The Unix Shell

• Michel Jouvin (IJCLab)

Room: Batiment 100 / Niveau -1 / Salle A913 The Unix shell has been around longer than most of its users have been alive. It has survived because it’s a powerful tool that allows users to perform complex and powerful tasks, often with just a few keystrokes or lines of code. It helps users automate repetitive tasks and easily combine smaller tasks into larger, more powerful workflows. 1. Introducing the Shell 2. Navigating Files and Directories 3. Working With Files and Directories 4. Pipes and Filters 5. Loops 6. Shell Scripts 7. Finding Things If you have a Windows, machine, you should install the Bash Shell provided by Git for Windows, following the instructions at [https://carpentries.github.io/workshop-template/install_instructions/#shell](https://carpentries.github.io/workshop-template/install_instructions/#shell). For all the questions during the installation, accept the default answer.

Git Survival Guide

• Michel Jouvin (IJCLab)

Room: Batiment 100 / Niveau -1 / Salle A913 In this survival guide, we will explore how to effectively use Git to manage and access the version history of any set of text files (code, manuscripts written in LaTeX, etc.), both in the context of team an personal projects. Specifically, we will cover the following points: - Initializing a Git repository - Tracking changes and managing branches - Merging and rebasing - Resolving conflicts - Working with remotes and forges (GitHub/GitLab) - Version control strategies - Best practices for collaborating with Git In addition to the presentation attached, there will be [online exercices](https://learngitbranching.js.org/).

Containers

• Vincent Rouvreau (INRIA)

Room: Batiment 100 / Niveau -1 / Salle A913 This training aims to give a taste of Docker with a hands-on experience. Docker concepts and its architecture will be explained in their basics. The main objective of this training is to show a complete workflow that could be useful to everyone. By the end of the course, participants should have a basic understanding of the concepts and some experience with the basic Docker commands that would enable them to assess possible use cases for their own work. More in-depth reading is left to the participants. Apptainer may be mentioned (subject to confirmation). Prerequisites: Docker must be installed and configured. Docker Engine is only available for Linux platforms. For Windows and Mac, Docker can be used through Docker Desktop. Commercial use of Docker Desktop requires paid subscription under certain conditions. This training will not cover Docker Desktop.

Introduction to Machine Learning

• Francoise BOUVET (IJCLab - CNRS - UPsay)

Room: Batiment 100 / Niveau -1 / Salle A913 We will describe the main concepts of Machine Learning (ML) and give some clues to address a problem of ML. In particular, we will talk about : - the concepts of AI/Machine Learning/Deep Learning, - supervised/unsupervised learning, - the preprocessing of the data, - the general principle of the algorithm, - the main pitfalls, - the evaluation of the training and the outcomes. Some exercises will be provided to understand the basic concepts of standard ML methods. Prerequisites: practice of Python and main libraries (numpy, pandas, matplotlib).

Paris-Saclay Computing Center Howto

• Guillaume PHILIPPON (IJCLab - CNRS)
• marco leoni

Room: Batiment 100 / Niveau -1 / Salle A913 This course is aimed at students wishing to learn how to use the scientific computing platforms at the Paris Saclay Computing Center (https://mesocentre.universite-paris-saclay.fr/). We will review the different existing computing architectures (cloud, supercomputer). We will then look at how to access the VirtualData cloud and the Ruche HPC computer, and how to use the resources effectively in a variety of situations. Finally, we will detail the use of services deployed in the cloud, such as the JupyterHub@Paris-Saclay (https://jupyterhub.ijclab.in2p3.fr/) and the GitLab instance (https://gitlab.dsi.universite-paris-saclay.fr).

Introduction to Deep Learning

• Francoise BOUVET (IJCLab - CNRS - UPsay)

Room: Batiment 100 / Niveau -1 / Salle A913 We will describe the main concepts of Deep Learning (DL). We will focus on Multilayer Neural Network (MLP) and Convolution Neural Network (CNN). In particular, we will talk about : - artificial neuron, - MLP : structure and how it works, - CNN : structure and how it works, - a brief review of other NN structures. Some exercises will be provided to program simple MLP and CNN in Python with Keras. Prerequisites: - Practice of Python and main libraries (numpy, pandas, matplotlib). - Priority will be given to the attendees of « Initiation to Machine Learning ».

GitLab Projects and Continuous Integration

• Vincent Rouvreau (INRIA)

Room: Batiment 100 / Niveau -1 / Salle A913 This course is NOT ABOUT GIT; we will focus on using GitLab for software project management. You will learn to leverage GitLab's features to facilitate collaborative development, automate your tests, and publish code releases. Specifically, we will cover the following points: - Creating a project in GitLab - Managing members and permissions - Working with issues: labels, milestones, templates - Team collaboration: Branches and Merge requests - Task automation through GitLab CI/CD: creating pipelines and jobs - Managing environment variables and secrets - Publishing packages and deploying documentation Prerequisites: be able to communicate with a remote repository via Git (clone, fetch, push) and know how to manage branches.

Make Your Code More Robust 1/2

• Hadrien Grasland (IJCLab)
• Julien Peloton (CNRS-IJCLab)

Room: Batiment 100 / Niveau -1 / Salle A913 Discovery of software engineering tools and methodologies to write more tested, documented code that is easier to understand and maintain. - static analysis - tests - documentation The examples will be based on the C++ and Python programming languages. Prerequisites: some knowledge of C++ or Python.

Make Your Code More Robust 2/2

• Hadrien Grasland (IJCLab)
• Julien Peloton (CNRS-IJCLab)

Room: Batiment 100 / Niveau -1 / Salle A913 Second part of "Make Your Code More Robust". See the contribution "Make Your Code More Robust 1/2".

Python Traps & Pitfalls

• David Chamont (IJCLab - IN2P3 - CNRS)

Room: Batiment 100 / Niveau -1 / Salle A913 First, we'll take a closer look at the basic mechanisms of the Python language, beyond what can be guessed by blindly copying and pasting examples, in order to avoid the most common traps and pitfalls. Then, we'll debate the interactions with Linux. In particular, we will talk about : - variables semantic, duck typing, - automatic memory management, - builtin types and collections, - shallow and deep copy, - functions, local and global variables, - differences between interpreter, scripts and notebooks, - the import and distribution of modules and packages. Prerequisites : to have practised Python regularly, to know Linux.

Object-Oriented C++

• Ivana Hrivnacova

Room: Batiment 100 / Niveau -1 / Salle A913 Get familiar with the object-oriented programming with C++. The lectures and hands-on aim to prepare students to use and/or contribute to large C++-based projects, such as Geant4. - Introduction to object-oriented methodology - Class definition and implementation - Class data members and member functions/methods - Static data members, member functions/methods - Base class and derived class - Virtual, pure virtual functions - C++11/17 features: auto, range for loop, … Prerequisites: basic knowledge of the C++ syntax and standard library (if, loops, functions, pointers, references, iostream, string, vector). Detailed program and course material: https://geant4-ed-project.pages.in2p3.fr/oo-cpp-web/

CMake Survival Guide

• Philip Deegan (https://www.lpp.polytechnique.fr/?lang=en)

Room: Batiment 100 / Niveau -1 / Salle A913 Introduction to CMake, which is the most widely used tool for managing C/C++ projects, especially in a cross platform context. The course is targeted at people who have some programming experience with C/C++ or Fortran. In particular, we will talk about: - building simple binaries and libraries - build and running tests via ctest - integrating third party applications - modern cmake best practices - most useful compilation/link commands Prerequisites: have a basic understanding of C/C++ or Fortran.

C++17 Initiation for Pythonists

• David Chamont (IJCLab - IN2P3 - CNRS)

Room: Batiment 100 / Niveau -1 / Salle A913 First contact with C++, in its most used version in new physics projects : aka C++17. The course is targeted at people who have programming experience, especially with Python. In particular, we will talk about : - builtin types, variables, functions, - passing arguments by value and by reference, - type inference and templates, - returning a tuple of values, - std::vector vs std::array, - compilation and libraries. Prerequisites : to have practised Python regularly.

Make Your Code More Accurate

• Vincent LAFAGE (IJCLab)

Room: Batiment 100 / Niveau -1 / Salle A913 When using numbers of type float or double, are you aware that 0.1+0.2 does not equal 0.3 ? Let’s review the theory behind such pitfalls, and discuss some case studies : - the quadratic equation in kinematics calculations - variance calculations in data analysis - calculations with complex numbers - accurate summation in large Monte-Carlo calculations - precision in matrix and geometry calculations (the interest of factoring) - differential equations Elements of numerical calculation - solution of equations, minimisation - scaling: combining accuracy and efficiency Clean code for computation

Data Basics

• Vincent LAFAGE (IJCLab)

Room: Batiment 100 / Niveau -1 / Salle A913 Contenu surprise...

Make Your Code More Efficient 1/2

• Hadrien Grasland (IJCLab)

Room: Batiment 100 / Niveau -1 / Salle A913 Most computer programs are inefficient and could serve the same purpose while using 10-1000x less resources (time, energy, memory…). However, achieving this result requires know-how that is not part of the typical programming curriculum. In this course, you will learn a general methodology to make any program use computing resources more efficiently: 1. Safety first 2. Set a useful benchmark 3. Identify the limiting hardware resource 4. Locate the code that most intensely uses it 5. Make the most of other people’s work 6. Optimize your own code 7. Know your programming language Prerequisites : Shell Unix/Linux (files, make…) + C++ basics (C-like features, std::vector, iostream, virtual) or Python+numpy. Laptop capable of connecting to a Linux server via SSH, ideally configured to use eduroam.

Make Your Code More Efficient 2/2

• Hadrien Grasland (IJCLab)

Room: Batiment 100 / Niveau -1 / Salle A913 Second part of "Make Your Code More Efficient". See the contribution "Make Your Code More Efficient 1/2".