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Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) or by staging out a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools. As a result, you can finally read your automatic derivative code just like the rest of your program. Tangent is useful to researchers and students who not only want to write their models in Python, but also read and debug automatically-generated derivative code without sacrificing speed and flexibility. ...

The project is now hosted on github: https://github.com/yanqi27/core_analyzer.git Core analyzer is a power tool to debug memory issues. With ever increasing complexity and dataset size of application programs, it is very challenging to find the root cause of a memory bug. Programmers often use debugger to go through many contexts or data objects in order to hunt down a suspicious variable or object. However, the task is boring, prone to error and usually fruitless. By parsing a core dump...