Release Notes#

This document describes the key features, software enhancements and improvements, and known issues for DALI 2.3.0. 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 dynamically link the CUDA toolkit. To use DALI, install the latest (12.x or 13.x) CUDA toolkit.

To upgrade to DALI 2.3.0 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:

  • Improved capture mode (transparent pipelining) (#6445, #6441, #6434, #6440, #6420, #6388)

    • Added support for tracing random ops and external source (#6423, #6401)

    • Added ndd.compile.invariant (#6429)

  • Improved dynamic mode skill (#6447, #6389, #6421, #6448)

  • Added support for nvImageCodec 0.9.0 (#6426)

  • Added support for CUDA 13.3U1 (#6416)

  • Improved StdCUFileStream (avoid unnecessary creation of buffered file-descriptors) (#6386)

Fixed Issues#

This DALI release includes the following fixed issues:

  • Fixed insufficient validation of NPY shape sizes (#6443)

  • Fixed wds2idx parsing with spaced tar owners and non-ASCII names (#6422, #6409)

  • Fixed insufficient validation of RecordIO record length (#6406)

  • Fixed parallel external source forwarding recycled output buffers (#6413)

  • Added a check to reject non-string COCO file names (#6415)

  • Fixed 0d reader tensor args handling in dynamic readers (#6398)

  • Fixed insufficient validation of TFRecord payload length (#6405)

  • Fixed malformed numpy headers parsing (#6410)

  • Fixed external source prefetch drain with separated async executor (#6397)

  • Added support for full-range VP9 video decoding (#6367)

Breaking Changes#

This DALI release does not include any breaking changes.

Deprecated Features#

This DALI release includes the following deprecated features:

  • Deprecated experimental support for Python 3.13t.

Known Issues#

This DALI release includes the following known issues:

  • A problem with insufficient static TLS allocation size has been observed on Ubuntu 22.04 for aarch64 that can result in process crash when loading dynamic libraries. Updating glibc to 2.39 or newer, or specifying higher static TLS size with GLIBC_TUNABLES=glibc.rtld.optional_static_tls=10000 should resolve the issue.

  • 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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