Using DALI 1.28.0
Note: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI,
install the latest
CUDA toolkit.
To upgrade to DALI 1.28.0 from a previous 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 CUDA 12.2 support (#4930, #4938, and #4939).
- Added cudaMallocAsync support (#4900, #4923, and #4921).
- Improved JAX multiprocessing support (#4929, #4927, #4919, #4906, and #4920).
- Added DALIRaggedIterator, which is a DALI Pytorch plugin
iterator that supports non-uniform tensors (#4911).
Fixed Issues
No major fixes are included in this release.
Breaking Changes
DALI 1.27 was the final release that supported Python3.6.
Deprecated Features
No features were deprecated in this 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 experimental VideoReaderDecoder does not support open
GOP.
It will not report an error and might produce invalid frames.
VideoReader uses a heuristic approach to detect open
GOP and should work in most common cases.
-
The DALI TensorFlow plug-in might not be compatible with
TensorFlow versions 1.15.0 and later.
To use DALI with the TensorFlow version that does not have the
prebuilt plug-in binary that is shipped with DALI, ensure
that the compiler that is used to build TensorFlow exists on the system
during the plug-in installation. (Depending on the particular version, you
can use GCC 4.8.4, GCC 4.8.5, or GCC 5.4.)
- In experimental debug and eager modes, the GPU external source is not properly
synchronized with DALI internal streams.
As a workaround, you can manually
synchronize the device before returning the data from the callback.
-
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