NVIDIA/DALI v1.46.0

DALI v1.46.0

on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

• Added CUDA 12.8 support
• Optimized workspace and operator specification (#5740, #5770)
• Introduced Common Subgraph Elimination for DALI pipeline/graph (#5752, #5755)
• Added support for nvImageCodec 0.4.1 (#5576, #5774, #5780)
• Improved documentation for supported environment variables (#5756)
• Made the pipeline's build call optional (#5754)

Fixed Issues

• Fixed DALIDataType printing in global namespace (for custom C++ builds) (#5748)

Improvements

• Disable nvimgcodec support for Xavier builds (#5780)
• Bump nvimagecodec to 0.4.1 version in conda build (#5774)
• Update VERSION to 1.46.0
• Optimize OpSpec and Workspace queries (#5770)
• Update CUTLASS to 3.6.0 (#5765)
• Upgrade nvImageCodec to 0.4.1 version (#5576)
• Deps update 01/2025 (#5767)
• Change the way the CUDA public key is added to the sources keyring (#5766)
• Add .as_cpu() to TensorCPU and TensorListCPU (#5751)
• Make build() optional (#5754)
• Adjust the L1 test to adhere to the normalized DALI plugin naming (#5760)
• Add a test to verify that CSE doesn't merge external source (#5755)
• Document environment variables. (#5756)
• CSE - Common Subexpression (Subgraph) Elimination (#5752)
• Add setuptools as conda build requirements (#5753)
• Normalize wheels and sdist names to have only _ as PEP 625 asks (#5750)
• Add usage of taskset in the TL1_decoder_perf (#5738)
• OpSchema major rework / deprecate default seed (#5740)
• Fix running the python-self-core-exec2 twice (#5743)

Bug Fixes

• Fix dlpack test (#5768)
• Fix CVE-2024-50612 in libsnd (#5745)
• Move operator<< and to_string for DALIDataType to the global namespace. (#5748)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

• Passing a seed argument to non-random operators is deprecated. Passing it has no effect but triggers a warning.

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==1.46.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.46.0

or just:

pip install nvidia-dali-cuda120==1.46.0
pip install nvidia-dali-tf-plugin-cuda120==1.46.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.46.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.46.0

or just:

pip install nvidia-dali-cuda110==1.46.0
pip install nvidia-dali-tf-plugin-cuda110==1.46.0

Or use direct download links (CUDA 12.0):

• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda120/nvidia_dali_cuda120-1.46.0-py3-none-manylinux2014_x86_64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda120/nvidia_dali_cuda120-1.46.0-py3-none-manylinux2014_aarch64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-tf-plugin-cuda120/nvidia_dali_tf_plugin_cuda120-1.46.0.tar.gz

Or use direct download links (CUDA 11.0):

• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda110/nvidia_dali_cuda110-1.46.0-py3-none-manylinux2014_x86_64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda110/nvidia_dali_cuda110-1.46.0-py3-none-manylinux2014_aarch64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-tf-plugin-cuda110/nvidia_dali_tf_plugin_cuda110-1.46.0.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