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Source channel @githubtrending · Post #14659 · May 1

#cplusplus#arm#convolution#deep_learning#embedded_devices#llm#machine_learning#ml#mnn#transformer#vulkan#winograd_algorithm MNN is a lightweight and efficient deep learning framework that helps run AI models on mobile devices and other small devices. It supports many types of AI models and can handle tasks like image recognition and language processing quickly and locally on your device. This means you can use AI features without needing to send data to the cloud, which improves privacy and speed. MNN is used in many apps, including those from Alibaba, and supports various platforms like Android and iOS. It also helps reduce the size of AI models, making them faster and more efficient. https://github.com/alibaba/MNN

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djangoproject

@djangoproject · Post #132 · 09/01/2016, 02:47 PM

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https://bit.ly/coroutines At Open Source Bridge and #PyGotham in 2015, and at SCALE14x, I demonstrated that you can code a Python 3 #async framework in under an hour. I start the demo by writing a callback-based async framework, built on non-blocking sockets and a simple event loop. Then I adapt the framework to use generator-based #coroutines, which are cleaner than callbacks but still more efficient than threads for async I/O.

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