Release Notes#

This document describes the key features, software enhancements and improvements, and known issues for DALI 1.48.0. 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 1.48.0 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 fn.experimental.decoders.video video decoder: (#5830, #5814)

    • Improved seeking and reset behavior

    • Added support for frame padding with configurable modes

    • Added frame selection options

    • Added build_index option to control the generation of a frame index

  • Added CPU support to dali.fn.experimental.warp_perspective (#5829, #5815)

  • Introduced new (experimental) C API (#5796, #5797, #5798, #5799)

Fixed Issues#

The following issues were fixed in this release:

  • Introduced AvUniquePtr to avoid memory leaks in frames decoder (#5834)

  • Removed an unnecessary host sync in operators taking pinned inputs. (#5822)

  • Fixed host-side access to pinned CPU buffers produced with non-host order (#5820)

  • Fixed handling of empty batches in GPU arithmetic operators. (#5818)

Breaking Changes#

  • There are no breaking changes in this DALI release.

Deprecated Features#

  • There are no deprecated features in this DALI release.

Improvements#

  • Fix data paths in TL3 short tests (#5845)

  • Revert change of batch size in SSD LT3 to 64 due to convergence problem (#5846)

  • Update VERSION to 1.48.0 (#5844)

  • Fix coverity issues 25/03 (#5843)

  • Bump up FFmpeg to 7.1.1 (#5838)

  • Reorganize video decoder sources (#5836)

  • Dependency update 2025-03 (#5833)

  • C API 2.0 Tensor and TensorList (#5799)

  • Update documentation of audio decoder operator (supported formats) (#5803)

  • Removes RN50 benchmark tests, move to DALI_EXTRA for RN50 DL FW iter tests (#5824)

  • Improve video decoder seeking and reset behavior (#5830)

  • Warp Perspective CPU Impl (#5829)

  • Remove ScratchpadAllocator and ScratchpadEstimator (#5810)

  • Code modernization and refactoring in Pipeline, OpSpec and InputOperator (#5826)

  • fn.experimental.decoders.video improvements (#5814)

  • C API 2.0 helpers (#5798)

  • C API 2.0 initialization and error handling (#5797)

  • Limit the max. tensor list size in TensorTest (#5823)

  • Relax DisplacementTest.Sphere constraints from 0.005 to 0.006 (#5821)

  • Restrict dm-tree version for Python 3.8 and 3.9 (#5819)

  • Add C API header and C language build test. (#5796)

  • Expose DLPack support in the docs (#5817)

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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