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Source channel @githubtrending · Post #14747 · May 25
#python#deep_learning#intel#machine_learning#neural_network#pytorch#quantization Intel Extension for PyTorch boosts the speed of PyTorch on Intel hardware, including both CPUs and GPUs, by using special features like AVX-512, AMX, and XMX for faster calculations[5][2][4]. It supports many popular large language models (LLMs) such as Llama, Qwen, Phi, and DeepSeek, offering optimizations for different data types and easy GPU acceleration. This means you can run advanced AI models much faster and more efficiently on your Intel computer, with simple setup and support for both ready-made and custom models. https://github.com/intel/intel-extension-for-pytorch
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@djangoproject · Post #461 · 10/04/2017, 04:20 AM
http://jcalderone.livejournal.com/tag/sixty%20seconds Greetings, and welcome back to "Twisted Web in 60 Seconds". In the previous entry, back at the beginning of December, I promised to cover Twisted Web's proxying capabilities. For various reasons I've decided to dump that topic and cover something else instead. So, prepare to learn about #Twisted Web's CGI capabilities! #learn
@djangoproject · Post #458 · 10/04/2017, 04:02 AM
http://krondo.com/an-introduction-to-asynchronous-programming-and-twisted/ Twisted Introduction This multi-part series introduces #Asynchronous Programming and the Twisted networking framework. #Twisted is an event-driven networking engine written in #Python and licensed under the open source MIT license. Twisted runs on Python 2 and an ever growing subset also works with Python 3. #network#learn