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Source channel @githubtrending · Post #15048 · Aug 12

#typescript The GitHub Actions Checkout action lets you download your repository code into the workflow environment so your automation can access it. It supports fetching specific branches, tags, or commits, and can fetch full history or just the latest commit. You can use tokens or SSH keys for authenticated access, enabling secure git commands during workflows. It also supports sparse checkouts to fetch only parts of the repo, and can handle submodules. This action simplifies automating tasks like testing, building, or deploying code by ensuring your workflow has the right code checked out efficiently and securely. https://github.com/actions/checkout

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djangoproject

@djangoproject · Post #118 · 08/08/2016, 11:44 AM

https://docs.python.org/3/library/multiprocessing.html multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows. The #multiprocessing module also introduces #APIs which do not have analogs in the #threading#module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data #parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool,