Release Notes#

This document describes the key features, software enhancements and improvements, and known issues for DALI 1.47.0. For previously released DALI documentation, see DALI Archives.

Overview#

DALI offers both performance and flexibility of accelerating different data pipelines (graphs that can have multiple outputs and inputs), as a single library, that can be easily integrated into different deep learning training and inference applications.

Using DALI#

Note

DALI builds for NVIDIA® CUDA® 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

To upgrade to DALI 1.47.0 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 support for DALI batched processing as a part of PyTorch DataLoader (DALI proxy):

  • Moved to JetPack 6.2 (CUDA 12.6) for Tegra builds (#5449)

Fixed Issues#

The following issues were fixed in this release:

  • Fixed insufficient synchronization issue in experimental image decoder (#5806)

  • Fixed memory leak in experimental video decoder (#5778)

Breaking Changes#

  • There are no breaking changes in this DALI release.

Deprecated Features#

  • There are no deprecated features in this DALI release.

Improvements#

  • Fix CVE-2024-13176 in openssl (#5805)

  • Update VERSION to 1.47.0 (#5809)

  • Make frames decoder to build index without file decoding (#5809)

  • Clean up warnings (#5811)

  • Move to PyPI to download PyNvVideoCodec (#5813)

  • Dependency update 02/2025 (#5801)

  • Use DALI as default in resnet50 example (#5808)

  • Add documentation about DALI proxy in EfficientNet and ResNet examples (#5800)

  • Add acknowledgements for AWS SDK C++, curl and openssl (#5794)

  • Move to CUDA 12.8 (#5793)

  • Move to JetPack 6.2 (CUDA 12.6) (#5449)

  • Add DALI proxy option to EfficientNet example (#5791)

  • Use DALI proxy to ResNet50 example. Introduce TL3_RN50_benchmark (#5792)

  • Remove libavutils from the asan suppression list (#5783)

  • Add a typical data loading pipeline path for the EfficeintNet (#5761)

  • Remove dead executor test (#5788)

  • Fix test_dali_proxy usage (#5784)

  • Fix TL1_decoder_perf usage of pip show (#5781)

  • Introduce (experimental) DALI proxy (#5726)

  • Move optical flow tests from specific TU test job to Ampere tests (#5771)

  • Fix minor Markdown issues in ipynb in docs (#5773)

Known Issues#

This DALI release includes the following known issues:

  • The following operators do not currently support checkpointing: experimental.readers.fits, experimental.decoders.video, experimental.inputs.video, and experimental.decoders.image_random_crop.

  • 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 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 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.)

  • 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

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