The DALI 0.12.0
is a beta release. Hence, for all the features, the functionality and performance will
likely be limited.
Using DALI 0.12.0 Beta
The DALI 0.12.0 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
0.12.0.
To upgrade to DALI 0.12.0 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 a new optimized implementation for CropMirrorNormalize for CPU and GPU.
- Added the ability to read video files together with labels.
Fixed Issues
This DALI release includes the following fixes.
- Resumed shipping the prebuilt version of the TensorFlow plugin as a part of
nvidia-dali-tf-plugin (as a result of fixing a compatibility
issue). With this fix, the user must first install
nvidia-dali-tf-plugin package. When this package is installed,
one of the prebuilt binaries of the TensorFlow plugin is selected. See Binary Installation.
Breaking Changes
- Source code build now requires the compiler be at least C++14 capable.
Deprecated Features
- Deprecated the following as noted below:
- _run: use schedule_run instead
- _share_outputs: use share_outputs instead, and
- _release_outputs: use release_outputs instead.
- Replaced HostDecoder and nvJPEGDecoder with generic ImageDecoder.
ImageDecoder is the recommended function for the image
decoding, and the replaced APIs HostDecoder and
nvJPEGDecoder will be removed in the future.
- NormalizePermute is replaced by the new CropMirrorNormalize.