DALI Release 1.13.0

Using DALI 1.13.0

To upgrade to DALI 1.13.0 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 per-frame (temporal) arguments to the Gaussian Blur and Laplacian operators (#3715 and #3723).
  • Optimized audio decoder resampling for ARM (#3745).
  • Improved the debug (immediate execution) mode:
    • Added direct operator calls in debug mode (#3734).
    • Added a debug mode benchmark (#3762).
  • Added support for GPU positional arguments in the Slice operator (#3741).
  • Documentation improvements:
    • Split the operator documentation into separate pages (#3794).
    • Added a mechanism for cross-referencing examples and operators (#3748).
    • Added an FAQ section to the DALI user guide (#3761).
    • Added new GTC talks (#3757).
    • Added shuffling and shards handling snippets to the parallel external source examples (#3744).

Fixed Issues

Breaking Changes

There are no breaking changes in this DALI release.

Fixed Issues

The following issues were fixed in this release:

Fixed the handling of samples that exceed 2GBs in the parallel external source (#3768).

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 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.)

  • 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