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Source channel @githubtrending · Post #15554 · Mar 12

#cplusplus LiteRT is Google's free framework for running fast machine learning and generative AI on phones, computers, and web without cloud help. It uses GPU and NPU for up to 2x speed boosts, zero-copy data handling, and async execution on Android, iOS, Linux, and more, plus easy PyTorch model conversion. You benefit by building quick, private apps like real-time image editing or chatbots that work offline on everyday devices, saving battery and boosting performance. https://github.com/google-ai-edge/LiteRT

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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,