The Unix Shell

• Michel Jouvin (IJCLab)

Room: 200/1-139 - Salle 139 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

Git Survival Guide

• Sonny Lion (IJCLab)

Room: 200/1-139 - Salle 139 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

Paris-Saclay Computing Center Howto

• Etienne Fayen
• Julien PELOTON (CNRS-IJCLab)
• marco leoni

Room: 200/1-139 - Salle 139 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).

Python Traps & Pitfalls

• David Chamont (IJCLab - IN2P3 - CNRS)

Room: 200/1-139 - Salle 139 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.

Computation accuracy

• Vincent LAFAGE (IJCLab)

Room: 200/1-139 - Salle 139 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

Object-Oriented C++

• Ivana HRIVNACOVA

Room: 200/1-139 - Salle 139 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). The course web site: [https://geant4-ed-project.pages.in2p3.fr/oo-cpp-web/](https://geant4-ed-project.pages.in2p3.fr/oo-cpp-web/)

Make Your Code More Robust 1/2

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

Room: 200/1-139 - Salle 139 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.

Introduction to Machine Learning

• Francoise BOUVET (IJCLab)

Room: 200/1-139 - Salle 139 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).

Make Your Code More Efficient 1/2

• Hadrien Grasland (IJCLab)

Room: 200/1-139 - Salle 139 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.

C++17 Initiation for Pythonists

• David Chamont (IJCLab - IN2P3 - CNRS)

Room: 200/1-139 - Salle 139 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.

CMake Survival Guide

• Philip Deegan (LPP)

Room: 200/1-139 - Salle 139 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.

Make Your Code More Robust 2/2

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

Room: 200/1-139 - Salle 139 Second part of "Make Your Code More Robust". See the contribution "Make Your Code More Robust 1/2".

GitLab Projects and Continuous Integration

• Sonny Lion (IJCLab)

Room: 200/1-139 - Salle 139 This course is NOT ABOUT GIT; here, we will focus on using GitLab for software pro-ject management. You will learn how to leverage GitLab's features to facilitate collab-orative 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.

Open Code and Data

• Philippe Gauron (IJCLab/CNRS-Université Paris-Sud)

Room: 200/1-139 - Salle 139 Research is based on reproducibility and an incremental improvement process. Open Science movement has been developed to facilitate this work and relies on 3 legs: open access to publications, open data and libre software. Topics : - open data - their organization, the conditions of their publication and their reuse - their valorization - Free/libre software - the software licences, - their valorization, - data management plan (DMP) - the associated tools - their lifecycle,

Web Interfaces In a Nutshell

• Sonny Lion (IJCLab)

Room: 200/1-139 - Salle 139 This course provides an introduction to web interface development. We will explore the fundamentals needed to design and develop interactive web user interfaces. Specifically, the course covers the following aspects: - Client-server architecture - Markup languages (HTML) - Cascading Style Sheets (CSS) - JavaScript and client-side interactivity - Server communication (AJAX) - Front-end frameworks and libraries - Best practices in web interface design Prerequisites : knowledge of a programming language (Python, Javascript or other).

Make Your Code More Efficient 2/2

• Hadrien Grasland (IJCLab)

Room: 200/1-139 - Salle 139 Second part of "Make Your Code More Efficient". See the contribution "Make Your Code More Efficient 1/2".

Introduction to Deep Learning

• Francoise BOUVET (IJCLab)

Room: 200/1-139 - Salle 139 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 ».