mlr3torch 0.2.0
Breaking Changes
- Removed some optimizers for which no fast (‘ignite’) variant
exists.
- The default optimizer is now AdamW instead of Adam.
- 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.
- 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
- Don’t use deprecated
data_formats anymore
- Added
CallbackSetTB, which allows logging that can be
viewed by TensorBoard.
mlr3torch 0.1.1
- 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