DALI Release 1.25.0
Using DALI 1.25.0
Note: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI,
install the latest CUDA toolkit.
To upgrade to DALI 1.25.0 from a previous version of DALI, follow the installation and usage information in the DALI User Guide.
Note: The internal DALI C++ API used for operator’s implementation, and
the C++ API that enables using DALI as a library from native code, is
not yet officially supported. Hence these APIs may change in the next release
without advance notice.
Key Features and Enhancements
This DALI release includes the following key features and enhancements:
- Added the experimental flexible image transport system (FITS) reader (fn.experimental.readers.fits) for the CPU backend (#4591).
- Added the CPU backend for the histogram equalization operator (fn.experimental.equalize) (#4742).
- Added the CPU backend for the 2-D convolution for images and video (fn.experimental.filter) (#4764).
- Added support for feeding pipeline inputs as named arguments in Pipeline.run() (#4712).
- Improved the automatic augmentations and conditional execution in the
following ways:
- Support for CPU inputs in predefined automatic augmentations (#4772).
- Reduced memory consumption (#4697).
- Support for conditional execution in debug mode (#4738).
- EfficientNet training example with DALI AutoAugment (#4678).
- More predefined policies for AutoAugment (#4753).
- Support for numerical types in the if predicate and not expression (#4715).
- Operator improvements:
- Added support for booleans in the DALI iterator for PyTorch (#4757).
Fixed Issues
The following issues were fixed in this release:
- Fixed possible hangs on a pipeline build or teardown when using fn.experimental.decoder.image (#4727).
- Fixed D2D copy synchronization that might result in fn.experimental.decoders.video returning incorrect frames for high-resolution videos (#4717).
- Fixed buffer exhaustion in fn.experimental.decoder.image (#4723).
- Fixed GPU unary arithmetic operators (for example, math.abs and math.floor) incorrectly processing non-scalar samples (#4746).
- Fixed host JPEG decoder leaking memory on incorrect files (#4748).
- Fixed missing source information in the numpy reader output (#4714).
- Fixed error message in assertion in base_iterator.py (#4726).