DALI Release 1.29.0

Using DALI 1.29.0

Note: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

To upgrade to DALI 1.29.0 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 the fn.experimental.median_blur GPU operator (#4950, #4975).
  • Improved JAX support:
    • Added support for jax.Sharding to dali.plugin.jax.DALIGenericIterator (#4969).
    • Improved examples and tutorials (#4973, #4956, #4944, #4937).
  • Optimized the HWC to the CHW transposition variant of the fn.crop_mirror_normalize operator (#4972).
  • Moved to CUDA 12.2U1 (#4966).

Fixed Issues

The following fixes are included in this release:
  • Fixed layout broadcasting in arithmetic expressions (#4951).
  • Added the missing layout propagation in fn.reductions (#4947).

Breaking Changes

There are no breaking changes in this release.

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