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Release notes

2.1.8

New Features

Bugs Squashed

  • Update for fastcore negate_func->not_
  • LR too high for gradient accumulation (#3040), thanks to @marii-moe
  • Torchscript transforms incompatibility with nn.Sequential (#2920)

2.1.7

New Features

  • Pytorch 1.7 subclassing support (#2769)

Bugs Squashed

  • unsupported operand type(s) for +=: 'TensorCategory' and 'TensorText' when using AWD_LSTM for text classification (#3027)
  • UserWarning when using SaveModelCallback() on after_epoch (#3025)
  • Segmentation error: no implementation found for 'torch.nn.functional.cross_entropy' on types that implement torch_function (#3022)
  • TextDataLoaders.from_df() returns TypeError: 'float' object is not iterable (#2978)
  • Internal assert error in awd_qrnn (#2967)

2.1.6

New Features

  • Option to preserve filenames in download_images (#2983), thanks to @mess-lelouch
  • Deprecate config in create_cnn and instead pass kwargs directly (#2966), thanks to @borisdayma

Bugs Squashed

  • Progress and Recorder callbacks serialize their data, resulting in large Learner export file sizes (#2981)
  • TextDataLoaders.from_df() returns TypeError: 'float' object is not iterable (#2978)
  • "only one element tensors can be converted to Python scalars" exception in Siamese Tutorial (#2973)
  • Learn.load and LRFinder not functioning properly for the optimizer states (#2892)

2.1.5

Breaking Changes

  • remove log_args (#2954)

New Features

  • Improve performance of RandomSplitter (h/t @muellerzr) (#2957)

Bugs Squashed

  • Exporting TabularLearner via learn.export() leads to huge file size (#2945)
  • TensorPoint object has no attribute img_size (#2950)

2.1.4

Breaking Changes

  • moved has_children from nn.Module to free function (#2931)

New Features

  • Support persistent workers (#2768)

Bugs Squashed

  • unet_learner segmentation fails (#2939)
  • In "Transfer learning in text" tutorial, the "dls.show_batch()" show wrong outputs (#2910)
  • Learn.load and LRFinder not functioning properly for the optimizer states (#2892)
  • Documentation for Show_Images broken (#2876)
  • URL link for documentation for torch_core library from the doc() method gives incorrect url (#2872)

2.1.3

Bugs Squashed

  • Work around broken PyTorch subclassing of some new_* methods (#2769)

2.1.0

New Features

  • PyTorch 1.7 compatibility (#2917)

PyTorch 1.7 includes support for tensor subclassing, so we have replaced much of our custom subclassing code with PyTorch's. We have seen a few bugs in PyTorch's subclassing feature, however, so please file an issue if you see any code failing now which was working before.

There is one breaking change in this version of fastai, which is that custom metadata is now stored directly in tensors as standard python attributes, instead of in the special _meta attribute. Only advanced customization of fastai OO tensors would have used this functionality, so if you do not know what this all means, then it means you did not use it.

2.0.19

This version was released after 2.1.0, and adds fastcore 1.3 compatibility, whilst maintaining PyTorch 1.6 compatibility. It has no new features or bug fixes.

2.0.18

Forthcoming breaking changes

The next version of fastai will be 2.1. It will require PyTorch 1.7, which has significant foundational changes. It should not require any code changes except for people doing sophisticated tensor subclassing work, but nonetheless we recommend testing carefully. Therefore, we recommend pinning your fastai version to <2.1 if you are not able to fully test your fastai code when the new version comes out.

Dependencies

  • pin pytorch (<1.7) and torchvision (<0.8) requirements (#2915)
  • Add version pin for fastcore
  • Remove version pin for sentencepiece

2.0.16

New Features

  • added support for tb projector word embeddings (#2853), thanks to @floleuerer
  • Added ability to have variable length draw (#2845), thanks to @marii-moe
  • add pip upgrade cell to all notebooks, to ensure colab has current fastai version (#2843)

Bugs Squashed

  • fix TabularDataLoaders inference of cont_names to keep y_names separate (#2859), thanks to @sutt

2.0.15

Breaking Changes

  • loss functions were moved to loss.py (#2843)

2.0.14

New Features

  • new callback event: after_create (#2842)

    • This event runs after a Learner is constructed. It's useful for initial setup which isn't needed for every fit, but just once for each Learner (such as setting initial defaults).

  • Modified XResNet to support Conv1d / Conv3d (#2744), thanks to @floleuerer

    • Supports different input dimensions, kernel sizes and stride (added parameters ndim, ks, stride). Tested with fastai_audio and fastai time series with promising results.

Bugs Squashed

  • img_size attribute for TensorPoint is not updated properly (#2799), thanks to @IRailean

2.0.13

Bugs Squashed

  • Undo breaking num_workers fix (#2804)
    • Some users found the recent addition of num_workers to inference functions was causing problems, particularly on Windows. This PR reverts that change, until we find a more reliable way to handle num_workers for inference.
  • learn.tta() fails on a learner imported with load_learner() (#2764)
  • learn.summary() crashes out on 2nd transfer learning (#2735)

2.0.12

Bugs Squashed

  • Undo breaking num_workers fix (#2804)

2.0.11

Bugs Squashed

  • Fix cont_cat_split for multi-label classification (#2759)
  • fastbook error: "index 3 is out of bounds for dimension 0 with size 3" (#2792)

2.0.10

New Features

  • update for fastcore 1.0.5 (#2775)

2.0.6

New Features

  • "Remove pandas min version requirement" (#2765)
  • Modify XResNet to support Conv1d / Conv3d (#2744)
    • Also support different input dimensions, kernel sizes and stride (added parameters ndim, ks, stride).
  • Add support for multidimensional arrays for RNNDropout (#2737)
  • MCDropoutCallback to enable Monte Carlo Dropout in fastai. (#2733)
    • A new callback to enable Monte Carlo Dropout in fastai in the get_preds method. Monte Carlo Dropout is simply enabling dropout during inference. Calling get_preds multiple times and stacking them yield of a distribution of predictions that you can use to evaluate your prediction uncertainty.
  • adjustable workers in get_preds (#2721)

Version 2.0.0

  • Initial release of v2