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Source channel @githubtrending · Post #14728 · May 20

#typescript#android#appwrite#backend#backend_as_a_service#docker#firebase#flutter#hacktoberfest#hosting#ios#javascript#nextjs#react#react_native#reactnative#self_hosted#selfhosted#serverless#swift#web Appwrite is a backend platform that helps you build web, mobile, and Flutter apps quickly and easily. It handles complex tasks like user authentication, database management, file storage, and more, so you don’t have to build these from scratch. Appwrite is open source, secure, and works with many programming languages and frameworks. You can use it in the cloud or host it yourself using Docker. The main benefit is that it saves you time and effort, letting you focus on creating great features for your app instead of worrying about backend setup and maintenance[3][5][1]. https://github.com/appwrite/appwrite

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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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