Suggest LO for @bclaremar

docs/day1/intro.md

@@ -0,0 +1,148 @@
+# Introduction
+
+!!! info "Learning outcomes"
+
+    - Learners know how the course material is organized
+    - Learners know how to download and unzip the tarball
+
+<!-- below is still RST -->
+
+`Welcome page and syllabus <https://uppmax.github.io/HPC-python/index.html>`_
+   - Also link at House symbol |:house:| at top of page 
+
+.. admonition:: **Learning outcomes**
+
+   - Load Python modules and site-installed Python packages
+   - Create a virtual environment
+   - Install Python packages with pip (Kebnekaise, Rackham, Snowy, Cosmos)
+   - Write a batch script for running Python
+   - Use the compute nodes interactively
+   - Use Python in parallel
+   - Use Python for ML
+   - Use GPUs with Python
+
+
+What is python?
+---------------
+
+As you probably already know…
+
+    - “Python combines remarkable power with very clear syntax.
+    - It has modules, classes, exceptions, very high level dynamic data types, and dynamic typing. 
+    - There are interfaces to many system calls and libraries, as well as to various windowing systems. …“
+
+In particular, what sets Python apart from other languages is its fantastic
+open-source ecosystem for scientific computing and machine learning with
+libraries like NumPy, SciPy, scikit-learn and Pytorch.
+
+- `Official Python documentation <https://www.python.org/doc/>`_ 
+- `Python forum <https://python-forum.io/>`_
+- `A nice introduction to packages <https://aaltoscicomp.github.io/python-for-scicomp/dependencies/>`_
+- The youtube video `Thinking about Concurrency <https://www.youtube.com/watch?v=Bv25Dwe84g0>`_ is a good introduction to writing concurrent programs in Python
+- The book `High Performance Python <https://www.oreilly.com/library/view/high-performance-python/9781492055013/>`_ is a good resource for ways of speeding up Python code.
+
+Material for improving your programming skills
+::::::::::::::::::::::::::::::::::::::::::::::
+
+.. admonition:: First level
+
+   `The Carpentries <https://carpentries.org/>`_  teaches basic lab skills for research computing.
+
+   - `Programming with Python <https://swcarpentry.github.io/python-novice-inflammation/>`_ 
+
+   - `Plotting and Programming in Python <http://swcarpentry.github.io/python-novice-gapminder/>`_ 
+
+   General introduction to Python by UPPMAX at https://www.uu.se/en/centre/uppmax/study/courses-and-workshops/introduction-to-uppmax
+
+
+.. admonition:: Second level
+
+   Other course/workshops given by NAISS HPC centres:
+
+   - `Pandas by LUNARC <https://github.com/rlpitts/Intro-to-Pandas>`_
+   - `Matplotlib for publication <https://github.com/rlpitts/Matplotlib4Publication>`_
+
+
+   CodeRefinery develops and maintains training material on software best practices for researchers that already write code. Their material addresses all academic disciplines and tries to be as programming language-independent as possible. 
+
+   - `Lessons <https://coderefinery.org/lessons/>`_ 
+   - `Data visualization using Python <https://coderefinery.github.io/data-visualization-python/>`_
+   - `Jupyter <https://coderefinery.github.io/jupyter/>`_
+
+   Aalto Scientific Computing
+
+   - `Data analysis workflows with R and Python <https://aaltoscicomp.github.io/data-analysis-workflows-course/>`_
+
+   - `Python for Scientific Computing <https://aaltoscicomp.github.io/python-for-scicomp/>`_
+
+      - `Introduction to packages <https://aaltoscicomp.github.io/python-for-scicomp/dependencies/>`_
+
+
+.. admonition:: Third level
+
+   `ENCCS (EuroCC National Competence Centre Sweden) <https://enccs.se/>`_ is a national centre that supports industry, public administration and academia accessing and using European supercomputers. They give higher-level training of programming and specific software.
+
+   - `High Performance Data Analytics in Python <https://enccs.github.io/hpda-python/>`_
+
+   - The youtube video `Thinking about Concurrency <https://www.youtube.com/watch?v=Bv25Dwe84g0>`_ is a good introduction to writing concurrent programs in Python 
+
+   - The book `High Performance Python <https://www.oreilly.com/library/view/high-performance-python/9781492055013/>`_ is a good resource for ways of speeding up Python code.
+
+Documentations at other NAISS centres
+-------------------------------------
+
+.. seealso::
+
+   - LUNARC
+      - `Python <https://lunarc-documentation.readthedocs.io/en/latest/guides/applications/Python/>`_
+      - `Jupyter <https://lunarc-documentation.readthedocs.io/en/latest/guides/applications/Python/?query=jupyter#jupyter-lab>`_
+   - C3SE
+      - `Python <https://www.c3se.chalmers.se/documentation/applications/python/>`_
+      - `Jupyter <https://www.c3se.chalmers.se/documentation/applications/jupyter/>`_
+      - `Python <https://www.nsc.liu.se/software/python/>`_
+   - PDC
+      - `Python <https://support.pdc.kth.se/doc/software-docs/python/>`_
+
+.. important::
+
+   Project ID and storage directory 
+
+   - UPPMAX: 
+       - Project ID: naiss2024-22-1442
+       - Storage directory: /proj/hpc-python-fall  
+   - HPC2N: 
+       - Project ID: hpc2n2024-142
+       - Storage directory: /proj/nobackup/hpc-python-fall-hpc2n
+   - LUNARC: 
+       - Project ID: lu2024-2-88
+       - Storage directory: /lunarc/nobackup/projects/lu2024-17-44  
+   - NSC: 
+       - Project ID: naiss2024-22-1493
+       - Storage directory: /proj/hpc-python-fall-nsc  
+
+   Login to the center you have an account at, go to the storage directory, and create a directory below it for you to work in. You can call this directory what you want, but your username is a good option. 
+
+.. important::
+
+   Course material 
+
+   - You can get the course material, including exercises, from the course repository on GitHub. You can either (on of these): 
+       - Clone it: ``git clone https://github.com/UPPMAX/HPC-python.git``
+       - Download the zip file and unzip it: 
+           - ``wget https://github.com/UPPMAX/HPC-python/archive/refs/heads/main.zip``  
+           - ``unzip main.zip``
+
+   - You should do either of the above from your space under the course directory on the HPC center of your choice. 
+
+.. objectives:: 
+
+    We will:
+
+    - teach you how to navigate the module system at HPC2N, UPPMAX, LUNARC, and NSC
+    - show you how to find out which versions of Python and packages are installed
+    - look at the package handler **pip**
+    - explain how to create and use virtual environments
+    - show you how to run batch jobs 
+    - show some examples with parallel computing and using GPUs
+    - guide you in how to start Python tools for Machine Learning
+

meeting_notes/20250228.md

@@ -12,8 +12,8 @@
     - That other course does not have Python anymore
 
 - planning: https://hackmd.io/iHDNS90TRY-HFT7WlxFqVg
-- source: `[dead URL]`
-- material: `[dead URL]`
+- source: https://github.com/UPPMAX/R-python-julia-HPC/
+- material: https://uppmax.github.io/R-python-julia-HPC/
 
 ## Spring