High-Performance Open-Source Archive
lazy_tensor printing.as_lazy_tensor() when
converting 1D tensors to lazy tensors.t_opt("adamw") now actually uses AdamW and not
Adam.mlr3torch to
mlr_reflections$loaded_packages to fix errors when using
mlr3torch in parallel.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).po("nn_identity")po("nn_fn") for calling custom functions in a
network.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.y_hat).lr_one_cycle callback now infers the total number
of steps.digits for controlling
the precision with which validation/training scores are logged.TorchIngressToken now also can take a
Selector as argument features.lazy_shape() to get the shape of a lazy
tensor.LearnerTorch base class now supports the private
method $.ingress_tokens(task, param_vals) for generating
the torch::dataset.NAs and not only the batch
dimension can be missing. However, most nn() operators
still expect only one missing values and will throw an error if multiple
dimensions are unknown.NA instead.param_groups parameter.NA is now a valid shape for lazy tensorslr_reduce_on_plateau callback now works.LearnerTorchModel can now be parallelized and trained
with encapsulation activated.jit_trace now works in combination with batch
normalization.R6 version 2.6.0LearnerTorch$.dataloader() method now
operates no longer on the task but on the
dataset generated by the private
LearnerTorch$.dataset() method.shuffle parameter during model training is now
initialized to TRUE to sidestep issues where data is
sorted.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.num_interop_threads to
LearnerTorch.tensor_dataset parameter was added, which allows to
stack all batches at the beginning of training to make loading of
batches afterwards faster.PipeOp for adaptive average pooling.n_layers parameter was added to the MLP
learner.AutoTuner.epochs - patience for the
internally tuned values instead of the trained number of
epochs as it was before.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).data_formats anymoreCallbackSetTB, which allows logging that can be
viewed by TensorBoard.PipeOps such as po("trafo_resize") which
failed in some cases.LearnerTabResnet now works correctlynn() helper function to simplify the
creation of neural network layers
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