DALI Release 1.4.0

Using DALI 1.4.0

To upgrade to DALI 1.4.0 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:

  • readers.numpy improvements:
    • Added ROI support in the GPU operator (#3034 and #3040).
    • Parallelized reading in the CPU operator (#3077).
    • Added a tutorial (#3095 and #3139).
  • DALI Dataset improvements:
  • Video reader improvements:
    • Added an option to pad missing frames at the end of sequence (#3002).
    • Added support for the VP8 and MJPEG formats (#3045).
  • Added CPU parallelization to the Slice and SliceFlipNormalizePermutePad kernels (#3062, #3068, and #3080).
  • Added an option to readers.nemo_asr to return indices of the entries in the manifest (#3085).
  • Improved the performance in the GPU image decoder by optimizing the memory allocations. (#3067).

Fixed Issues

This DALI release includes the following fixes:

  • Fixed a crash that happened when a functools.partial result was passed as a `source` to external_source (#3143).
  • Fixed the hardware image decoder to fall back to the hybrid implementation for unsupported file formats instead of throwing an error (#3086).

Breaking Changes

There are no breaking changes in this DALI release.

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 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