On veut compiler une nouvelle librairie dans notre beau code. Le “README” indique seulement 2 dépendances… Par expérience, c’est rare que la liste soit exhaustive.
Voici un truc rapide de la part d’un collègue pour aller à la «chasse aux dépendances» sans cochonner sa machine. Ça implique Docker et linux.
Voici quelques conseils pour réduire la friction des télétravailleurs avec le bureau.
For a few months I have been struggling with small sound issues. It’s hard to put words to describe it, but it was like noise/static here and there, more often than not while playing games and rendering 3D.
I tried reinstalling audio drivers but that didn’t fix the issue. Finally, I found a post (to which I lost the link) with the solution.
In Windows 10 power options, I changed the configuration from “balanced” to “performance”.
At Datacratic, part of our infrastructure runs in the cloud. Our elastic cluster is managed by StarCluster and job dispatch is managed by Open Grid Scheduler (OGS), a fork of Sun Grid Engine (SGE).
While I was looking at StarCluster’s output to help a new user, I realized there was a lot of timeouts. I dug a bit and found out that the command
qacct was too slow. Following the lead, I understood that the said command parses a text file each time it is executed.
After a few years of operations, our main cluster has dispatched over 5 000 000 jobs. The log file parsed by
qacct was about 2Gb in size. Tada! A couple of search queries taught me that OGS, in its install directory (
<path to ogs>/util/logchecker.sh), has a script, ready to be configured, to rotate its logs. I configured and launched it. The timeouts are gone.
Lesson learned: StarCluster/OGS operators, it is important to configure and schedule that script to run every now and then if you want to keep your operations stable.
While working on a C++ program that made use of a Python wrapped library to start a subprocess, when the subprocess crashed I got a
boost::detail::system_signal_exception at the C++ level even though the executing context was still on the python layer. How is that possible?