep 29, 2018 at 6:24 AM Antoine Pitrou wrote:

Hi Sean,

On Fri, 28 Sep 2018 19:23:06 -0400 Sean Harrington wrote:

...

My simple argument is that the developer should not be constrained to make the objects passed globally available in the process, as this MAY break encapsulation for large projects.

IMHO, global variables don't break encapsulation if they remain private to the module where they actually play a role.

Of course, there are also global-like alternatives to globals, such as class attributes... The multiprocessing module itself uses globals (or quasi-globals) internally for various implementation details.

...

...

Yes, class attributes are a viable alternative. I've written about

this here. https://thelaziestprogrammer.com/python/multiprocessing-pool-a-global-soluti... Still, the argument is not against global variables, class attributes or any close cousins -- it is simply that developers shouldn't be forced to use these.

...

...

lazily-initialize the resource when a function needing it is executed; this also avoids creating the resource if the child doesn't use it at all. Would that work for you?

I have nothing against globals, my gripe is with being enforced to use

3. If you don't like globals, you could probably do something like them for every Pool use case. Further, if initializing the resource is expensive, we only want to do this ONE time per worker process.

That's what I meant with lazy initialization: initialize it if not already done, otherwise just use the already-initialized resource. It's a common pattern.

(you can view it as a 1-element cache if you prefer)

...

...

Sorry - I wasn't following your initial suggestion. This is a valid solution for ONE of the general use cases (where we initialize objects in each worker post-fork). However it fails for the other Pool use case of "initializing a big object in your parent, and passing to each worker, without using globals."

...

As a more general remark, I understand the desire to make the Pool

...

object more flexible, but we can also not pile up features until it satisfies all use cases.

I understand that this is a legitimate concern, but this is about API approachability. Python end-users of Pool are forced to declare a global from a lexical scope. Most Python end-users probably don't even know this is possible.

Hmm... We might have a disagreement on the target audience of the multiprocessing module. multiprocessing isn't very high-level, I would expect it to be used by experienced programmers who know how to mutate a global variable from a lexical scope.

...

...

It is one thing to MUTATE a global from a lexical scope. No gripes

there. The specific concept I'm referencing here, is "DECLARING a global variable, from within a lexical scope". This is not as a intuitive for most programmers.

For non-programmer end-users, such as data scientists, there are higher-level libraries such as Celery (http://www.celeryproject.org/) and Dask distributed (https://distributed.readthedocs.io/en/latest/). Perhaps it would be worth mentioning them in the documentation.

...

...

We likely do NOT have disagreements on the multiprocessing module. Multiprocessing is NOT high-level, I agree. But the beauty of the "Pool" API is that it gives non-programmer end-users (like data scientists) the ability to leverage multiple cores, without (in most cases) needing to know implementation details about multiprocessing. All they need to understand is the higher-order-function "map()", which is a very simple concept. (I even sound over-complicated myself calling it a "higher-order-function"...)

Regards

Antoine. _______________________________________________ Python-Dev mailing list Python-Dev@python.org https://mail.python.org/mailman/listinfo/python-dev Unsubscribe: https://mail.python.org/mailman/options/python-dev/seanharr11%40gmail.com

On Fri, Sep 28, 2018 at 9:27 PM Michael Selik wrote:

On Fri, Sep 28, 2018 at 2:11 PM Sean Harrington wrote:

...

kwarg on Pool.__init__ called `expect_initret`, that defaults to False. When set to True: Capture the return value of the initializer kwarg of Pool Pass this value to the function being applied, as a kwarg.

The parameter name you chose, "initret" is awkward, because nowhere else in Python does an initializer return a value. Initializers mutate an encapsulated scope. For a class __init__, that scope is an instance's attributes. For a subprocess managed by Pool, that encapsulated scope is its "globals". I'm using quotes to emphasize that these "globals" aren't shared.

...

Yes - if you bucket the "initializer" arg of Pool into the "Python initializers" then I see your point here. And yes initializer mutates the global scope of the worker subprocess. Again, my gripe is not with globals. I am looking for the ability to have a clear, explicit flow of data from

parent -> child process, without being constrained to using globals.

On Fri, Sep 28, 2018 at 4:39 PM Sean Harrington wrote:

...

On Fri, Sep 28, 2018 at 6:45 PM Antoine Pitrou wrote:

...

3. If you don't like globals, you could probably do something like lazily-initialize the resource when a function needing it is executed

if initializing the resource is expensive, we only want to do this ONE time per worker process.

We must have a different concept of "lazily-initialize". I understood Antoine's suggestion to be a one-time initialize per worker process.

...

See my response to Anotoine earlier. I missed the point made. This is a valid solution to the problem of "initializing objects after a worker has been forked", but fails to address the "create big object in parent, pass to each worker".

On Fri, Sep 28, 2018 at 4:39 PM Sean Harrington wrote:

...

My simple argument is that the developer should not be constrained to make the objects passed globally available in the process, as this MAY break encapsulation for large projects.

I could imagine someone switching from Pool to ThreadPool and getting into trouble, but in my mind using threads is caveat emptor. Are you worried about breaking encapsulation in a different scenario?

...

Without a specific example on-hand, you could imagine a tree of function calls that occur in the worker process (even newly created objects), that should not necessarily have access to objects passed from parent -> worker. In every case given the current implementation, they will.

On Sat, Sep 29, 2018 at 5:24 AM Sean Harrington wrote:

...

On Fri, Sep 28, 2018 at 4:39 PM Sean Harrington wrote:

...

My simple argument is that the developer should not be constrained to make the objects passed globally available in the process, as this MAY break encapsulation for large projects.

I could imagine someone switching from Pool to ThreadPool and getting into trouble, but in my mind using threads is caveat emptor. Are you worried about breaking encapsulation in a different scenario?

...

...

Without a specific example on-hand, you could imagine a tree of function calls that occur in the worker process (even newly created objects), that should not necessarily have access to objects passed from parent -> worker. In every case given the current implementation, they will.

Echoing Antoine: If you want some functions to not have access to a module's globals, you can put those functions in a different module. Note that multiprocessing already encapsulates each subprocesses' globals in essentially a separate namespace. Without a specific example, this discussion is going to go around in circles. You have a clear aversion to globals. Antoine and I do not. No one else seems to have found this conversation interesting enough to participate, yet.

You don't like using Pool.starmap and itertools.repeat or a comprehension that repeats an object? On Wed, Oct 3, 2018, 6:30 PM Sean Harrington wrote:

Hi guys -

The solution to "lazily initialize" an expensive object in the worker process (i.e. via @lru_cache) is a great solution (that I must admit I did not think of). Additionally, in the second use case of "*passing a large object to each worker process*", I also agree with your suggestion to "shelter functions in a different module to avoid exposure to globals" as a good solution if one is wary of globals.

That said, I still think "*passing a large object from parent process to worker processes*" should be easier when using Pool. Would either of you be open to something like the following?

def func(x, big_cache=None): return big_cache[x]

big_cache = { str(k): k for k in range(10000) }

ls = [ i for i in range(1000) ]

with Pool(func_kwargs={"big_cache": big_cache}) as pool:

pool.map(func, ls)

It's a much cleaner interface (which presumably requires a more difficult implementation) than my initial proposal. This also reads a lot better than the "initializer + global" recipe (clear flow of data), and is less constraining than the "define globals in parent" recipe. Most importantly, when taking sequential code and parallelizing via Pool.map, this does not force the user to re-implement "func" such that it consumes a global (rather than a kwarg). It allows "func" to be used elsewhere (i.e. in the parent process, from a different module, testing w/o globals, etc...)..

This would essentially be an efficient implementation of Pool.starmap(), where kwargs are static, and passed to each application of "func" over our iterable.

Thoughts?

On Sat, Sep 29, 2018 at 3:00 PM Michael Selik wrote:

...

...

...

On Fri, Sep 28, 2018 at 4:39 PM Sean Harrington wrote:

...

My simple argument is that the developer should not be constrained to make the objects passed globally available in the process, as this MAY break encapsulation for large projects.

I could imagine someone switching from Pool to ThreadPool and getting into trouble, but in my mind using threads is caveat emptor. Are you worried about breaking encapsulation in a different scenario?

...

...

Without a specific example on-hand, you could imagine a tree of function calls that occur in the worker process (even newly created objects), that should not necessarily have access to objects passed from

On Sat, Sep 29, 2018 at 5:24 AM Sean Harrington wrote: parent -> worker. In every case given the current implementation, they will.

Echoing Antoine: If you want some functions to not have access to a module's globals, you can put those functions in a different module. Note that multiprocessing already encapsulates each subprocesses' globals in essentially a separate namespace.

Without a specific example, this discussion is going to go around in circles. You have a clear aversion to globals. Antoine and I do not. No one else seems to have found this conversation interesting enough to participate, yet.

...

...

...

...

On Wed, Oct 3, 2018 at 6:30 PM, Sean Harrington wrote:

with Pool(func_kwargs={"big_cache": big_cache}) as pool: pool.map(func, ls)

I feel like it would be nicer to spell this: with Pool() as pool: pool.map(functools.partial(func, big_cache=big_cache), ls) And this might also solve your problem, if pool.map is clever enough to only send the function object once to each worker? -n -- Nathaniel J. Smith -- https://vorpus.org

On Fri, 12 Oct 2018 08:33:32 -0400 Sean Harrington wrote:

Hi Nathaniel - this if this solution can be made performant, than I would be more than satisfied.

I think this would require removing "func" from the "task tuple", and storing the "func" "once per worker" somewhere globally (maybe a class attribute set post-fork?).

This also has the beneficial outcome of increasing general performance of Pool.map and friends. I've seen MANY folks across the interwebs doing things like passing instance methods to map, resulting in "big" tasks, and slower-than-sequential parallelized code. Parallelizing "instance methods" by passing them to map, w/o needing to wrangle with staticmethods and globals, would be a GREAT feature! It'd just be as easy as:

Pool.map(self.func, ls)

What do you think about this idea? This is something I'd be able to take on, assuming I get a few core dev blessings...

Well, I'm not sure how it would work, so it's difficult to give an opinion. How do you plan to avoid passing "self"? By caching (by equality? by identity?)? Something else? But what happens if "self" changed value (in the case of a mutable object) in the parent? Do you keep using the stale version in the child? That would break compatibility... Regards Antoine.

Le 12/10/2018 à 15:17, Sean Harrington a écrit :

The implementation details need to be flushed out, but agnostic of these, do you believe this a valid solution to the initial problem? Do you also see it as a beneficial optimization to Pool, given that we don't need to store funcs/bound-methods/partials on the tasks themselves?

I'm not sure, TBH. I also think it may be better to leave this to higher levels (for example Dask will intelligently distribute data on workers and let you work with a kind of proxy object in the main process, transfering data only when necessary).

The latter concern about "what happens if `self` changed value in the parent" is the same concern as "what happens if `func` changes in the parent?" given the current implementation. This is an assumption that is currently made with Pool.map_async(func, ls). If "func" changes in the parent, there is no communication with the child. So one just needs to be aware that calling "map_async(self.func, ls)" while the state of "self" is changing, will not communicate changes to each worker. The state is frozen when Pool.map is called, just as is the case now.

If you cache "self" between pool.map calls, then the question is not "what happens if self changes *during* a map() call" but "what happens if self changes *between* two map() calls"? While the former is intuitively undefined, current users would expect the latter to have a clear answer, which is: the latest version of self when map() is called is taken into account. Regards Antoine.

On Fri, 12 Oct 2018 09:42:50 -0400 Sean Harrington wrote:

I would contend that this is much more granular than Dask - this is just an optimization of Pool.map() to avoid redundantly passing the same `func` repeatedly, once per task, to each worker, with the primary goal of eliminating redundant serialization of large-memory-footprinted Callables. This is a different use case than Dask - I don't intend to approach the shared memory or distributed computing realms.

And the second call to Pool.map would update the cached "self" as a part of its initialization workflow, s.t. "the latest version of self when map() is called is taken into account".

I still don't understand how that works. If you "updated the cached self", then surely you must transmit it to the child, right? Regards Antoine.

Le 12/10/2018 à 16:49, Sean Harrington a écrit :

Yes - “func” (and “self” which func is bound to) would be copied to each child worker process, where they are stored and applied to each element of the iterable being mapped over.

Only if it has changed, then, right? I suspect that would work, but it will break compatibility in some cases (think of a mutable object that hasn't defined equality - so it defaults to identity). It's also introducing a complication in the API which people didn't have to think of before. The fact that you're doing all this in order to eschew global variables for global resources doesn't warm me much to the idea. Unless other core developers are enthusiastic I'm not willing to integrate such a change. Regards Antoine.

On Fri, Oct 12, 2018 at 10:41 AM Antoine Pitrou mailto:solipsis@pitrou.net> wrote:

On Fri, 12 Oct 2018 09:42:50 -0400 Sean Harrington mailto:seanharr11@gmail.com> wrote: > I would contend that this is much more granular than Dask - this is just an > optimization of Pool.map() to avoid redundantly passing the same `func` > repeatedly, once per task, to each worker, with the primary goal of > eliminating redundant serialization of large-memory-footprinted Callables. > This is a different use case than Dask - I don't intend to approach the > shared memory or distributed computing realms. > > And the second call to Pool.map would update the cached "self" as a part of > its initialization workflow, s.t. "the latest version of self when map() is > called is taken into account".

I still don't understand how that works.  If you "updated the cached self", then surely you must transmit it to the child, right?

Regards

Antoine.

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@Nataniel this is what I am suggesting as well. No cacheing - just storing the `fn` on each worker, rather than pickling it for each item in our iterable. As long as we store the `fn` post-fork on the worker process (perhaps as global), subsequent calls to Pool.map shouldn't be effected (referencing Antoine's & Michael's points that "multiprocessing encapsulates each subprocesses globals in a separate namespace"). @Antoine - I'm making an effort to take everything you've said into consideration here. My initial PR and talk https://www.youtube.com/watch?v=DH0JVSXvxu0 was intended to shed light on a couple of pitfalls that I often see Python end-users encounter with Pool. Moving beyond my naive first attempt, and the onslaught of deserved criticism, it seems that we have an opportunity here: No changes to the interface, just an optimization to reduce the frequency of pickling. Raymond Hettinger may also be interested in this optimization, as he speaks (with great analogies) about different ways you can misuse concurrency in Python https://www.youtube.com/watch?v=9zinZmE3Ogk. This would address one of the pitfalls that he outlines: the "size of the serialized/deserialized data". Is this an optimization that either of you would be willing to review, and accept, if I find there is a *reasonable way* to implement it? On Fri, Oct 12, 2018 at 3:40 PM Nathaniel Smith wrote:

On Fri, Oct 12, 2018, 06:09 Antoine Pitrou wrote:

...

On Fri, 12 Oct 2018 08:33:32 -0400 Sean Harrington wrote:

...

Hi Nathaniel - this if this solution can be made performant, than I would be more than satisfied.

I think this would require removing "func" from the "task tuple", and storing the "func" "once per worker" somewhere globally (maybe a class attribute set post-fork?).

This also has the beneficial outcome of increasing general performance of Pool.map and friends. I've seen MANY folks across the interwebs doing things like passing instance methods to map, resulting in "big" tasks, and slower-than-sequential parallelized code. Parallelizing "instance methods" by passing them to map, w/o needing to wrangle with staticmethods and globals, would be a GREAT feature! It'd just be as easy as:

Pool.map(self.func, ls)

What do you think about this idea? This is something I'd be able to take on, assuming I get a few core dev blessings...

Well, I'm not sure how it would work, so it's difficult to give an opinion. How do you plan to avoid passing "self"? By caching (by equality? by identity?)? Something else? But what happens if "self" changed value (in the case of a mutable object) in the parent? Do you keep using the stale version in the child? That would break compatibility...

I was just suggesting that within a single call to Pool.map, it would be reasonable optimization to only send the fn once to each worker. So e.g. if you have 5 workers and 1000 items, you'd only pickle fn 5 times, rather than 1000 times like we do now. I wouldn't want to get any fancier than that with caching data between different map calls or anything.

Of course even this may turn out to be too complicated to implement in a reasonable way, since it would require managing some extra state on the workers. But semantically it would be purely an optimization of current semantics.

-n

...

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Is your concern something like the following? with Pool(8) as p: gen = p.imap_unordered(func, ls) first_elem = next(gen) p.apply_async(long_func, x) remaining_elems = [elem for elem in gen] ...here, if we store "func" on each worker Process as a global, and execute this pattern above, we will likely alter state of one of the worker processes s.t. it stores "long_func" in place of the initial "func". So yes, this could break things. *A potential solution*: Replace "func" in the task tuple with an identifier (maybe, *perhaps

naively*, func.__qualname__), and store the "identifier => func map" somewhere globally accessible, maybe as a class attribute on Pool. On any call to Pool.map, Pool.apply, etc... this map is updated. Then, in the worker process, as each task is processed, we use the "func identifier" on the task to recover the globally mapped 'func', and apply it.

This would avoid the weird stateful bug above. We could also do something slightly different to this if folks are averse to the "Pool class-attribute func map" (i.e. averse to globals), and store this map as an Instance Attribute on the Pool object, and wrap the "initializer" func to make the map globally available in the worker via the "global" keyword. One note: this isn't a "cache", it's just a global map which has its keys & values updated *blindly* with every call to Pool.. It serves as a way to bypass repeated serialization of functions in Pool, which can be large when bound to big objects (like large class instances, or functools.partial objects). On Tue, Oct 16, 2018 at 9:27 AM Michael Selik wrote:

Would this change the other pool method behavior in some way if the user, for whatever reason, mixed techniques?

imap_unordered will only block when nexting the generator. If the user mingles nexting that generator with, say, apply_async, could the change you're proposing have some side-effect?

On Tue, Oct 16, 2018, 5:09 AM Sean Harrington wrote:

...

@Nataniel this is what I am suggesting as well. No cacheing - just storing the `fn` on each worker, rather than pickling it for each item in our iterable.

As long as we store the `fn` post-fork on the worker process (perhaps as global), subsequent calls to Pool.map shouldn't be effected (referencing Antoine's & Michael's points that "multiprocessing encapsulates each subprocesses globals in a separate namespace").

@Antoine - I'm making an effort to take everything you've said into consideration here. My initial PR and talk https://www.youtube.com/watch?v=DH0JVSXvxu0 was intended to shed light on a couple of pitfalls that I often see Python end-users encounter with Pool. Moving beyond my naive first attempt, and the onslaught of deserved criticism, it seems that we have an opportunity here: No changes to the interface, just an optimization to reduce the frequency of pickling.

Raymond Hettinger may also be interested in this optimization, as he speaks (with great analogies) about different ways you can misuse concurrency in Python https://www.youtube.com/watch?v=9zinZmE3Ogk. This would address one of the pitfalls that he outlines: the "size of the serialized/deserialized data".

Is this an optimization that either of you would be willing to review, and accept, if I find there is a *reasonable way* to implement it?

On Fri, Oct 12, 2018 at 3:40 PM Nathaniel Smith wrote:

...

On Fri, Oct 12, 2018, 06:09 Antoine Pitrou wrote:

...

...

Hi Nathaniel - this if this solution can be made performant, than I would be more than satisfied.

I think this would require removing "func" from the "task tuple", and storing the "func" "once per worker" somewhere globally (maybe a class attribute set post-fork?).

This also has the beneficial outcome of increasing general

On Fri, 12 Oct 2018 08:33:32 -0400 Sean Harrington wrote: performance of

...

Pool.map and friends. I've seen MANY folks across the interwebs doing things like passing instance methods to map, resulting in "big" tasks, and slower-than-sequential parallelized code. Parallelizing "instance methods" by passing them to map, w/o needing to wrangle with staticmethods and globals, would be a GREAT feature! It'd just be as easy as:

Pool.map(self.func, ls)

What do you think about this idea? This is something I'd be able to take on, assuming I get a few core dev blessings...

Well, I'm not sure how it would work, so it's difficult to give an opinion. How do you plan to avoid passing "self"? By caching (by equality? by identity?)? Something else? But what happens if "self" changed value (in the case of a mutable object) in the parent? Do you keep using the stale version in the child? That would break compatibility...

I was just suggesting that within a single call to Pool.map, it would be reasonable optimization to only send the fn once to each worker. So e.g. if you have 5 workers and 1000 items, you'd only pickle fn 5 times, rather than 1000 times like we do now. I wouldn't want to get any fancier than that with caching data between different map calls or anything.

Of course even this may turn out to be too complicated to implement in a reasonable way, since it would require managing some extra state on the workers. But semantically it would be purely an optimization of current semantics.

-n

...

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You have correctly identified the summary of my intentions, and I agree with your reasoning & concern - however there is a somewhat reasonable answer as to why this optimization has never been implemented: In Pool, the `task` tuple consists of (result_job, func, (x,), {}) . This is the object that is serialized/deserialized b/t processes. The only thing we really care about here is the tuple `(x,)`, confusingly, not `func` (func is ACTUALLY either mapstar() or starmapstar(), which is called with (x,) as its *args). Our element of interest is `(x,)` - a tuple of (func, iterable). Because we need to temper the size of the `iterable` bundled in each task, to avoid de/serialization slowness, we usually end up with multiple tasks per worker, and thus multiple `func`s per worker. Thus, this is really only an optimization in the case of really big functions/closures/partials (or REALLY big iterables with an unreasonably small chunksize passed to map()). The most common use case comes up when passing instance methods (of really big objects!) to Pool.map(). This post https://thelaziestprogrammer.com/python/a-multiprocessing-pool-pickle#stuck-... may color in the above with more details. Further, let me pivot on my idea of __qualname__...we can use the `id` of `func` as the cache key to address your concern, and store this `id` on the `task` tuple (i.e. an integer in-lieu of the `func` previously stored there). On Thu, Oct 18, 2018 at 12:49 AM Michael Selik wrote:

If imap_unordered is currently re-pickling and sending func each time it's called on the worker, I have to suspect there was some reason to do that and not cache it after the first call. Rather than assuming that's an opportunity for an optimization, I'd want to be certain it won't have edge case negative effects.

On Tue, Oct 16, 2018 at 2:53 PM Sean Harrington wrote:

...

Is your concern something like the following?

with Pool(8) as p: gen = p.imap_unordered(func, ls) first_elem = next(gen) p.apply_async(long_func, x) remaining_elems = [elem for elem in gen]

My concern was passing the same function (or a function with the same qualname). You're suggesting caching functions and identifying them by qualname to avoid re-pickling a large stateful object that's shoved into the function's defaults or closure. Is that a correct summary?

If so, how would the function cache distinguish between two functions with the same name? Would it need to examine the defaults and closure as well? If so, that means it's pickling the second one anyway, so there's no efficiency gain.

In [1]: def foo(a): ...: def bar(): ...: print(a) ...: return bar In [2]: f = foo(1) In [3]: g = foo(2) In [4]: f Out[4]: In [5]: g Out[5]:

If we say pool.apply_async(f) and pool.apply_async(g), would you want the latter one to avoid serialization, letting the worker make a second call with the first function object?

One idea would be for the Pool method to generate a uuid and slap it on the function as an attribute. If a function being passed in doesn't have one, generate one. If it already has one, just pass that instead of pickling. The child process will keep a cache mapping uuids to functions. I'm still worried about unintended consequences. On Thu, Oct 18, 2018 at 9:00 AM Michael Selik wrote:

On Thu, Oct 18, 2018 at 8:35 AM Sean Harrington wrote:

...

The most common use case comes up when passing instance methods (of really big objects!) to Pool.map().

This reminds me of that old joke: "A patient says to the doctor, 'Doctor, it hurts when I ...!' The doctor replies, 'Well, don't do that.'"

Further, let me pivot on my idea of __qualname__...we can use the `id` of

...

`func` as the cache key to address your concern, and store this `id` on the `task` tuple (i.e. an integer in-lieu of the `func` previously stored there).

Possible. Does the Pool keep a reference to the passed function in the main process? If not, couldn't the garbage collector free that memory location and a new function could replace it? Then it could have the same qualname and id in CPython. Edge case, for sure. Worse, it'd be hard to reproduce as it'd be dependent on the vagaries of memory allocation.

Hello, I have been working on the concurent.futures module lately and I think this optimization should be avoided in the context of python Pools. This is an interesting idea, however its implementation will bring many complicated issues as it breaks the basic paradigm of a Pool: the tasks are independent and you don't know which worker is going to run which task. The function is serialized with each task because of this paradigm. This ensure that any worker picking the task will be able to perform it independently from the tasks it has run before, given that it as been initialized correctly at the beginning. This makes it simple to run each task. As the Pool comes with no scheduler, with your idea, you would need a synchronization step to send the function to all workers before you can launch your task. But if there is already one worker performing a long running task, does the Pool wait for it to be done before it sends the function? If the Pool doesn't wait, how does it ensure that this worker will be able to get the definition of the function before running it? Also, the multiprocessing.Pool has some features where a worker can shut itself down after a given number of tasks or a timeout. How does it ensure that the new worker will have the definition of the function? It is unsafe to try such a feature (sending only once an object) anywhere else than in the initializer which is guaranteed to be run once per worker. On the other hand, you mentioned an interesting point being that making globals available in the workers could be made simpler. A possible solution would be to add a "globals" argument in the Pool which would instanciate global variables in the workers. I have no specific idea but on the implementation of such features but it would be safer as it would be an initialization feature. Regards, Thomas Moreau On Thu, Oct 18, 2018, 22:20 Chris Jerdonek wrote:

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On Thu, Oct 18, 2018 at 8:35 AM Sean Harrington wrote:

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Further, let me pivot on my idea of __qualname__...we can use the `id` of `func` as the cache key to address your concern, and store this `id` on

On Thu, Oct 18, 2018 at 9:11 AM Michael Selik wrote: the `task` tuple (i.e. an integer in-lieu of the `func` previously stored there).

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Possible. Does the Pool keep a reference to the passed function in the

main process? If not, couldn't the garbage collector free that memory location and a new function could replace it? Then it could have the same qualname and id in CPython. Edge case, for sure. Worse, it'd be hard to reproduce as it'd be dependent on the vagaries of memory allocation.

I'm not following this thread closely, but I just wanted to point out that __qualname__ won't necessarily be an attribute of the object if the API accepts any callable. (I happen to be following an issue on the tracker where this came up.)

--Chris _______________________________________________ Python-Dev mailing list Python-Dev@python.org https://mail.python.org/mailman/listinfo/python-dev Unsubscribe: https://mail.python.org/mailman/options/python-dev/thomas.moreau.2010%40gmai...

On Fri, Oct 19, 2018 at 7:32 AM Joni Orponen wrote:

On Fri, Oct 19, 2018 at 9:09 AM Thomas Moreau < thomas.moreau.2010@gmail.com> wrote:

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Hello,

I have been working on the concurent.futures module lately and I think this optimization should be avoided in the context of python Pools.

This is an interesting idea, however its implementation will bring many complicated issues as it breaks the basic paradigm of a Pool: the tasks are independent and you don't know which worker is going to run which task.

The function is serialized with each task because of this paradigm. This ensure that any worker picking the task will be able to perform it independently from the tasks it has run before, given that it as been initialized correctly at the beginning. This makes it simple to run each task.

I would not mind if there would be a subtype of Pool where you can only apply one kind of task to. This is a very common use mode.

Though the question there is 'should this live in Python itself'? I'd be

fine with a package on PyPi.

Thomas makes a good point: despite the common user mode of calling Pool.map() once, blocking, and returning, the need for serialization of functions within tasks arises when calling Pool.map() (and friends) while workers are still running (i.e. there was a previous call to Pool.async_map()). However this is an uncommon user mode, as Joni points out. The most common user mode is that your Pool workers are only ever executing one type of task at a given time. This opens up optimization opportunities, so long as we store state on the subclassed Pool object of whether or not a SingleTask is running, or has been completed(SingleTaskPool?), to prevent the user from getting in this funky state above. I would rather see this included in the multiprocessing stdlib, as it seemingly will not introduce a lot of new code, would benefit from existing tests. Most importantly it optimizes (in my opinion) the most common user mode of Pool.

As the Pool comes with no scheduler, with your idea, you would need a

...

synchronization step to send the function to all workers before you can launch your task. But if there is already one worker performing a long running task, does the Pool wait for it to be done before it sends the function? If the Pool doesn't wait, how does it ensure that this worker will be able to get the definition of the function before running it? Also, the multiprocessing.Pool has some features where a worker can shut itself down after a given number of tasks or a timeout. How does it ensure that the new worker will have the definition of the function? It is unsafe to try such a feature (sending only once an object) anywhere else than in the initializer which is guaranteed to be run once per worker.

On the other hand, you mentioned an interesting point being that making globals available in the workers could be made simpler. A possible solution would be to add a "globals" argument in the Pool which would instanciate global variables in the workers. I have no specific idea but on the implementation of such features but it would be safer as it would be an initialization feature.

Would this also mean one could use a Pool in a context where threading is used? Currently using threading side effects unpicklables into the globals.

Also being able to pass in globals=None would be optimal for a lot of use cases.

We could do this - but we can easily get the same behavior by declaring a "global" in "initializer" (albeit a pattern which I do not like). I like the idea to extend the Pool class a bit more, but this is also my opinion.

-- Joni Orponen _______________________________________________ Python-Dev mailing list Python-Dev@python.org https://mail.python.org/mailman/listinfo/python-dev Unsubscribe: https://mail.python.org/mailman/options/python-dev/seanharr11%40gmail.com

Michael - the initializer/globals pattern still might be necessary if you need to create an object AFTER a worker process has been instantiated (i.e. a database connection). Further, the user may want to access all of the niceties of Pool, like imap, imap_unordered, etc. The goal (IMO) would be to preserve an interface which many Python users have grown accustomed to, and to allow them to access this optimization out-of-the-bag. Having talked to folks at the Boston Python meetup, folks on my dev team, and perusing stack overflow, this "instance method parallelization" is a pretty common pattern that is often times a negative return on investment for the developer, due to the implicit implementation detail of pickling the function (and object) once per task. Is anyone open to reviewing a PR concerning this optimization of Pool, delivered as a subclass? This feature restricts the number of unique tasks being executed by workers at once to 1, while allowing aggressive subprocess-level function cacheing to prevent repeated serialization/deserialization of large functions/closures. The use case is s.t. the user only ever needs 1 call to Pool.map(func, ls) (or friends) executing at once, when `func` has a non-trivial memory footprint. On Fri, Oct 19, 2018 at 4:06 PM Michael Selik wrote:

On Fri, Oct 19, 2018 at 5:01 AM Sean Harrington wrote:

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I like the idea to extend the Pool class [to optimize the case when only one function is passed to the workers].

Why would this keep the same interface as the Pool class? If its workers are restricted to calling only one function, that should be passed into the Pool constructor. The map and apply methods would then only receive that function's args and not the function itself. You're also trying to avoid the initializer/globals pattern, so you could eliminate that parameter from the Pool constructor. In fact, it sounds more like you'd want a function than a class. You can call it "procmap" or similar. That's code I've written more than once.

results = poolmap(func, iterable, processes=cpu_count())

The nuance is that, since there's no explicit context manager, you'll want to ensure the pool is shut down after all the tasks are finished, even if the results generator hasn't been fully consumed.

On Mon, Oct 22, 2018 at 2:01 PM Michael Selik wrote:

This thread seems more appropriate for python-ideas than python-dev.

On Mon, Oct 22, 2018 at 5:28 AM Sean Harrington wrote:

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Michael - the initializer/globals pattern still might be necessary if you need to create an object AFTER a worker process has been instantiated (i.e. a database connection).

You said you wanted to avoid the initializer/globals pattern and have such things as database connections in the defaults or closure of the task-function, or the bound instance, no? Did I misunderstand?

Further, the user may want to access all of the niceties of Pool, like

...

imap, imap_unordered, etc. The goal (IMO) would be to preserve an interface which many Python users have grown accustomed to, and to allow them to access this optimization out-of-the-bag.

You just said that the dominant use-case was mapping a single task-function. It sounds like we're talking past each other in some way. It'll help to have a concrete example of a case that satisfies all the characteristics you've described: (1) no globals used for communication between initializer and task-functions; (2) single task-function, mapped once; (3) an instance-method as task-function, causing a large serialization burden; and (4) did I miss anything?

You're right, it's really only use cases (2) and (3) that define this spec. However, the case for subclassing really boils down to the "free" inheritance of the public methods of Pool (map, imap, imap_unordered, etc...). Why exclude these (by implementing "procmap()") if we get this great return with such little investment?

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Having talked to folks at the Boston Python meetup, folks on my dev team, and perusing stack overflow, this "instance method parallelization" is a pretty common pattern that is often times a negative return on investment for the developer, due to the implicit implementation detail of pickling the function (and object) once per task.

I believe you.

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Is anyone open to reviewing a PR concerning this optimization of Pool, delivered as a subclass? This feature restricts the number of unique tasks being executed by workers at once to 1, while allowing aggressive subprocess-level function cacheing to prevent repeated serialization/deserialization of large functions/closures. The use case is s.t. the user only ever needs 1 call to Pool.map(func, ls) (or friends) executing at once, when `func` has a non-trivial memory footprint.

You're quite eager to have this PR merged. I understand that. However, it's reasonable to take some time to discuss the design of what you're proposing. You don't need it in the stdlib to get your own work done, nor to share it with others.

I am just eager to solve this problem, which is likely evident, given that this is the 3rd different implementation discussed in detail since my initial PR. If the group consensus is that this is best implemented via "procmap" function in github gist, then the idea will live there, and likely have a lonely life there. I contend that multiprocessing.Pool is used most frequently with a single task. I am proposing a feature that enforces this invariant, optimizes task memory-footprints & thus serialization time, and preserves the well-established interface to Pool through subclassing.