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Source channel @githubtrending · Post #15238 · Oct 20

#rust#dataflow#rust#static_analysis#vscode Flowistry is a tool for Rust programming that helps you focus on the parts of code relevant to your current task by analyzing how data flows within functions. When you select a variable or expression, it fades out unrelated code, making it easier to understand complex functions and see what affects or is affected by that code. It works as a VSCode plugin and uses Rust’s ownership system to provide precise analysis. This helps you read, debug, and modify Rust code more efficiently by reducing distractions and highlighting important dependencies. However, it has some limitations with certain Rust features like interior mutability and nested functions. https://github.com/willcrichton/flowistry

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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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