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Meeting 24 Jan

Agenda

  • Background
  • What to do differently this time
  • What goes to what days & teachers
  • Learning outcomes
  • Resources
  • How to advertise?
  • Next meeting
  • Next meeting R-matlab-julia

Background

As discussed in a hurry in December when setting dates

  • New course/workhop with 4 days of python

  • APPROX

    • 24 Apr: intro to Python 24 April
      • beginners or warm-up
      • ?the other days not recommended for complete beginners?
        • Too short of time maybe to digest?
        • Or let the users decide 👍 [name=RB] <---
      • separate registration? 👍 [name=RB]
        • Advertisement for all 4 days, learners pick on registration
    • 25 Apr packages, pandas, matplotlib
      • some higher analysis later april?
    • 28-29 Apr: slurm, parallel, big data, GPU and ML/DL/AI
      • including some analysis, like seaborn and ?
    • Is this a good way?
      • [name=RB] Yes, this is fine

  • Proposal

    • 1st day with intro (similar or same as Richel's 'NAISS Intro to Python' course on March 4th)

      • cluster modules: it is a NAISS course :-)
      • has a login session

    • 2nd day will be about packages and running on a cluster + basic analysis without slurm!!

      • morning: packages, virt env
        • conda is used in many clusters
        • pip and possibly pixi?
        • ?containers. We'll decide later 👍
      • afternoon
        • analysis without slurm(?)
        • what fits in here?
        • [VOTED] IDE's
          • [name=RB] ?Jupyter
          • [name=JY] ?Spyder
          • [name=JY] ?VScode
        • need interactive session

    • 3rd day needs a introduction to slurm and GPUs

      • slurm, batch, interactive, on-demand
      • analysis requiring more than login resources
      • (parallel), (big data), (GPU)

    • 4th day for the most advanced material

      • Anders Hast (InfraVis) comes as a guest teacher to talk about dimensionality reduction techniques. (or first day): must be before 'Machine learning' in the schedule, ~30-60 mins
      • Data preparation
      • (GPU), ML/DL/AI
      • ?Professional Python development, e.g. testing

  • Material, timing and interactivity

    • NAISS 'Intro to Python' --> Day 1 ≈ 1:1 relation
    • Combine
      • Python day in 4-day course
        • more packaging
        • less analysis
        • less ML
      • HPC-python in 2 days
        • less packaging
        • more IDEs
        • more analysis
        • more ML/DL
      • much common material
        • login, run, slurm, parallel, packaging
      • --> 3 days into 3 days --> more relaxed and airy!!
        • BUT: include Anders Hast as well?

What to do differently this time

What goes to which day? And Teachers

  • Earlier sessions from HPC-python (to be placed out)

    • intro (just first day?) [name=RB]
    • load (just first day?) [name=RB]
    • install packages (expanded) [name=BC]
    • Compute nodes [name=BB]
      • batch [name=BB]
      • interactive [name=B]
      • Desktop on demand [name=RP]
      • IDEs [name=BB]
    • matplotlib [name=RP]
    • GPU [name=BB]
    • Pandas [name=RP]
    • seaborn etc [name=RP]
    • parallel [name=PO]
    • big data and formats [name=BC]
    • Dimensionality reduction? [name=AH]
    • ML/DL [name=JY]

  • Day 1 (copied from NAISS 'Intro to Python')

⚠️ Does not include loading a module

  • BC: could we have "loading lmod module" here?
Time Topic
09:00-10:00 Using the Python interpreter, includes login
10:00-10:15 Break
10:15-11:00 The way of the program, includes creating and running a script
11:00-11:15 Break
11:15-12:00 Working with Python scripts
12:00-13:00 Break
13:00-14:00 Variables, expressions and statements: variables
14:00-14:15 Break
14:15-15:00 Variables, expressions and statements: operators
15:00-15:15 Break
15:15-15:45 Variables, expressions and statements: user input
15:45-16:00 Evaluation
  • Day 2
    • [VOTED] IDEs
Time Topic
09:00-10:00 .
10:00-10:15 Break
10:15-11:00 .
11:00-11:15 Break
11:15-12:00 .
12:00-13:00 Break
13:00-14:00 .
14:00-14:15 Break
14:15-15:00 .
15:00-15:15 Break
15:15-15:45 ?RB: Jupyter
15:45-16:00 Evaluation
  • Day 3
  • Day 4

Learning outcomes

  • Useful

  • We have most material, BUT

  • what is the amount users can learn in 3-4 days?

  • balance of

    • be confident in doing
      • many exercises
    • knowing big picture and knowing where look up details
      • showing
        • demos
      • mentioning
        • talk
        • discuss
      • link to good material for plunging into
    • BC's view: all are needed, not just one or 2

  • where to put the level in this course?

    • different in different sessions?

  • Learning Outcomes

    • learner-centered
    • make material from these!

Resources

  • NSC for non-users
  • as usual + dardel

How to advertise?

  • Each day having a name describing content?

How to work?

  • issues
  • meetings (how often)
  • matrix (phase out slack)
  • combination

ToDos

  • All:
    • Look at evaluation and think about changes
    • (LOs)
    • schedule
    • suggestions of more required time for sessions

Next meeting

  • Fri Feb 14 11-12

Next meeting R-matlab-julia

  • Fri 31 Jan 11-12