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Changelog

Source: NEWS.md

mlr3torch (development version)

Breaking changes

  • The freq_type parameter of t_clbk("checkpoint") was removed and checkpointing is always per epoch.
  • The construction argument only_batch_unknown of PipeOpTorch was removed, as shape inference functions are now expected to handle multiple unknown dimensions.
  • The dropout probability p of lrn("classif.mlp") / lrn("regr.mlp") is now initialized to 0.1 instead of 0.5. Set p = 0.5 explicitly to keep the old behaviour.
  • The cache argument of materialize() is now a utils::hashtab() instead of an environment(), which avoids possible hash collisions and raises the R dependency to >= 4.2.0.
  • The num_interop_threads parameter of LearnerTorch is no longer initialized to 1, so torch’s default is left in place unless the parameter is set. Setting it to a value that torch can no longer apply is now an error instead of a warning.

Features

  • The $model of a LearnerTorch now has a printer.
  • Added more image learners from {torchvision}.
  • Most LearnerTorchVision are now jittable.
  • Ported the TabM tabular learner from Python.
  • LearnerTorch now has .loss_fn(task, param_vals) private method that allows to customize the construction of the loss function.
  • LearnerTorch now has restore_best_weights parameter that can be used when early stopping is active.
  • A network can now return more than one prediction during training as a list. The first is expected to be the primary prediction. In ContextTorch, $y_hat is the primary prediction and $y_hats contains the complete prediction.
  • New parameter batch_size_predict for LearnerTorch, which overrides batch_size for prediction
  • Added multihead attention and transformer encoder pipeops.
  • Any dimension of an input shape can now be unknown (NA), not only the batch dimension.
  • Improved error messages during PipeOpTorch’s shape inference.
  • The shape parameter of nn("reshape") can now be a function(shape) of the input shape.
  • Exported various helpers useful for implementing shape inference for custom PipeOpTorch classes.
  • ContextTorch has a new field $callbacks, which gives a callback access to the other callbacks of the training run.
  • CallbackSet has a new field $weight that controls when a callback is called within a stage.
  • t_clbk("checkpoint") now accepts an existing empty directory as its path
  • The path of t_clbk("checkpoint") can now be a function() that is called at the beginning of each training run and returns that run’s path.

Bug fixes

  • Fixed some hashing bugs related to R jit compilation.
  • ContextTorch$epoch is now 0 during the on_begin stage instead of NULL.
  • t_clbk("checkpoint") no longer writes an epoch that was interrupted under that epoch’s own number, so network<n>.pt is now always the network at the end of epoch n rather than sometimes a half-trained one.
  • replace_head() for mobilenet_v2 and VGG works for width_mult above 1.
  • PipeOpTorch$shapes_out() now always returns integer() shapes (and not sometimes doubles like NA).
  • po("torch_model_classif") and po("torch_model_regr") now have the correct $packages.
  • The batch_sampler parameter can now be used without setting batch_size for training.
  • Configuration errors that are only caught during LearnerTorch no longer trigger a fallback learner.
  • The LearnerTorch’s sampler and batch_sampler parameters are now not used during prediction.
  • logical() features are now encoded as 1-based instead of 1-based.
  • lazy_tensor columns are now again printed correctly inside data.tables
  • Fixed some links on the pkgdown website and the help pages.
  • Fixed various other shape inference bugs.
  • po("torch_model_{regr, classif}") now resets the parameters of the network at the beginning of $train() when the network is built from PipeOpTorch objects, which makes the results reproducible for the set seed parameter.
  • nn() now properly interprets nn("linear_1") as po("nn_linear", id = "linear_21").
  • Fixed some bugs in FTTransformer: attention_initialization now has an effect, n_blocks = 0 is allowed and the hidden dimension falls back to d_token * 4/3 as in the reference implementation.
  • Fixed some issues in the documentation.
  • Examples, vignettes and the README now use nn("linear") instead of the equivalent, but longer po("nn_linear").

mlr3torch 0.3.3

CRAN release: 2026-01-31

  • Feat: Improve lazy_tensor printing.
  • Fix: Improve consistency in as_lazy_tensor() when converting 1D tensors to lazy tensors.
  • Various minor bug fixes

mlr3torch 0.3.2

CRAN release: 2025-10-31

Bug Fixes

  • t_opt("adamw") now actually uses AdamW and not Adam.
  • Caching: Cache directory is now created, even if its parent directory does not exist.
  • Add mlr3torch to mlr_reflections$loaded_packages to fix errors when using mlr3torch in parallel.

mlr3torch 0.3.1

CRAN release: 2025-08-26

Bug Fixes

  • FT Transformer can now be (un-)marshaled after being trained on categorical data (#412).
  • Parameters (batch)-sampler now work (#420, thanks @tdhock)

Features

  • Better error messages.

mlr3torch 0.3.0

CRAN release: 2025-07-07

Breaking Changes:

  • The output dimension of neural networks for binary classification tasks is now expected to be 1 and not 2 as before. The behavior of nn("head") was also changed to match this. This means that for binary classification tasks, t_loss("cross_entropy") now generates nn_bce_with_logits_loss instead of nn_cross_entropy_loss. This also came with a reparametrization of the t_loss("cross_entropy") loss (thanks to @tdhock, #374).

New Features:

PipeOps & Learners:

  • Added po("nn_identity")
  • Added po("nn_fn") for calling custom functions in a network.
  • Added the FT Transformer model for tabular data.
  • Added encoders for numericals and categoricals
  • nn("block") (which allows to repeat the same network segment multiple times) now has an extra argument trafo, which allows to modify the parameter values per layer.

Callbacks:

  • The context for callbacks now includes the network prediction (y_hat).
  • The lr_one_cycle callback now infers the total number of steps.
  • Progress callback got argument digits for controlling the precision with which validation/training scores are logged.

Other:

  • TorchIngressToken now also can take a Selector as argument features.
  • Added function lazy_shape() to get the shape of a lazy tensor.
  • Better error messages for MLP and TabResNet learners.
  • TabResNet learner now supports lazy tensors.
  • The LearnerTorch base class now supports the private method $.ingress_tokens(task, param_vals) for generating the torch::dataset.
  • Shapes can now have multiple NAs and not only the batch dimension can be missing. However, most nn() operators still expect only one missing values and will throw an error if multiple dimensions are unknown.
  • Training now does not fail anymore when encountering a missing value during validation but uses NA instead.
  • It is now possible to specify parameter groups for optimizers via the param_groups parameter.

Bug Fixes:

  • fix: lazy tensors of length 0 can now be materialized.
  • fix: NA is now a valid shape for lazy tensors
  • fix: The lr_reduce_on_plateau callback now works.

mlr3torch 0.2.1

CRAN release: 2025-02-13

Bug Fixes:

  • LearnerTorchModel can now be parallelized and trained with encapsulation activated.
  • jit_trace now works in combination with batch normalization.
  • Ensures compatibility with R6 version 2.6.0

mlr3torch 0.2.0

CRAN release: 2025-02-07

Breaking Changes

  • Removed some optimizers for which no fast (‘ignite’) variant exists.
  • The private LearnerTorch$.dataloader() method now operates no longer on the task but on the dataset generated by the private LearnerTorch$.dataset() method.
  • The shuffle parameter during model training is now initialized to TRUE to sidestep issues where data is sorted.

Performance Improvements

  • Optimizers now use the faster (‘ignite’) version of the optimizers, which leads to considerable speed improvements.
  • The jit_trace parameter was added to LearnerTorch, which when set to TRUE can lead to significant speedups. This should only be enabled for ‘static’ models, see the torch tutorial for more information.
  • Added parameter num_interop_threads to LearnerTorch.
  • The tensor_dataset parameter was added, which allows to stack all batches at the beginning of training to make loading of batches afterwards faster.
  • Use a faster default image loader.

Features

  • Added PipeOp for adaptive average pooling.
  • The n_layers parameter was added to the MLP learner.
  • Added multimodal melanoma and cifar{10, 100} example tasks.
  • Added a callback to iteratively unfreeze parameters for finetuning.
  • Added different learning rate schedulers as callbacks.

Bug Fixes:

  • Torch learners can now be used with AutoTuner.
  • Early stopping now not uses epochs - patience for the internally tuned values instead of the trained number of epochs as it was before.
  • The dataset of a learner must no longer return the tensors on the specified device, which allows for parallel dataloading on GPUs.
  • PipeOpBlock should no longer create ID clashes with other PipeOps in the graph (#260).

mlr3torch 0.1.2

CRAN release: 2024-10-15

  • Don’t use deprecated data_formats anymore
  • Added CallbackSetTB, which allows logging that can be viewed by TensorBoard.

mlr3torch 0.1.1

CRAN release: 2024-09-12

  • fix(preprocessing): regarding the construction of some PipeOps such as po("trafo_resize") which failed in some cases.
  • fix(ci): tests were not run in the CI
  • fix(learner): LearnerTabResnet now works correctly
  • Fix that tests were not run in the CI
  • feat: added the nn() helper function to simplify the creation of neural network layers

mlr3torch 0.1.0

CRAN release: 2024-07-08

  • Initial CRAN release