Release Notes#

This document describes the key features, software enhancements and improvements, and known issues for DALI 1.52.0. 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 1.52.0 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:

  • Introduced experimental Dynamic Mode: imperative execution model with lazy evaluation for easier integration into Python workflows. (#6066, #6064, #6060, #6056, #6042, #6039, #6037, #6036, #5954)

    • Dynamic mode: add augmentation gallery (#6057)

    • DALI Dynamic docs main page (#6052)

  • Added pipeline ZOO - snippets and examples for common image and video processing use cases. (#5922)

  • Added support for CUDA 13U2 (#6063)

  • Added fn.decoders.numpy (#5953) and CPU fn.paste operators (#5968).

  • Exposed knobs for pipeline dynamic executor:

    • Exposed executor’s stream_policy and concurrency options. (#5983)

    • Environment variable to control executor threads. (#5949)

Fixed Issues#

This DALI release includes the following fixed issues:

  • Fixed stream ordering in Tensor::Copy and Tensor(List)GPU.as_cpu. (#6070 )

  • Fixed conversion of pinned tensors to DLPack. (#6061)

  • Fixed DLPack stride check if stride pointer is NULL. (#6023)

  • Fixed handling of videos without keyframes and reuse of old indices. (#6058)

  • Fixed resize_crop_mirror video output shape. (#5957)

Breaking Changes#

There are no breaking changes in this DALI release.

Deprecated Features#

No features were deprecated in this release.

Known Issues#

This DALI release includes the following known issues:

  • In some cases, the pass-through parallel external source outputs may be corrupted when used with pipelined dynamic executor. The issue occurs when all four conditions are met: 1. the pipeline uses dynamic executor exec_dynamic=True (default), 2. the external_source runs in parallel mode (parallel=True), 3. the ES output is directly returned from the pipeline, 4. the ES output is a single contiguous chunk of memory (either batch=True or batch_size=1). Currently, as a workaround, user can specify exec_dynamic=False when instantiating pipeline or add an extra fn.copy to prevent directly returning ES outputs from the pipeline.

  • 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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