Release Notes#

This document describes the key features, software enhancements and improvements, and known issues for DALI 1.51.2. 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 1.51.2 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 CUDA 13 and CUDA 12.9U1. (#5946)

  • Added support for nvImageCodec 0.6.0.

  • Improved CPU multithreading efficiency. (#5960, #5963, #5961)

    • Reduced lock contention on ARM CPUs.

    • Reduced number of mutex locks in ThreadPool.

    • Optimized spinlock hot path.

  • Made the new (dynamic) executor a default. (#5936, #5944)

  • Improved memory management in nvImageCodec based decoders. (#5948, #5945)

Fixed Issues#

This DALI release includes the following fixed issues:

  • Removed the requirement to make the image decoder output forcibly non-contiguous. (#5948)

  • Allocate the entire batch in nvImageCodec decoder. (#5945)

  • Set prefetch_queue_depth=1 parameter in test_crop_window_warning test pipelin. (#5938)

  • Fix ResNet50 test for DALI proxy. (#5925)

Breaking Changes#

The following breaking changes are present in this release:

  • DALI 1.50 was the last release to support CUDA 11.

  • Support for architectures of compute capability lower than 75 was dropped in CUDA 13 builds.

Deprecated Features#

No features were deprecated in this release.

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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