On Sun, Oct 14, 2018, 22:34 Konstantin Gizdov <arch@kge.pw> wrote:
Hello,
I am Konstantin Gizdov [1] [2], (`kgizdov`, `arch@kge.pw`, `kgizdov@gmail.com`)
I would like to apply to be a Trusted User under Baptiste Jonglez's sponsorship.
A few words about me:
I am currently a Particle Physics PhD at Univerisity of Edinburgh and I have used Linux since my early teenage years. After I finished high-school, Linux has been my main operating system. I embraced Open Source software for a long time ago and contribute to such several projects [2][3][4]. I have been with Arch Linux since 4+ years ago, although only more active since 2+ years.
My main work is concentrated on Experimental Particle Physics & the LHCb Detector at the Large Hadron Collider at CERN. As part of that I have been involved in the development, upgrade & maintenance of the High-Level Trigger & RICH systems and LHCb's data flow [3]. I also have experience with a lot of data processing & analysis - data distillation & enrichment, machine learning, statistical analysis, etc - and associated tools. Separately, I maintain several machines - a personal web server, company server with several VMs, local workhorse server, personal workstation & laptop (sprinkle around some RaspberryPis and network devices here and there). This put me in a unique position to work with many and different kinds of systems and software - ranging from ASICs & FPGAs, through localized control systems & end-user devices to large clusters & super computers. Daily, I use popular tools such as VMs, docker, git, GCC, CUDA, tensorflow, Cern's ROOT, but I also run a lot of custom and even-self made software [4][6]. All of this has been a breeze on Arch Linux.
Thus, a couple of years ago, I decide to get more involved and contribute. I took on the task to maintain CERN's ROOT package [7] and since then I've involved myself heavily into that, I'm a contributor to the project and I use it daily in my work. I have been providing this package for many colleagues in the field, including all of its stack & complementary tools (Pythia, XRootD & other Python tools). I have enabled a lot of new features and worked with upstream towards new functionality, bug fixes, etc. On top of that I have shipped several other related projects - machine learning packages, SciKit-HEP packages like uproot, Docker images, GitLab CIs and so on.
I have also been able to develop and publish a machine learning project me and colleague came up with [4]. This is soon going to be a package in SciKit-HEP and I will aim to make it package here too. Arch Linux was a great platform for all of this. I was able to install & configure up-to-date software easily and what I did not find, I provided for me & others on the AUR without too much hassle.
Overall, I have to say Arch Linux (and its community) have played a key role in me being able to do all of these things. I have found the OS itself to be stable and flexible and the users & maintainers approachable and direct, which I appreciate a lot. I have met a lot of people through the Arch Linux community - forums, AUR and just saying 'I use Arch, too!', haha.
The reason for applying to become a TU is to get even more involved and give back to the community. If you accept me, I would like to continue maintaining and improving my current packages as well as bring new packages. As an AUR maintainer I basically consider it an on-going duty already.
I would like to maintain/contribute/adopt the following:
* Packages I would like to co-maintain: o python-awkward o libafterimage o xxhash o unuran * Packages I already maintain and intend to move from AUR: o root & root-extra o xrootd o simpletools o root5 o python-root_numpy o python-uproot o python-uproot-methods o python-hep_ml o pythia o llvm50 o llvm50-libs o clang50 * New packages I would like to add/move from AUR: o cern-vdt o cvmfs o HepDrone [4] o python-keras o root_pandas (new) o histbook (new) o decaylanguage (new) o pyjet (new) o vegascope (new) o root_ufunc (new) o formulate (new)
I hope to make Arch Linux more versatile and accessible to users in data science, high-energy physics & machine learning, and possibly as a whole.
Thank you.
-- Regards,
Konstantin
1. https://keybase.io/kgizdov 2. https://github.com/kgizdov 3. https://gitlab.cern.ch/kgizdov 4. https://github.com/Tevien/HEPDrone 5. https://github.com/scikit-hep 6. https://gitlab.cern.ch/kgizdov/pdqa-automation
Great stuff. Would you be interested in co-maintaining tensorflow, cuda and pytorch and related packages? They sometimes cost a lot of time to fix up.