DALI Files

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

• Added support for CUDA 13 and CUDA 12.9U1. (#5946)
• Added support for nvImageCodec 0.6.0.
• Improved CPU multithreading efficiency. (#5960, [#5963], [#5961])
• Reduced lock contention on ARM CPUs.
• Reduced number of mutex locks in ThreadPool.
• Optimized spinlock hot path.
• Made the new (dynamic) executor a default. (#5936, [#5944])
• Improved memory management in nvImageCodec based decoders (#5948, [#5945])

Improvements

• Optimize spinlock hot path. (#5961)
• Improve ThreadPool efficiency (#5963)
• Reduce the number of mutex locks in ThreadPool. (#5960)
• Fix model weight path for TL1_superres_pytorch test (#5955)
• Update VERSION to 1.51.0
• Dependencies update 06.2025 (#5951)
• Fix problem of installing numpy 2 in some tests (#5952)
• Move to CUDA 12.9U1 (#5946)
• New executor performance fixes. (#5944)
• Update TensorFlow and Numba versions in tests (#5942)
• Use std::move instead of copy where applicable (#5940)
• BLD: Silence warning from setuptools about packages config (#5939)
• Make dynamic executor the default choice. (#5936)
• Update DALI_DEPS_VERSION (#5934)
• Add a guard for out-of-range memory write in sw_scale (#5931)
• Removes duplicated document version selector (#5933)
• Update submodule dependencies (#5927)
• Enable tfrecord2idx script to convert a tfrecord from Object Storage into the index file, which is also stored in Object Storage (#5918)
• Disable conda tests when sanitizers are enabled (#5923)
• Enable conda build with AWS SDK (#5917)
• Add POST_BUILD to custom commands and include stdexcept in wrap files (#5903)
• Enable nvJPEG2k in conda build (#5920)

Bug Fixes

• Don't make the image decoder output forcibly non-contiguous. (#5948)
• nvImageCodec decoder - allocate whole batch (#5945)
• Set prefetch_queue_depth=1 parameter in test_crop_window_warning test pipeline (#5938)
• BLD: Set CMake Policy 175 to NEW (#5937)
• Suppress Warning when No Boxes in Sample (#5932)
• Fix ResNet50 test for DALI proxy (#5925)

Breaking API changes

• DALI 1.50 was the last release to support CUDA 11.
• Support for architectures of compute capability lower than 75 was dropped in CUDA 13 builds.

Deprecated features

No features were deprecated in this release.

Known issues:

• 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: 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 dynamically link the CUDA toolkit. To use DALI, please install the latest (12.x or 13.x) CUDA toolkit.

CUDA 12.0 and CUDA 13.0 builds use CUDA toolkit enhanced compatibility. 
They are built with the latest CUDA 12.x/13.x toolkit respectively but they can run on any, 
stable drivers from the respective CUDA family (525 and 580 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 13.0: pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda130==1.51.2 pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda130==1.51.2

or just:

pip install nvidia-dali-cuda130==1.51.2 pip install nvidia-dali-tf-plugin-cuda130==1.51.2

For CUDA 12: pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.51.2 pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.51.2

or just:

pip install nvidia-dali-cuda120==1.51.2 pip install nvidia-dali-tf-plugin-cuda120==1.51.2

Or use direct download links (CUDA 13.0): * https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda130/nvidia_dali_cuda130-1.51.2-py3-none-manylinux2014_x86_64.whl * https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda130/nvidia_dali_cuda130-1.51.2-py3-none-manylinux2014_aarch64.whl * https://developer.download.nvidia.com/compute/redist/nvidia-dali-tf-plugin-cuda130/nvidia_dali_tf_plugin_cuda130-1.51.2.tar.gz

Or use direct download links (CUDA 12.0): * https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda120/nvidia_dali_cuda120-1.51.2-py3-none-manylinux2014_x86_64.whl * https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda120/nvidia_dali_cuda120-1.51.2-py3-none-manylinux2014_aarch64.whl * https://developer.download.nvidia.com/compute/redist/nvidia-dali-tf-plugin-cuda120/nvidia_dali_tf_plugin_cuda120-1.51.2.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