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Source channel @githubtrending · Post #14673 · May 5

#go#ai#assistant#cli#kubernetes **kubectl-ai** is a tool that helps manage Kubernetes using AI. It lets you ask questions or give commands in simple language, and it will execute the right Kubernetes actions for you. This makes it easier to manage your Kubernetes cluster without needing to remember complex commands. You can use it to check app status, create deployments, or troubleshoot issues, all by just typing what you want to do. It supports various AI models and can be used interactively or with other Unix commands, making it a powerful assistant for Kubernetes users. https://github.com/GoogleCloudPlatform/kubectl-ai

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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,