Release Notes

This document describes the key features, software enhancements and improvements, and known issues for DALI 1.40.0. 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 1.40.0 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 operators: fn.zeros , fn.zeros_like , fn.ones , fn.ones_like , fn.full, and fn.full_like (5505).

  • Added support for H264, H265, and AV1 video formats to fn.plugin.video (5504).

  • Added support for CUDA 12.5U1 (5545).

Fixed Issues

Fixed following issues with S3 files reading:

  • Fixed handling of file names with whitespaces in TFRecord reader (5525).

  • Fixed loading when no GPU is available (5533).

  • Fixed handling of TFrecord index file (5515).

Breaking Changes

  • DALI 1.39.0 was the final release to support the MXNet integration.

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, experimental.decoders.video, experimental.inputs.video, 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