Memray is incredibly usable. In a few projects I was able to go from 0 to identifying my "problem children" in less than 15 minutes.<p>I don't use it often, but when I need to I'm glad it's a part of my toolbox!
I have a lot of praise for using Memray as part of working out some memory kinks in a codebase I’m working on. It allowed me to quickly iterate on some hunches and then enshrine the resulting memory savings in tests via pytest-memray.
Does anyone know how it compares to scalene?<p><a href="https://github.com/plasma-umass/scalene">https://github.com/plasma-umass/scalene</a>
Related:<p><i>Memray: a memory profiler for Python</i> - <a href="https://news.ycombinator.com/item?id=31102089">https://news.ycombinator.com/item?id=31102089</a> - April 2022 (48 comments)
memray is awesome. Used it to demonstrate massive memory savings wins for presentations<p>My favorite feature is the jupyter integration. Just decorate a cell with
%%memray_flamegraph<p>I'd really like a version of this to spit out a csv or something for going straight into making plots
bloomberg! they seem to be sponsoring a bunch of good work for OSS. Thank you!! I also happen to meet some of their engineers at Gophercon this year. Wouldn't have thought they do so much! Thanks!
Ive used Memray and it works great locally. But when I deployed my application over long running processes (i.e. in production) because I want to see memory usage over a long period of time, the profiler outputs get really large, like hundreds of gbs. They cause disk outages and also take forever to download and visualize with the flamegraphs. What do people use to understand memory usage of long running workloads in production?