
Title: Higher-Level Interface of ‘torch’ Package to Auto-Train Neural Networks
Whether you’re generating neural network architecture expressions or
directly fitting/training models, {kindling} minimizes
boilerplate code while preserving {torch}. Since this
package uses {torch} as its backend, GPU acceleration is
supported.
{kindling} also bridges the gap between
{torch} and {tidymodels}. It works seamlessly
with {parsnip}, {recipes}, and
{workflows} to bring deep learning into your existing
{tidymodels} modeling pipeline. This enables a streamlined
interface for building, training, and tuning deep learning models within
the familiar {tidymodels} ecosystem.
Code generation of {torch} expression
Multiple architectures available
Native support for R ML workflows and pipelines (currently
{tidymodels}; {mlr3} planned)
Fine-grained control over network depth, layer sizes, and activation functions
GPU acceleration support via {torch}
tensors
You can install {kindling} on CRAN:
install.packages('kindling')Or install the development version from GitHub:
# install.packages("pak")
pak::pak("joshuamarie/kindling")
## devtools::install_github("joshuamarie/kindling")Falbel D, Luraschi J (2023). torch: Tensors and Neural Networks with ‘GPU’ Acceleration. R package version 0.13.0, https://torch.mlverse.org, https://github.com/mlverse/torch.
Wickham H (2019). Advanced R, 2nd edition. Chapman and Hall/CRC. ISBN 978-0815384571, https://adv-r.hadley.nz/.
Goodfellow I, Bengio Y, Courville A (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/.
If you use {kindling} in a publication, please cite it.
Run citation("kindling") in R to get the current citation,
or see the CITATION
file.
MIT + file LICENSE
Please note that the kindling project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.