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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 layersThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.