The DALI 0.23.0 is a beta release, therefore, all features,
functionality, and performance will likely be limited.
Using DALI 0.23.0 Beta
To upgrade to DALI 0.23.0 beta 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.
-
The DALI package name now adds -cuda110 and
-cuda100 suffixes to indicate the CUDA version and
allows the hosting of all packages under one pip index.
This is important only for installation, and the DALI module in Python is
still `nvidia.dali` regardless of the CUDA version.
Refer to the Installation section in the DALI User Guide for more
information.
- New and improved Operators:
- Normalize the Operator for GPU (#1974, #1981, #1986)
-
Support for epsilon and delta degrees of freedom arguments
for the Normalize Operator (#1964)
-
SequenceRearrange Operator (#465)
-
Erase the Operator for GPU (#1971)
-
Improve how iterators count padded samples based on the reader (#1831) - the provided iterators can now query
reader for the epoch size and sharding and handle the shard size
changing from epoch-to-epoch when it's not evenly divisible by number of
shards (rank) and batch size. Refer to Advanced topics for more
information.
-
CUDA 11 build scripts for DALI were added (#2008)
Fixed Issues
This DALI release includes the following fixes.
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
lesser frequency, then the returned frames may be out of sync.
-
The DALI TensorFlow plugin may not be compatible with
TensorFlow versions 1.15.0 and/or later. If the user wants to use DALI with the TensorFlow version which doesn’t have prebuilt
plugin binary shipped with DALI it requires the gcc compiler
that matches the one used to build TensorFlow (gcc 4.8.4 or gcc, 4.8.5 or
5.4, depending on the particular version) is present on the system.
-
Due to some known issues with meltdown/spectra mitigations and
DALI,
DALI shows 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