01:00 <bbot2> Started: http://buildbot.pypy.org/builders/pypy-c-jit-win-x86-32/builds/4680 [py3.6]

01:00 <bbot2> Started: http://buildbot.pypy.org/builders/own-linux-x86-32/builds/6505 [py3.6]

01:00 <bbot2> Started: http://buildbot.pypy.org/builders/pypy-c-jit-linux-x86-32/builds/5283 [py3.6]

01:00 <bbot2> Started: http://buildbot.pypy.org/builders/pypy-c-jit-macosx-x86-64/builds/4455 [py3.6]

01:00 <bbot2> Started: http://buildbot.pypy.org/builders/pypy-c-jit-linux-x86-64/builds/6282 [py3.6]

01:00 <bbot2> Started: http://buildbot.pypy.org/builders/own-linux-x86-64/builds/7531 [py3.6]

02:06 <bbot2> Failure: http://buildbot.pypy.org/builders/pypy-c-jit-linux-x86-32/builds/5283 [py3.6]

02:29 <bbot2> Failure: http://buildbot.pypy.org/builders/pypy-c-jit-linux-x86-64/builds/6282 [py3.6]

03:38 <bbot2> Failure: http://buildbot.pypy.org/builders/own-linux-x86-32/builds/6505 [py3.6]

03:48 <bbot2> Failure: http://buildbot.pypy.org/builders/pypy-c-jit-macosx-x86-64/builds/4455 [py3.6]

04:02 <bbot2> Failure: http://buildbot.pypy.org/builders/own-linux-x86-64/builds/7531 [py3.6]

05:00 <bbot2> Started: http://buildbot.pypy.org/builders/jit-benchmark-linux-x86-64/builds/2632

06:08 <bbot2> Failure: http://buildbot.pypy.org/builders/pypy-c-jit-win-x86-32/builds/4680 [py3.6]

06:46 <kenaan> mattip py3.6 bfa7f1269de9 /pypy/objspace/std/: more closely follow cpython implementation for error modes

07:19 <bbot2> Success: http://buildbot.pypy.org/builders/jit-benchmark-linux-x86-64/builds/2632

08:53 <niso> Hi, img trying t o "hunt" some high memory usage on my program which i can not reproduce atm. there is any useful packages for purpose? ATM i thought to wrap some of my main functions with decorator that will monitor the memory before and after annd after the function (using psutil), any other recommendations? TY!

09:17 <arigato> see https://stackoverflow.com/questions/45210681/how-do-i-profile-memory-usage-with-pypy . maybe someone else will have another tool to add

09:19 <arigato> "python -m vmprof --mem" is or was known to work at some point

09:22 <arigato> (I mean "pypy -m vmprof --mem")

10:28 <niso> atm i dont know what cause this high memory usage, so i want to add some logs that will points me to the flows that can cause this. vmprof require a specific flow no?

10:28 <niso> and i saw objgraph, but that will be used when i can produce that problem

10:30 <mattip> vmprof is a statistical profiler. Every x millisecs it checks your programs and logs the stack (an maybe memory use)

10:30 <mattip> when your program is done, you analyse the log

10:31 <mattip> then you can see how often and how long it was in a particular function call, and maybe how much memory was in play

10:35 <niso> I remember that there was an issue with memory profiling with pypy + vmprof, that not relevant anymore?

20:06 <kenaan> stevie_92 cpyext-gc-cycle a1dc8b9e7d98 /: Implemented tuple untracking for rrc objects Improved stability list traversal and removed unneccesary ...