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Source channel @githubtrending · Post #15007 · Jul 29

#c_lang You can find detailed guides for Linux kernel developers and users in the Documentation/ folder, which includes files in formats like HTML and PDF. To build these documents yourself, use commands like `make htmldocs` or `make pdfdocs`. The documentation covers important topics such as kernel building, running requirements, and upgrade issues. You can also view the latest formatted docs online. Additionally, the kernel source uses a special comment style called kernel-doc to embed documentation directly in the code, making it easier to understand functions and structures. This helps you learn, build, and maintain the Linux kernel more effectively. https://github.com/raspberrypi/linux

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djangoproject

@djangoproject · Post #274 · 03/18/2017, 01:48 AM

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https://github.com/riga/tfdeploy Google's TensorFlow framework is taking off big-time now that it's at a full 1.0 release. One common question about it: How can I make use of the models I train in TensorFlow without using TensorFlow itself? #Tfdeploy is a partial answer to that question. It exports a trained TensorFlow model to "a simple #NumPy-based callable," meaning the model can be used in Python with Tfdeploy and the the NumPy math-and-stats library as the only dependencies. Most of the operations you can perform in TensorFlow can also be performed in Tfdeploy, and you can extend the behaviors of the library by way of standard Python metaphors (such as overloading a class). Now the bad news: Tfdeploy doesn't support GPU acceleration, if only because NumPy doesn't do that. Tfdeploy's creator suggests using the gNumPy project as a possible replacement. #Machine_learning

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