How can you access our #PythonForSciComp videos without YouTube? Via this repository, #GitAnnex to distribute raw videos around. One of those places is a publicly accessible object storage, and with a few commands you can download the processed videos. https://github.com/coderefinery/video-processing/
Problem: yes, git-annex installation is a barrier. This isn't designed as a primary access method but a backup.
#PythonForSciComp 2023 is done, and it was probably one of the best instances so far. Videos are on YouTube; stay tuned for follow-up, "how we did videos so quickly", and the way to view them without YouTube.
As always, all materials will stay public and we welcome contributions for next year.
https://scicomp.aalto.fi/training/scip/python-for-scicomp-2023/
#python #SciComp #HPC #RSEng
#PythonForSciComp day 3 is tomorrow, 8:50 CET. Almost everyone will find something new here:
- Scripts (break out of your IDE/Jupiter and make your work reusable)
- Library ecosystem (thoughts on reuse)
- Dependencies (make your environment reusable)
-Binder (make your code reusable by anyone in place)
https://scicomp.aalto.fi/training/scip/python-for-scicomp-2023/
I processed all today's #PythonForSciComp videos in record time (and record "not getting annoyed at myself") - and I'm still doing it all myself. With some extra helpers who weren't teaching, I bet it could be done in 1-2 hours. Do any other courses have a video processing workflows that go this fast?
#PythonForSciComp videos are published for day 2. The course ended at 13:00 and by 18:06 all five videos from the 3 hour day are live, which is a new record (both speed and lack of frustration). Videos may not be that useful for someone learning later (we've got reading material for that), but it does allow instant reviewing and catching-up.
https://www.youtube.com/playlist?list=PLZLVmS9rf3nNI3oQEqSJW6yXltOAZnkpa
#teaching #python #VideoProduction #coderefinery
Team meeting summary 2023-11-06
Today, we talked just a bit about #PythonForSciComp starting tomorrow (follow @SciCompAalto).
Then had a very long discussion about what we'd like for CodeRefinery for the future:
MOOCs? In-person? YouTuber/Streamer? We'd have people interested in all of these and what unifies us is the material. If you are interested in using CodeRefinery material for any of these, get in touch and join our upcoming meetings.
#PythonForSciComp (#Python for Scientific Computing) starts tomorrow and as usual, it's online and #livestream, so anyone can watch (9:00 CET). Drop ins are welcome, and Q&A/videos are usually published same-day.
Day 1 (tomorrow) starts with basics to make sure we are all on the same page: #Jupyter, #Numpy, #Pandas.
https://scicomp.aalto.fi/training/scip/python-for-scicomp-2023/
#SciComp #HPC #AcademicMastodon #PhD
Want to know how many people we can reach with a livstream course? Check our stats from #PythonForSciComp: https://github.com/coderefinery/workshop-stats/tree/main/data/python-for-scicomp-2022
Summary for day 2:
~300 uniques on stream, ~150 average
~ 500 Twitch recording views same-day as the course (instant review)
~ 500 unique lesson material viewers
~ #HedgeDoc maxed at ~190 simultaneous engagers
In #PythonForSciComp course, I closely tracked stats this time. For our automatic Twitch videos, we got hundreds of views - almost all the same afternoon after the course (course was in mornings).
This tells me that many people were engaging and using those videos to review in preparation for the next day. I think this is an important and often-missed way to support learners.
Stay tuned to @coderefinery for behind the scenes of these videos.
#PythonForSciComp And we are done! We'd like to thank the ~10 instructors and ~5-10 other staff who helped make this possible.
> Best course I've taken all year, thanks so much everyone! +2
#teaching #CodeRefinery #Python #SciComp
material for future reading: https://aaltoscicomp.github.io/python-for-scicomp/
#PythonForSciComp schedule update:
- soon: #packaging python code: #pyproject.toml, #pip, #PyPI
- xx:30: Panel discussion
- xx:50: Outro
#PythonForSciComp is now live on Twitch. Upcoming schedule:
- now: dependencies (#pip, #conda, environments, requirements.txt, environment.yml)
- in about an hour: #BinderHub #reproducibleresearch
#Livestream courses worth it or not? Sample feedback:
> I like livestreaming as you can have multiple levels in the course depending on your background. Like this document, where you can ask also more advanced questions. +1 (livestreaming is so much more flexible. Lecture halls are easily dominated by one or a few students - whose questions others might find not interesting/important)
#PythonForSciComp day 3 has finished, and it went great!
Tomorrow we switch and focus on the big picture of #ReproducibleResearch, using and reusing #python code.
- Dependency management with #pip #conda #mamba #virtualenv - keep your work organized!
- #MyBinder / #BinderHub for making code easy-to-run by others in the cloud
- Packaging and sharing Python libs
- A #PanelDiscussion where you can ask us anything
- Wrap-up and after-party
Tomorrow 9:50 EET / 8:50 CET, https://scicomp.aalto.fi/training/scip/python-for-scicomp-2022/
#PythonForSciComp schedule update:
- Now (xx:10), Parallel in #python. #multiprocessing is useful for many people, #MPI is more as a demo. A good 50-min intro if you want to know what's available.
#PythonForSciComp behind the scenes. This is my #livestream setup that also lets me monitor what all the audience and @coderefinery instructors are communicating. Maybe a bit excessive but it seems to work well!
#OBS for broadcasting, #zoom for instructor studio, @zulip for internal communication, @hedgedoc for audience communication, #twitch for the stream, #github for lessons. #debian of course.
#PythonForSciComp is going well. Coming up:
- Now: #scipy library ecosystem
- xx:15 or so: getting data from web APIs
- In about an hour: #parallel (#multiprocessing #MPI in #Python)
#PythonForSciComp #livestream resumes tomorrow morning 9:50 EET / 8:50 CET, with these four lessons:
- scripts (moving from #jupyter notebooks to reusable interfaces with #CommandLine)
- T+~1h: library ecosystem (#scipy ecosystem)
- T+~1h: Getting data from web APIs with #requests
- T+~2h: Parallel code in Python (#multiprocessing, #MPI, and a bit more)
All are relative basic level, designed to introduce new programmers to the topics.
#PythonForSciComp, "data formats with #Python" just starting. Computing is one thing, but reading the data in can often take more time than you expect! Learn how to make things better from an expert:
https://twitch.tv/coderefinery
#teaching #livestream #CodeRefinery
Day finished successfully, feedback was good. We resume tomorrow 9:50 EET / 8:50 CET with more #Pandas (going through practical usage), #visualization (#matplotlib as a base of the ecosystem), and then disk-based data formats.
If you want something to review for tomorrow, check out #TidyData as defined in this paper - useful for anyone organizing, #Python or not:
https://vita.had.co.nz/papers/tidy-data.pdf
