![]() Even if they are mostly working on independent datasets, they normally have to go back to the master thread to report results and get new data to work on. # And finally, we join the process.True multiprocessing Python programs (as opposed to multi- threaded Python programs which have to deal with the dreaded GIL) are no different from those in any other language. Process = multiprocessing.Process(target=child_process) # down parameters to the function being executed as a child process. ![]() # we are using the `args` parameter now, this means that we can pass # `child_process` function to be executed. # Here we create a new instance of the Process class and assign our Creating a child process from your parentĪnd what about spawning different child processes inside your parent process? Well, to do that, we have the aid of the Process class from multiprocessing module, and it looks like this: #!/usr/bin/env python Which will produce the following output: ~]$ python /tmp/tmp.iuW2VAurGG/scratch.pyĪs you can see, any running Python script or program is a process of its own. Well, let's change that and do a very simple example of a process in Python: #!/usr/bin/env python A process executes a program you can have multiple processes executing the same program, but each process has its own copy of the program within its own address space and executes it independently of the other copies."īut what does that look like in Python? So far, we have managed to give some descriptions and references to what a process is, the difference between a process and a thread, but we haven't touched any code so far. ![]() Each process has its own address space and (usually) one thread of control. ![]() As explained in the GNU C Library: "Processes are the primitive units for allocation of system resources. In this section, we will try to give a better scope of what a process is, and how you can identify, use and manage processes within Python. We won't go over all classes and utilities from the multiprocessing module, but rather, we will focus on a very specific class, the process class. That module is specially designed to be the main point of interaction if a program needs to apply parallelism in its workflow. In general, the multiprocessing module offers a variety of other classes, functions and utilities that one could use to handle multiple processes executing during your program execution. If you want to learn more about the difference between a process and a thread, read this amazing article by Jong Hyuck Won, Process vs Thread: What’s the difference? The threading module is not the focus of this article, but in summary, the threading module will handle a small segment of code execution (lightweight and with shared memory), while the multiprocessing one will handle a program execution (heavier, and totally isolated). The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. What better way of describing what the module than to pull from the official documentation? Multiprocessing is a package that supports spawning processes using an API similar to the threading module. I will give you a quick overview with examples. In this article, we will learn how to work with a specific Python class from the multiprocessing module, the process class. ![]()
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