brulee vs dbt Core

brulee fits neural networks for tidymodels on torch, and 1.0.0 redefined what that means: alongside the original MLP it now ships Regularization Learning Networks, ResNet with skip connections and batch normalization, AutoInt with columnwise attention, SAINT with row and column attention, and Chronos2, a foundational forecasting model. GPU acceleration arrived in the same release with automatic CUDA selection and opt-in MPS. Version 1.1.0 added TabICL, an open-source tabular foundation model, and 1.1.1 spent its time cleaning up the consequences of shipping models that need weight downloads.

The package has crossed from a torch convenience wrapper into a catalog of current tabular architectures, and the recent releases show it absorbing what that costs. Pretrained weights meant a 400MB download, so 1.1.1 stopped fetching them on attach and moved the cache to the platform-appropriate R_user_dir location. Numerical robustness is the other constant thread — 64-bit tensors, Gaussian initialization, gradient clipping extended architecture by architecture, and a ResNet batch-normalization bug where a single-row trailing batch produced all-NA predictions.

Gradient clipping has been rolled out one architecture at a time and TabICL is the newest arrival, so the likely next step is bringing the foundation models to parity with the trained ones on device selection, prediction types, and the tuning surface.