The DALI 0.13.0
is a beta release. Hence, for all the features, the functionality and performance will
likely be limited.
Using DALI 0.13.0 Beta
The DALI 0.13.0 can be used with the 19.08 NVIDIA GPU Cloud (NGC) optimized container for
MXNet, PyTorch, and TensorFlow. Also, the 19.08 container will be shipped with DALI
0.13.0.
To upgrade to DALI 0.13.0 beta from an older version of DALI, follow the installation
instructions in the DALI Quick Start Guide.
Refer to the DALI Developer Guide for usage details.
Note: The internal DALI C++ API used for operators implementation, and the C++ API that
enables using DALI as a library from native code, are 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 new:
- Fast coco reader
- TorchPythonFunction operator
- Sink operators
- Reworked how the reader pick samples from the shuffling buffer.
- Used DALI_extra for test data.
- Added checks to see if Python API is not mixed between simple, scheduled and
iterator APIs.
- Added support for reading video files with labels using
file_list argument.
Fixed Issues
This DALI release includes the following fixes.
- Restored support of use_batched_decode argument in
nvJPEGDecoder operator (only for legacy nvJPEGDecoder
implementation).
- Fixed FP16 support in DALI TensorFlow plugin.
- Fixed Python operator with side effects.
- Fixed a race condition in async pipeline executor.
- Disabled video_reader_op test when NVDEC is disabled.
- Fixed sampling of chroma in the VideoReader operation.
- Fix detection pipeline example.
Breaking Changes
- Reader sampling from shuffling buffer was adjusted. Now samples are not mixed between
epochs.
Deprecated Features
- Deprecated NormalizePermute in favor of
CropMirrorNormalize
- Multiple Input Sets handling was removed from backend and is only Python level
syntactic sugar.