From 15ffd52c8c2053e85f6cfd947fde7ca0f6e2b8a8 Mon Sep 17 00:00:00 2001 From: Marie Weiel <marie.weiel@kit.edu> Date: Wed, 30 Oct 2024 16:16:39 +0100 Subject: [PATCH] update README --- README.md | 172 +++++++++++++++++++++++++++--------------------------- 1 file changed, 85 insertions(+), 87 deletions(-) diff --git a/README.md b/README.md index 4a83ecf..7597e18 100644 --- a/README.md +++ b/README.md @@ -1,93 +1,91 @@ -# ScalableAI - - - -## Getting started - -To make it easy for you to get started with GitLab, here's a list of recommended next steps. - -Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)! - -## Add your files - -- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files -- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command: - -``` -cd existing_repo -git remote add origin https://gitlab.kit.edu/marie.weiel/scalableai2425.git -git branch -M main -git push -uf origin main -``` - -## Integrate with your tools - -- [ ] [Set up project integrations](https://gitlab.kit.edu/marie.weiel/scalableai2425/-/settings/integrations) - -## Collaborate with your team - -- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/) -- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html) -- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically) -- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/) -- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html) - -## Test and Deploy - -Use the built-in continuous integration in GitLab. - -- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html) -- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/) -- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html) -- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/) -- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html) - +# Vorlesung 2400004 – Skalierbare Methoden der Künstlichen Intelligenz WS 24/25 +Vorlesung der KIT-Fakultät Informatik am Karlsruher Institut für Technologie + +Kontakt: +- Dr. Charlotte Debus (<charlotte.debus@kit.edu>) +- Dr. Markus Götz (<markus.goetz@kit.edu>) +- Dr. Marie Weiel (<marie.weiel@kit.edu>) +- Dr. Kaleb Phipps (<kaleb.phipps@kit.edu>) *** +### Kursthemen +- Skalierbarkeit von ML-/KI-Algorithmen +- Parallelisierungsstrategien und Performanz +- Anwendungsbeispiele -# Editing this README +### Kursbeschreibung +Die Methoden der künstlichen Intelligenz (KI) haben in der letzten Dekade zu erstaunlichen Durchbrüchen in Wissenschaft und Technik geführt. Dabei zeichnet sich zunehmend ein Trend zur Verarbeitung von immer größeren Datenmengen und dem Einsatz von parallelen und verteilten Rechenressourcen ab [1]. Ein prominentes Beispiel ist der Maschinenübersetzungsalgorithmus Generative Pre-trained Transformer 3 (GPT-3) [2], welcher mit 175 Milliarden trainierbaren Parametern auf 285.000 Prozessorkernen und 10.000 Grafikkarten die Grenzen herkömmlicher KI-Hardware sprengt. In der Vorlesung werden den Studierenden die Parallelisierung und Skalierbarkeit verschiedener KI-Algorithmen nähergebracht. Hierbei liegt der Fokus auf den Vorteilen und Ansätzen des parallelen Rechnens für KI-Methoden, verschiedenen verfügbaren Softwarepaketen zur Implementierung sowie den algorithmenspezifischen Herausforderungen. Diese werden anhand verschiedener Beispiele und Algorithmenklassen dargestellt, um die vielfältigen Anwendungsmöglichkeiten für skalierbare künstliche Intelligenz zu illustrieren: -When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template. +- Skalierbares unüberwachtes Lernen +- Skalierbares überwachtes Lernen +- Skalierbare neuronale Netze +- Skalierbare Ensemblemethoden +- Skalierbare Suchverfahren -## Suggestions for a good README - -Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information. - -## Name -Choose a self-explaining name for your project. - -## Description -Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors. - -## Badges -On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge. - -## Visuals -Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method. - -## Installation -Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection. - -## Usage -Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README. - -## Support -Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc. - -## Roadmap -If you have ideas for releases in the future, it is a good idea to list them in the README. - -## Contributing -State if you are open to contributions and what your requirements are for accepting them. - -For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self. - -You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser. - -## Authors and acknowledgment -Show your appreciation to those who have contributed to the project. +Darüber hinaus werden Datenformate und -management, gängige Maschinenmodelle sowie der Einsatz neuartiger Hardware, z.B. Quantencomputer oder neuromorphe Geräte, diskutiert. +*** +Over the last decade, artificial intelligence (AI) methods have significantly advanced the state-of-the-art in science and engineering. One of the most prominent trends is an ever increasing amount of analzyed (training) data, necessitating the usage of parallel and distributed computational resources. A well-known example for this is the machine translation algorithm Generative Pre-trained Transformer 3 (GPT-3) [1]. With a total of 175 billion parameters trained on 285.000 processor cores as well as 10.000 GPUs, this model exceeds the capabilities of traditional AI hardware. In this lecture, students will learn about parallelization and scaling approaches for different AI algorithms. An emphasis is put on the advantages of parallel computing for AI, available software packages for implementation, and, majorly, the algorithmic design challenges. In line with this, examples from the following algorithmic classes will illustrate the potential use for scalable AI: -## License -For open source projects, say how it is licensed. +- Unsupervised learning +- Supervised learning +- Neural networks +- Ensemble methods +- (Hyperparameter) search methods -## Project status -If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers. +In conjuction with the course topics, the students will also learn about supporting data formats, machine models, and the use of novel hardware, such as quantum computers or neuromorphic devices. +*** +[1] Ben-Nun, Tal, and Torsten Hoefler. "Demystifying parallel and distributed deep learning: An in-depth concurrency analysis." ACM Computing Surveys (CSUR) 52.4 (2019): 1-43. + +[2] Brown, Tom B., et al. "Language models are few-shot learners." arXiv preprint arXiv:2005.14165 (2020). + +<!-- +#### Vorlesungen +- Einführung (24.10.24) +- Grundbegriffe und **Fundamentalkonzepte** + - KI-Grundbegriffe (24.10.24) + - Parallele Hardware, Skalierbarkeit und skalierbare Programmierung (31.10.24) +- **Unüberwachtes** Lernen bzw. Clusteringverfahren + - k-Means, k-Medians, PSRS (Parallel Sorting) (09.11.23) + - DBSCAN, paarweise Distanzen (16.11.23) + - Spektrales Clustering, hierarchisches Clustering (23.11.23) +- **Überwachtes** Lernen + - Lineare und logistische Regression (30.11.23) + - (Kaskadierende) Support-Vektor-Maschinen (SVM) (07.12.23) +- **Ensemblemethoden** + - Entscheidungsbäume, Random Forests, Boosting, Voting (14.12.23) +- **Weihnachtsvorlesung** (21.12.23) +- **Neuronale Netze** + - Datenparallelität (11.01.24) + - Modellparallelität (18.01.24) + - Hyperparameteroptimierung und neurale Architektursuche (NAS) (25.01.24) + - Bioinspirierte Verfahren (01.02.24) +- **Neuartige Hardwaresysteme** + - Quantenannealer und neuromorphe Geräte (08.02.24) +--> + +#### Übungen +Die Programmierübungen werden in Form von Jupyter-Notebooks bereitgestellt und erfordern Zugriff auf ein entsprechendes HPC-System +(bwUniCluster, Zugänge werden wir zu Beginn der Vorlesung gemeinsam organisieren). +Sie orientieren sich thematisch an den Inhalten der Vorlesung. +Zusätzlich gibt es zu jeder Übung einen ergänzenden Foliensatz. + +- Übung 1: Skalierbare Programmierung (05.11.24) +- Übung 2: Paralleles k-Means Clustering (19.11.24) +- Übung 3: Paralleles Sortieren und verteilte paarweise Distanzen (03.12.24) +- Übung 4: Logistische Regression - Parallelisierung auf Ebene der Daten (17.12.24) +- Übung 5: Parallele Ensemblemethoden (14.01.25) +- Übung 6: Datenparallele neuronale Netze - AlexNet und Pytorch's DistributedDataParallel (28.01.25) +- Übung 7: Bioinspirierte Optimierungsmethoden - Partikelschwarmoptimierung und evolutionäre Algorithmen (11.02.25) + +### Sprache / Language +Deutsch / German + +### Programmiersprache /Programming language +Hauptsächlich Python (Pytorch) + +### Vorwissen / Prior knowledge +- Programmierkenntnisse, optimal Python +- Konzepte paralleler Programmierung +- Grundbegriffe künstlicher Intelligenz und maschinellen Lernens + +Hier finden Sie sämtliche Übungsmaterialien zur Vorlesung [Skalierbare Methoden der Künstlichen Intelligenz](https://ilias.studium.kit.edu/ilias.php?baseClass=ilrepositorygui&ref_id=2499244). +Die Foliensätze sämtlicher Vorlesungen und Übungen finden Sie zu gegebener Zeit im ILIAS-Portal. -- GitLab