DALI Release 0.22.0 Beta

The DALI 0.22.0 is a beta release, therefore, all features, functionality, and performance will likely be limited.

Using DALI 0.22.0 Beta

To upgrade to DALI 0.22.0 beta 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.

  • DALI now support CUDA 11:
    • DALI builds for CUDA 11 are now available.
    • CUDA 9 support has been deprecated.

      DALI 0.22.0 is final release that provides a CUDA 9 build.

  • Support is now available for the Ampere Hardware JPEG decoder.

    Refer to Loading Data Fast with DALI and the New Hardware JPEG Decoder in NVIDIA A100 GPUs for more information.

  • The following new operators are now available:

    • NumpyReader, which allows you to read standard .npy (NumPy) files (#1858).

    • CoordFlip for CPU and GPU (#1894 and #1895).

  • Readers can be set to read files directly instead of using mmap, which improves network filesystem performance (#1909).

  • DALI can be built as CMake subproject (#1924).

Fixed Issues

This DALI release includes the following fixes.

  • Fixed the jitter operator illegal memory access issue (#1914).

Deprecated Features

  • CUDA 9 support has been deprecated.

    DALI 0.22.0 is the last release that provides a CUDA 9 build.

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 lesser frequency, then the returned frames may be out of sync.

  • The DALI TensorFlow plugin may not be compatible with TensorFlow versions 1.15.0 and/or later. If the user wants to use DALI with the TensorFlow version which doesn’t have prebuilt plugin binary shipped with DALI it requires the gcc compiler that matches the one used to build TensorFlow (gcc 4.8.4 or gcc, 4.8.5 or 5.4, depending on the particular version) is present on the system.

  • Due to some known issues with meltdown/spectra mitigations and DALI, DALI shows 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