Using DALI 1.1.0
To upgrade to DALI 1.1.0 from an older 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:
- Documentation improvements (#2834, 2824, #2758, #2820, and #2822).
- The following operators were added:
- The experimental numba_func operator that allows
the use of Numba functions in the DALI pipeline (#2804).
- The expand_dims and squeeze operators for shape
manipulation (GPU and CPU) (#2800, #2791, #2792).
- The multi_paste operator (GPU) (#2681).
- The following kernels were added:
- JPEG compression distortion (GPU) (#2801, #2830, and #2839).
- JPEG color conversion and chroma subsampling (GPU) (#2771).
- Enabled CUDA kernels compression to decrease the DALI binaries size
(#2833).
- Added the src_dims argument to the reshape operator
(#2788).
Fixed Issues
This DALI release includes the following fixes:
- Fixed a race condition in readers.nemo_asr when
pad_last_batch is set to True (#2828).
- Fixed the optical flow initialization issue (#2816).
- Fixed a race condition in the data loader (#2773).
Breaking Changes
There are no breaking changes in this DALI release.
Deprecated Features
There are no deprecated features in this DALI release.
Known Issues
This DALI release includes the following known issues:
-
The video loader operator requires that the key frames occur, at a minimum,
every 10 to 15 frames of the video stream.
If the key frames occur at a frequency that is less than 10-15 frames, the
returned frames might be out of sync.
-
The DALI TensorFlow plugin might not be compatible with
TensorFlow versions 1.15.0 and later.
To use DALI with the TensorFlow version that does not have a
prebuilt plugin binary that is shipped with DALI, make sure
that the compiler that is used to build TensorFlow exists on the system
during the plugin installation. (Depending on the particular version, you
can use GCC 4.8.4, GCC 4.8.5, or GCC 5.4.)
-
Due to some known issues with meltdown/spectra mitigations and
DALI,
DALI shows the best performance when
running in Docker with escalated privileges, for example:
- privileged=yes in Extra Settings for AWS data
points
- --privileged or --security-opt
seccomp=unconfined for bare Docker