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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
Search: #mongoengine
@djangoproject · Post #346 · 06/21/2017, 07:24 AM
http://mongoengine.org To get to grips with MongoEngine, there is extensive documentation, API references and a tutorial. You can find help by joining the MongoEngine Users mailing list or by chatting with other users on the #mongoengine IRC channel.
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@djangoproject · Post #364 · 07/04/2017, 03:25 AM
http://mongoengine.readthedocs.io/en/latest/index.html #MongoEngine is an Object-Document Mapper, written in #Python for working with #MongoDB. To install it, simply run #Django
@djangoproject · Post #373 · 07/05/2017, 06:39 AM
https://realpython.com/blog/python/introduction-to-mongodb-and-python/#.WMfv6BURLc4.linkedin #SQL vs #NoSQL #MongoDB #PyMongo #MongoEngine Conclusion
@djangoproject · Post #421 · 08/21/2017, 10:39 AM
https://alysivji.github.io/flask-part1-generating-html-pages-with-mongoengine-jinja2.html Generating HTML Pages from #MongoDB with #MongoEngine and #Jinja2 (Flask Part 1) Summary Overview of MongoDB Discussion of Object-Relational Mapping (#ORM) Use MongoEngine to get items out of MongoDB Render #HTML pages using Jinja2 Interact with #REST API to send emails with #Requests