Development of efficient numerical algorithms requires precisely monitoring execution time and resource consumption. When working with large datasets, memory consumption can be a real bottleneck. We present a small extension to memory-profiler (https://github.com/fabianp/memory_profiler) that allows recording of memory usage along time, and some experimental facts about Numpy/Scipy memory usage. A few best practices are highlighted.
