mlr3torch: A Deep Learning Framework in R based on mlr3 and torch
Sebastian Fischer, Lukas Burk, Carson Zhang, Bernd Bischl, Martin Binder
TLDR
mlr3torch is an R package for deep learning, simplifying neural network definition, training, and evaluation within the mlr3 ecosystem.
Key contributions
- Provides an extensible deep learning framework for R's mlr3 ecosystem, built on the torch package.
- Simplifies neural network definition, training, and evaluation for various data types and tasks.
- Enables defining entire modeling workflows, including preprocessing and network architecture, as graphs.
- Seamlessly integrates with mlr3 for convenient resampling, benchmarking, and preprocessing tasks.
Why it matters
This package brings deep learning capabilities to the well-established mlr3 ecosystem in R. It streamlines complex DL workflows, making advanced neural network modeling more accessible and efficient for R users. Its graph-based approach and integration simplify research and application.
Original Abstract
Deep learning (DL) has become a cornerstone of modern machine learning (ML) praxis. We introduce the R package mlr3torch, which is an extensible DL framework for the mlr3 ecosystem. It is built upon the torch package, and simplifies the definition, training, and evaluation of neural networks for both tabular data and generic tensors (e.g., images) for classification and regression. The package implements predefined architectures, and torch models can easily be converted to mlr3 learners. It also allows users to define neural networks as graphs. This representation is based on the graph language defined in mlr3pipelines and allows users to define the entire modeling workflow, including preprocessing, data augmentation, and network architecture, in a single graph. Through its integration into the mlr3 ecosystem, the package allows for convenient resampling, benchmarking, preprocessing, and more. We explain the package's design and features and show how to customize and extend it to new problems. Furthermore, we demonstrate the package's capabilities using three use cases, namely hyperparameter tuning, fine-tuning, and defining architectures for multimodal data. Finally, we present some runtime benchmarks.
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