DALI Release 1.25.0

Using DALI 1.25.0

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

To upgrade to DALI 1.25.0 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 experimental flexible image transport system (FITS) reader (fn.experimental.readers.fits) for the CPU backend (#4591).
  • Added the CPU backend for the histogram equalization operator (fn.experimental.equalize) (#4742).
  • Added the CPU backend for the 2-D convolution for images and video (fn.experimental.filter) (#4764).
  • Added support for feeding pipeline inputs as named arguments in Pipeline.run() (#4712).
  • Improved the automatic augmentations and conditional execution in the following ways:
    • Support for CPU inputs in predefined automatic augmentations (#4772).
    • Reduced memory consumption (#4697).
    • Support for conditional execution in debug mode (#4738).
    • EfficientNet training example with DALI AutoAugment (#4678).
    • More predefined policies for AutoAugment (#4753).
    • Support for numerical types in the if predicate and not expression (#4715).
  • Operator improvements:
    • Improved the performance of CPU brightness and contrast operators for uint8 samples (#4737).
    • Improved the fn.readers.webdataset performance (#4708).
    • Support booleans in fn.readers.numpy (#4745).
  • Added support for booleans in the DALI iterator for PyTorch (#4757).

Fixed Issues

The following issues were fixed in this release:
  • Fixed possible hangs on a pipeline build or teardown when using fn.experimental.decoder.image (#4727).
  • Fixed D2D copy synchronization that might result in fn.experimental.decoders.video returning incorrect frames for high-resolution videos (#4717).
  • Fixed buffer exhaustion in fn.experimental.decoder.image (#4723).
  • Fixed GPU unary arithmetic operators (for example, math.abs and math.floor) incorrectly processing non-scalar samples (#4746).
  • Fixed host JPEG decoder leaking memory on incorrect files (#4748).
  • Fixed missing source information in the numpy reader output (#4714).
  • Fixed error message in assertion in base_iterator.py (#4726).

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