Release Notes

This document describes the key features, software enhancements and improvements, and known issues for DALI 1.39.0. For previously released DALI documentation, see DALI Archives.


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


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

To upgrade to DALI 1.39.0 from a previous version of DALI, follow the installation and usage information in the DALI User Guide.


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 12.5 (5478).

  • Added support for CUDA 12.5 (5470).

  • Improved error handling (5466, 5494, 5486, and 5491).

Fixed Issues

The following fixes are included in this release:

  • Fixed DALI TF plugin compatibility with TensorFlow 2.9 (5499).

  • Fixed S3 fn.readers.file support for pad_last_batch=True (5493).

  • Fixed a bug that resulted in long build times for some pipelines with enabled conditional execution. (5475).

Breaking Changes

There are no breaking changes in this release.

Deprecated Features

The following feature has been deprecated:

  • DALI 1.39.0 is the final release that will support the MXNet integration.

Known Issues

This DALI release includes the following known issues:

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

  • 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.

  • 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