NVIDIA/DALI v2.4.0 on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added experimental reader support for Google Cloud Storage (gs:// paths) (#6495, #6467)
Improved arithmetic operators in dynamic mode (#6475, #6476, #6480, #6481)
Added support for CUDA 13.4U1 (#6489, #6478)
Added S3 support for webdataset index files (#6483)
Add experimental support for Python 3.15 (#6461)
Added documentation for Torchvision custom operators (#6490, #6470)
Moved from custom DALI conda recipe to upstream nvidia-dali-feedstock (#6402, #6474)

Fixed Issues

Fixed zero-extent GPU image launches (#6505)
- Thank you @arcusbuilds for your contribution!
Fixed silent integer overflows in arithmetic operators (#6481, #6480)
Fixed numpy reader returning unread sample data (#6482)
Fixed torchvision ColorJitter’s missing hue to degrees conversion (#6471)
- Thank you @Kayvan-Zahiri for your contribution
Fixed legacy viedo NVDEC reader using stale surface requirements (#6460)

Improvements

Update VERSION to 2.4.0
Bump packaging upper bound to 26.3 (#6523)
Update DALI_DEPS_VERSION (#6521)
TorchVision documentation (#6490)
Extend static analysis for arithmetic operators (#6476)
Add schema aliases. Use aliases in rename-only deprecations. (#6310)
Retry install_pip_pkg with --ignore-installed after an unrecordable uninstall (#6485)
Process workspace inputs and outputs without code duplication. (#6499)
Update DALI_deps for gRPC cross-compilation (#6495)
Document that random_shuffle shuffles only locally (#6491)
Add Google Cloud Storage (gs://) reader support (#6467)
Add S3 storage integration tests, and S3 support for webdataset index files (#6483)
Move to CUDA 13.4 U1 (#6489)
Deps update 09.2026 (#6477)
Add DALI_INT64_VEC and use it in Constant (#6486)
Update CUDA 13 Paddle package to 3.4.0 (#6488)
Improve the sharding and shuffling documentation (#6484)
Add CUDA 13.4 build configuration (#6478)
Multiple crops example (#5656)
Fix Coverity use-after-free in C API checkpointing test (#6473)
Pull latest DALI deps hardinging (#6472)
Update TorchVision API links in the documentation (#6470)
Enable data analytics directly from nvidia-theme (#6469)
Move from custom DALI conda recipe to upstream nvidia-dali-feedstock (#6402)
Add CUDA 13 Paddle packages for Python versions (#6468)
Skip Paddle iterator tests with ASan (#6465)
Respect user warning filters in Python deprecation warnings (#6463)
Fix flaky conditional merge test (#6457)
Update DALI_DEPS_VERSION (patch libsound) (#6462)
Add experimental support for Python 3.15 (#6461)
Suggest a remedy when as_array hits a non-uniform tensor list (#6464)
docs: fix minor typos in documentation (#6459)
Add Secret scanning workflow (#6390)
Enable copy-pr-bot for NVIDIA-hosted GitHub Actions runners (#6450)
Warn when S3 SSL verification or plugin preload is configured (#6442)
Disable XLA command buffers in JAX examples (#6449)
Improve documentation for framework iterators and readers (#6435)
Fix a dynamic mode thread-safety test (#6452)

Bug Fixes

Fix schema alias test on platforms which don't have BUILD_CUFILE (#6524)
Promote scalars to constants in ndd arithmetic operators (#6475)
Fix zero-extent GPU image launches (#6505)
Prevent silent integer overflows in arithmetic operators (#6481)
Fix protobuf 36 serialization in ASAN builds (#6492)
Fix int downcasting in dynamic mode (#6480)
Keep NumPy 1 for Paddle 2.6 (#6487)
Fix numpy reader returning unread sample data (#6482)
Convert torchvision ColorJitter hue to degrees for fn.color_twist (#6471)
Fix conda custom operator build and self-tests (#6474)
Update Paddle examples for Paddle 3.4 (#6446)
Prevent stale code ids in the transparent functions registry (#6458)
Reconfigure NVDEC decoder for increased surface requirements (#6460)
Restrict worker result deserialization (#6444)
Fix zero-sized batches in debug mode pipelines. (#6455)

Breaking API changes

There are no breaking changes in this DALI release.

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.

DALI builds use CUDA toolkit enhanced compatibility: 
DALI is built with the latest CUDA 12.x/13.x toolkit but can be run on any stable drivers from the respective CUDA family (525 and 580).
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==2.4.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda130==2.4.0

or just:

pip install nvidia-dali-cuda130==2.4.0
pip install nvidia-dali-tf-plugin-cuda130==2.4.0

For CUDA 12:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==2.4.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==2.4.0

or just:

pip install nvidia-dali-cuda120==2.4.0
pip install nvidia-dali-tf-plugin-cuda120==2.4.0

Or use direct download links (CUDA 13.0):

Or use direct download links (CUDA 12.0):

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

NVIDIA/DALI v2.4.0 DALI v2.4.0 on GitHub