Release Notes#

This document describes the key features, software enhancements and improvements, and known issues for DALI 1.50.0. 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 1.50.0 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 support for CUDA 12.9 (#5908).

  • Included the option to disable SSL verification for S3 bucket (#5907).

  • Added support for loading nvComp from a Python wheel (#5894, #5889, #5909).

  • Improved the error messages in video loader by including the file name in the message (#5910).

Fixed Issues#

The following issues were fixed in this release:

  • Handling multiple frames per packet in video decoder (#5911).

  • Sparse tensor handling in TF plugin (#5916, #5887).

  • Serialization of default seeds in operators (#5919).

  • Handling of empty inputs in GPU reductions (#5914).

  • Fixed handling of stdin descriptor in CUFileDriverScope (#5902).

Breaking Changes#

The following breaking changes are present in this release:

  • DALI 1.49 was the last release to support Python 3.8.

Deprecated Features#

The following features are flagged for deprecation as of this release:

  • Support for CUDA 11 will end in an upcoming release. Further details will be provided in future release notes.

Improvements#

  • Make Python 3.10 a default version for the build.sh (#5913).

  • Make library bundling errors easier to find in the log (#5915).

  • Migrate DALI TF plugin to C API 2.0 (#5904).

  • BLD: Use CMake nvimgcodec module if available to get headers (#5906).

  • C API changes required for TF plugin (#5898).

  • Remove redundant imports from the augmentation_gallery (#5900).

  • Move to externally provided nvComp (#5894).

  • Remove Python 3.8 support due to EOL (#5896).

  • Extend EfficientNet readme (#5895).

  • Fix memory consumption by PyTorch in dlpack zero-copy perf test (#5891).

  • Add handling for NVMLError_NotSupported in get_device_memory_info (#5890).

  • Enable nvComp for SBSA platform (#5889).

  • Experimental video reader to drop frames with negative display timestamps (#5885).

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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