The DALI 0.25.0 is not yet a major release, so the features,
functionality, and performance might be limited.
Using DALI 0.25.0
To upgrade to DALI 0.25.0 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.
- Added support for the aarch64Server Base System Architecture (#2110).
Refer to Installing prebuilt DALI
packages in the DALI user guide for more
information.
- New operators:
- Normal Distribution GPU Operator (#2125)
- Video reader resize (#2097)
- Improvements to ExternalSource Op:
- Added the no_copy option, which allows DALI to
borrow a user's memory instead of copying it (#2024).
- Removed the redundant copy in the ExternalSource
operator (#2124)
- Reworked the Resize operator family, including video, channel-first, RoI,
and multiple-type support (#2164) with the new Resize tutorial (#2189).
- Bundled all python versions into one wheel (#2096).
One DALI wheel can be used with all
supported Python versions, including 3.5, 3.6, 3.7 and 3.8.
- Improved error messages and added information about the Operator of origin
(#2065).
- Extended the following C APIs to copy output and input samples:
Fixed Issues
This DALI release includes the following fixes.
- Fixed the missing layouts in the operators (#2118, #2133, #2136).
Breaking Changes
There are no breaking changes in this release.
Deprecated Features
- Removed the deprecated use of ltrb in BboxRandomCrop
(#2141).
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