I'm playing with Python multiprocessing module to have a (read-only) array shared among multiple processes. My goal is to use multiprocessing.Array to allocate the data and then have my code forked (forkserver) so that each worker can read straight from the array to do their job.
While reading the Programming guidelines I got a bit confused.
It is first said:
Avoid shared state
As far as possible one should try to avoid shifting large amounts of
data between processes.
It is probably best to stick to using queues or pipes for
communication between processes rather than using the lower level
synchronization primitives.
And then, a couple of lines below:
Better to inherit than pickle/unpickle
When using the spawn or forkserver start methods many types from
multiprocessing need to be picklable so that child processes can use
them. However, one should generally avoid sending shared objects to
other processes using pipes or queues. Instead you should arrange the
program so that a process which needs access to a shared resource
created elsewhere can inherit it from an ancestor process.
As far as I understand, queues and pipes pickle objects. If so, aren't those two guidelines conflicting?
Thanks.
The second guideline is the one relevant to your use case.
The first is reminding you that this isn't threading where you manipulate shared data structures with locks (or atomic operations). If you use Manager.dict() (which is actually SyncManager.dict) for everything, every read and write has to access the manager's process, and you also need the synchronization typical of threaded programs (which itself may come at a higher cost from being cross-process).
The second guideline suggests inheriting shared, read-only objects via fork; in the forkserver case, this means you have to create such objects before the call to set_start_method, since all workers are children of a process created at that time.
The reports on the usability of such sharing are mixed at best, but if you can use a small number of any of the C-like array types (like numpy or the standard array module), you should see good performance (because the majority of pages will never be written to deal with reference counts). Note that you do not need multiprocessing.Array here (though it may work fine), since you do not need writes in one concurrent process to be visible in another.
Related
I need to read a large dataset (about 25GB of images) into memory and read it from multiple processes. None of the processes has to write, only read. All the processes are started using Python's multiprocessing module, so they have the same parent process. They train different models on the data and run independently of each other. The reason why I want to read it only one time rather than in each process is that the memory on the machine is limited.
I have tried using Redis, but unfortunately it is extremely slow when many processes read from it. Is there another option to do this?
Is it maybe somehow possible to have another process that only serves as a "get the image with ID x" function? What Python module would be suited for this? Otherwise, I was thinking about implementign a small webserver using werkzeug or Flask, but I am not sure if that would become my new bottleneck then...
Another possibility that came to my mind was to use threads instead of processes, but since Python is not really doing "real" multithreading, this would probably become my new bottleneck.
If you are on linux and the content is read-only, you can use the linux fork inheriting mechanism.
from mp documentation:
Better to inherit than pickle/unpickle
When using the spawn or forkserver start methods many types from
multiprocessing need to be picklable so that child processes can use
them. However, one should generally avoid sending shared objects to
other processes using pipes or queues. Instead you should arrange the
program so that a process which needs access to a shared resource
created elsewhere can inherit it from an ancestor process.
which means:
Before you fork your child processes, prepare your big data in a module level variable (global to all the functions).
Then in the same module, run your child with multiprocessing in 'fork' mode set_start_method('fork').
using this the sub-processes will see this variable without copying it. This happens due to linux forking mechanism that creates child processes with the same memory mapping as the parent (see "copy on write").
I'd suggest mmapping the files, that way they can be shared across multiple processes as well as getting swapped in/out as appropriate
the details of this would depend on what you mean by "25GB of images" and how these models want to access the images
the basic idea would be to preprocess the images into an appropriate format (e.g. one big 4D uint8 numpy array or maybe smaller ones, indicies could be (image, row, column, channel)) and save them in a format where they can be efficiently used by the models. see numpy.memmap for some examples of this
I'd suggest preprocessing files into a useful format "offline", i.e. not part of the model training but a seperate program that is run first. as this would probably take a while and you'd probably not want to do it every time
I was doing multiprocessing in python and hit a pickling error. Which makes me wonder why do we need to pickle the object in order to do multiprocessing? isn't fork() enough?
Edit: I kind of get why we need pickle to do interprocess communication, but that is just for the data you want to transfer right? why does the multiprocessing module also try to pickle stuff like functions etc?
Which makes me wonder why do we need to pickle the object in order to
do multiprocessing?
We don't need pickle, but we do need to communicate between processes, and pickle happens to be a very convenient, fast, and general serialization method for Python. Serialization is one way to communicate between processes. Memory sharing is the other. Unlike memory sharing, the processes don't even need to be on the same machine to communicate. For example, PySpark using serialization very heavily to communicate between executors (which are typically different machines).
Addendum: There are also issues with the GIL (Global Interpreter Lock) when sharing memory in Python (see comments below for detail).
isn't fork() enough?
Not if you want your processes to communicate and share data after they've forked. fork() clones the current memory space, but changes in one process won't be reflected in another after the fork (unless we explicitly share data, of course).
I kind of get why we need pickle to do interprocess communication, but
that is just for the data you want to transfer right? why does the
multiprocessing module also try to pickle stuff like functions etc?
Sometimes complex objects (i.e. "other stuff"? not totally clear on what you meant here) contain the data you want to manipulate, so we'll definitely want to be able to send that "other stuff".
Being able to send a function to another process is incredibly useful. You can create a bunch of child processes and then send them all a function to execute concurrently that you define later in your program. This is essentially the crux of PySpark (again a bit off topic, since PySpark isn't multiprocessing, but it feels strangely relevant).
There are some functional purists (mostly the LISP people) that make arguments that code and data are the same thing. So it's not much of a line to draw for some.
I'm trying to find a reasonable approach in Python for a real-time application, multiprocessing and large files.
A parent process spawn 2 or more child. The first child reads data, keep in memory, and the others process it in a pipeline fashion. The data should be organized into an object,sent to the following process, processed,sent, processed and so on.
Available methodologies such as Pipe, Queue, Managers seem not adequate due to overheads (serialization, etc).
Is there an adequate approach for this?
I've used Celery and Redis for real-time multiprocessing in high memory applications, but it really depends on what you're trying to accomplish.
The biggest benefits I've found in Celery over built-in multiprocessing tools (Pipe/Queue) are:
Low overhead. You call a function directly, no need to serialize data.
Scaling. Need to ramp up worker processes? Just add more workers.
Transparency. Easy to inspect tasks/workers and find bottlenecks.
For really squeezing out performance, ZMQ is my go to. A lot more work to set up and fine-tune, but it's as close to bare sockets as you can safely get.
Disclaimer: This is all anecdotal. It really comes down to what your specific needs are. I'd benchmark different options with sample data before you go down any path.
First, a suspicion that message-passing may be inadequate because of all the overhead is not a good reason to overcomplicate your program. It's a good reason to build a proof of concept and come up with some sample data and start testing. If you're spending 80% of your time pickling things or pushing stuff through queues, then yes, that's probably going to be a problem in your real life code—assuming the amount of work your proof of concept does is reasonably comparable to your real code. But if you're spending 98% of your time doing the real work, then there is no problem to solve. Message passing will be simpler, so just use it.
Also, even if you do identify a problem here, that doesn't mean that you have to abandon message passing; it may just be a problem with what's built in to multiprocessing. Technologies like 0MQ and Celery may have lower overhead than a simple queue. Even being more careful about what you send over the queue can make a huge difference.
But if message passing is out, the obvious alternative is data sharing. This is explained pretty well in the multiprocessing docs, along with the pros and cons of each.
Sharing state between processes describes the basics of how to do it. There are other alternatives, like using mmapped files of platform-specific shared memory APIs, but there's not much reason to do that over multiprocessing unless you need, e.g., persistent storage between runs.
There are two big problems to deal with, but both can be dealt with.
First, you can't share Python objects, only simple values. Python objects have internal references to each other all over the place, the garbage collector can't see references to objects in other processes' heaps, and so on. So multiprocessing.Value can only hold the same basic kinds of native values as array.array, and multiprocessing.Array can hold (as you'd guess by the name) 1D arrays of the same values, and that's it. For anything more complicated, if you can define it in terms of a ctypes.Structure, you can use https://docs.python.org/3/library/multiprocessing.html#module-multiprocessing.sharedctypes, but this still means that any references between objects have to be indirect. (For example, you often have to store indices into an array.) (Of course none of this is bad news if you're using NumPy, because you're probably already storing most of your data in NumPy arrays of simple values, which are sharable.)
Second, shared data are of course subject to race conditions. And, unlike multithreading within a single process, you can't rely on the GIL to help protect you here; there are multiple interpreters that can all be trying to modify the same data at the same time. So you have to use locks or conditions to protect things.
For multiprocessing pipeline check out MPipe.
For shared memory (specifically NumPy arrays) check out numpy-sharedmem.
I've used these to do high-performance realtime, parallel image processing (average accumulation and face detection using OpenCV) while squeezing out all available resources from a multi-core CPU system. Check out Sherlock if interested. Hope this helps.
One option is to use something like brain-plasma that maintains a shared-memory object namespace that is independent of the Python process or thread. Kind of like Redis but can be used with big objects and has a simple API, built on top of Apache Arrow.
$ pip install brain-plasma
# process 1
from brain_plasma import Brain
brain = Brain()
brain['myvar'] = 657
# process 2
from brain_plasma import Brain
brain = Brain()
brain['myvar']
# >>> 657
Python 3.8 now offers shared memory access between processes using multiprocessing.shared_memory. All you hand off between processes is a string that references the shared memory block. In the consuming process you get a memoryview object which supports slicing without copying the data like byte arrays do. If you are using numpy it can reference the memory block in an O(1) operation, allowing fast transfers of large blocks of numeric data. As far as I understand generic objects still need to be deserialized since a raw byte array is what's received by the consuming process.
Why does the multiprocessing package for python pickle objects to pass them between processes, i.e. to return results from different processes to the main interpreter process? This may be an incredibly naive question, but why can't process A say to process B "object x is at point y in memory, it's yours now" without having to perform the operation necessary to represent the object as a string.
multiprocessing runs jobs in different processes. Processes have their own independent memory spaces, and in general cannot share data through memory.
To make processes communicate, you need some sort of channel. One possible channel would be a "shared memory segment", which pretty much is what it sounds like. But it's more common to use "serialization". I haven't studied this issue extensively but my guess is that the shared memory solution is too tightly coupled; serialization lets processes communicate without letting one process cause a fault in the other.
When data sets are really large, and speed is critical, shared memory segments may be the best way to go. The main example I can think of is video frame buffer image data (for example, passed from a user-mode driver to the kernel or vice versa).
http://en.wikipedia.org/wiki/Shared_memory
http://en.wikipedia.org/wiki/Serialization
Linux, and other *NIX operating systems, provide a built-in mechanism for sharing data via serialization: "domain sockets" This should be quite fast.
http://en.wikipedia.org/wiki/Unix_domain_socket
Since Python has pickle that works well for serialization, multiprocessing uses that. pickle is a fast, binary format; it should be more efficient in general than a serialization format like XML or JSON. There are other binary serialization formats such as Google Protocol Buffers.
One good thing about using serialization: it's about the same to share the work within one computer (to use additional cores) or to share the work between multiple computers (to use multiple computers in a cluster). The serialization work is identical, and network sockets work about like domain sockets.
EDIT: #Mike McKerns said, in a comment below, that multiprocessing can use shared memory sometimes. I did a Google search and found this great discussion of it: Python multiprocessing shared memory
I'm creating a simple multiplayer game in python. I have split the processes up using the default thread module in python. However I noticed that the program still slows down with the speed of other threads. I tried using the multiprocessing module but not all of my objects can be pickled.
Is there an alternative to using the multiprocessing module for running simultaneous processes?
Here are your options:
MPI4PY:
http://code.google.com/p/mpi4py/
Celery:
http://www.celeryproject.org/
Pprocess:
http://www.boddie.org.uk/python/pprocess.html
Parallel Python(PP):
http://www.parallelpython.com/
You need to analyze why your program is slowing down when other threads do their work. Assuming that the threads are doing CPU-intensive work, the slowdown is consistent with threads being serialized by the global interpreter lock.
It is impossible to answer in detaile without knowing more about the nature of the work your threads are performing and of objects that must be shared in parallel. In general, you have two viable options:
Use processes, typically through the multiprocessing module. The typical reasons why objects are not picklable is because they contain unpicklable state such as closures, open file handles, or other system resources. But pickle allows objects to implement methods like __getstate__ or __reduce__ which identify object's state, using the state to rebuild the objects. If your objects are unpicklable because they are huge, then you might need to write a C extension that stores them in shared memory or a memory-mapped file, and pickle only a key that identifies them in the shared memory.
Use threads, finding ways to work around the GIL. If your computation is concentrated in several hot spots, you can move those hot spots to C, and release the GIL for the duration of the computation. For this to work, the computation must not refer to any Python objects, i.e. all data must be extracted from the objects while the GIL is held, and stored back into the Python world after the GIL has been reacquired.