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Uppsats

Introducing the ModularML Framework - A transparent and modular machine learning framework made as a tool for research and education

Kandidat-uppsats

Norland, William, Bremer, Måns, Sättemo, Gabriel, Carlsson, Sander, Nilsson, Elias, Carlsson, Tim

Chalmers tekniska högskola / Institutionen för data och informationsteknik

Publicerad: 2025

Språk: Engelska

Nyckelord

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⌕Artificial Intelligence⌕machine learning⌕Computer Vision⌕Neural Network⌕framework⌕LeNet⌕AlexNet⌕MNIST⌕ImageNet⌕Research-Tool

Sammanfattning

This thesis explores the process of creating a highly modular machine learning frameworkin C++, without performance compromises. The framework can parse ONNXmodels into dynamic C++ objects, modify the implementation of core computationalfunctions (like GEMM), and use the models to perform inference. The frameworkreproduces results achieved in peer frameworks like PyTorch and TensorFlow.The usefulness of this framework stems from its pure C++ implementation, withno API layers to compiled modules or other black-box functionality. This makes ithighly suitable for use in education and research, where debuggability, modularity,and ease of use are paramount.

Information

Författare
Norland, William, Bremer, Måns, Sättemo, Gabriel, Carlsson, Sander, Nilsson, Elias, Carlsson, Tim
Lärosäte / institution
Chalmers tekniska högskola / Institutionen för data och informationsteknik
Publiceringsdatum
2025
Uppsatstyp
Kandidat-uppsats
Språk
Engelska
Nyckelord
⌕Artificial Intelligence⌕machine learning⌕Computer Vision⌕Neural Network⌕framework⌕LeNet⌕AlexNet⌕MNIST⌕ImageNet⌕Research-Tool

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