NVIDIA/DALI v1.47.0

DALI v1.47.0

on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

• Added support for DALI batched processing as a part of PyTorch DataLoader (DALI proxy):
  • Experimental DALI proxy (#5726, #5784)
  • Examples of DALI Proxy in training (#5791, #5792)
• Moved to JetPack 6.2 (CUDA 12.6) for Tegra builds (#5449)

Fixed Issues

• Fixed insufficient synchronization issue in experimental image decoder (#5806)
• Fixed memory leak in experimental video decoder (#5778)

Improvements

• Fix CVE-2024-13176 in openssl (#5805)
• Update VERSION to 1.47.0
• Make frames decoder to build index without file decoding (#5809)
• Clean up warnings (#5811)
• Move to PyPI to download PyNvVideoCodec (#5813)
• Dependency update 02/2025 (#5801)
• Use DALI as default in resnet50 example (#5808)
• Add documentation about DALI proxy in EfficientNet and ResNet examples (#5800)
• Add acknowledgements for AWS SDK C++, curl and openssl (#5794)
• Move to CUDA 12.8 (#5793)
• Move to JetPack 6.2 (CUDA 12.6) (#5449)
• Add DALI proxy option to EfficientNet example (#5791)
• Use DALI proxy to ResNet50 example. Introduce TL3_RN50_benchmark (#5792)
• Remove libavutils from the asan suppression list (#5783)
• Add a typical data loading pipeline path for the EfficeintNet (#5761)
• Remove dead executor test. (#5788)
• Fix test_dali_proxy usage (#5784)
• Fix TL1_decoder_perf usage of pip show (#5781)
• Introduce (experimental) DALI proxy (#5726)
• Move optical flow tests from specific TU test job to Ampere tests (#5771)
• Fix minor Markdown issues in ipynb in docs (#5773)

Bug Fixes

• Ensure allocated temp memory is usable by nvImageCodec streams, as we are skipping pre-sync due to unnecessary overhead in the general case (#5806)
• Remove redundant nvml::Shutdown from optical flow (#5804)
• Fix hogging memory by libaviutils (#5778)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

No features were deprecated in this release.

Known issues:

• The following operators: experimental.readers.fits, experimental.decoders.video, and experimental.inputs.video 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.
• 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 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.

Binary builds

NOTE: DALI builds for CUDA 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

CUDA 11.0 and CUDA 12.0 builds use CUDA toolkit enhanced compatibility. 
They are built with the latest CUDA 11.x/12.x toolkit respectively but they can run on the latest, 
stable CUDA 11.0/CUDA 12.0 capable drivers (450.80 or later and 525.60 or later respectively).
However, using the most recent driver may enable additional functionality. 
More details can be found in enhanced CUDA compatibility guide.

Install via pip for CUDA 12.0:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.47.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.47.0

or just:

pip install nvidia-dali-cuda120==1.47.0
pip install nvidia-dali-tf-plugin-cuda120==1.47.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.47.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.47.0

or just:

pip install nvidia-dali-cuda110==1.47.0
pip install nvidia-dali-tf-plugin-cuda110==1.47.0

Or use direct download links (CUDA 12.0):

• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda120/nvidia_dali_cuda120-1.47.0-py3-none-manylinux2014_x86_64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda120/nvidia_dali_cuda120-1.47.0-py3-none-manylinux2014_aarch64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-tf-plugin-cuda120/nvidia_dali_tf_plugin_cuda120-1.47.0.tar.gz

Or use direct download links (CUDA 11.0):

• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda110/nvidia_dali_cuda110-1.47.0-py3-none-manylinux2014_x86_64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda110/nvidia_dali_cuda110-1.47.0-py3-none-manylinux2014_aarch64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-tf-plugin-cuda110/nvidia_dali_tf_plugin_cuda110-1.47.0.tar.gz

FFmpeg source code:

• This software uses code of FFmpeg licensed under the LGPLv2.1 and its source can be downloaded here

Libsndfile source code:

• https://developer.download.nvidia.com/compute/redist/nvidia-dali/libsndfile-1.2.2.tar.gz