DALI Release 1.9.0

Using DALI 1.9.0

To upgrade to DALI 1.9.0 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:

  • Extended the jpeg_compression_distortion operator to support video inputs (#3482 and #3447).
  • Added the file_filter argument to the readers.file operator that allows you to filter files by names (#3459).
  • Extended the slice operator to support per-sample axes arguments and negative axis indexing (#3516).
  • Extended the pad operator to support per-sample axes, fill_value arguments, and negative axis indexing (#3534)
  • Improved the performance of the slice operator for small batch sizes (#3557).
  • Added the Laplacian CPU kernel (#3565, #3535, and #3518).

Fixed Issues

This DALI release includes the following fixes:

  • Fixed a race condition that randomly caused incorrect outputs in the TensorFlow plug-in (#3547).
  • Fixed synchronization issues in the PaddlePaddle plug-in that may have caused incorrect results (#3498 and #3487).

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 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