scalene

ata." Some links from the presentation - https://github.com/plasma-umass#plasma-projects - https://github.com/stephenfreund/flowco Plasma - projects: - RightTyper: Automatically annotates your Python code with high-quality types - https://github.com/righttyper/righttyper - Scalene: A state-of-the-art CPU+GPU+memory profiler for Python, with AI-powered optimization suggestions GitHub stars - https://github.com/plasma-umass/scalene - Coz: A causal profiler that tells you where to optimize your code (C/C++/Rust/Swift/Java) - https://github.com/plasma-umass/coz - CSrankings: Ranks the world's CS departments - https://csrankings.org/ - CoverUp: Automatically generates high-coverage tests - https://github.com/plasma-umass/coverup - Slipcover: Nearly zero-overhead code coverage analysis for Python - https://github.com/plasma-umass/slipcover - ChatDBG: AI-assisted debugging in lldb, gdb, and pdb. Uses AI to answer 'why' - https://github.com/plasma-umass/ChatDBG - CWhy: ("See why") Explains and suggests fixes for C/C++/Rust compiler error messages. - https://github.com/plasma-umass/CWhy - Commentator: Automatically comments Python code, adding docstrings and type annotations - https://github.com/plasma-umass/commentator - SQLwrite: SQLite3 with natural language to SQL, recommends indexes to optimize queries - https://github.com/plasma-umass/sqlwrite

ene/issues/554. (Full disclosure: I am one of the principal designers of Scalene.)

to use it properly. I tried Memray, but first ran into an issue (https://github.com/bloomberg/memray/issues/212), but after using some workaround, it worked now. I get a flamegraph out, but it doesn't really seem accurate? After a while, there don't seem to be any new memory allocations at all anymore, and I don't quite trust that this is correct. There is also Austin (https://github.com/P403n1x87/austin), which I also wanted to try (have not yet). Somehow this experience so far was very disappointing. Side node, I debugged some very strange memory allocation behavior of Python before, where all local variables were kept around after an exception, even though I made sure there is no reference anymore to the exception object, to the traceback, etc, and I even called frame.clear() for all frames to really clear it. It turns out, frame.f_locals will create another copy of all the local variables, and the exception object and all the locals in the other frame still stay alive until you access frame.f_locals again. At that point, it will sync the f_locals again with the real (fast) locals, and then it can finally free everything. It was quite annoying to find the source of this problem and to find workarounds for it. https://github.com/python/cpython/issues/113939 Another side node: Bloomberg has a couple of nice open source projects. E.g. I just realized, PyStack (https://bloomberg.github.io/pystack/) is also by Bloomberg.