NVIDIA/DALI v1.50.0 on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added support for CUDA 12.9 (#5908)
Added option to disable SSL verification for S3 bucket (#5907)
Thank you @dimabasow for your contribution.
Added support for loading nvComp from a Python wheel (#5894, #5889, #5909)
Improved error messages in video loader with file name in the message (#5910)

Fixed Issues

Fixed handling of multiple frames per packet in video decoder (#5911)
Fixed sparse tensor handling in TF plugin (#5916, #5887)
Fixed serialization of default seeds in operators (#5919)
Fixed handling of empty inputs in GPU reductions (#5914)
Fixed handling of stdin descriptor in CUFileDriverScope (#5902)

Improvements

Make Python 3.10 a default version for the build.sh (#5913)
Make library bundling errors easier to find in the log. (#5915)
Move to CUDA 12.9 (#5908)
Improve error messages in video loader with file name in the message (#5910)
Add an ability to disable SSL verification for S3 bucket (#5907)
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)

Bug Fixes

Coverity check 04.2025 (#5912)
frames decoder fixes: avoid overflow, handle multiple frames per packet (#5911)
Fix sparse tensor construction in TF plugin. (#5916)
Do not serialize default seed (#5919)
Fix gpu empty reductions (#5914)
Make sure that nvComp is bundled also when WITH_DYNAMIC_CUDA_TOOLKIT is off (#5909)
Improve conda build recipe (#5905)
Fix stdin handling in CUFileDriverScope (#5902)
Remove squeezing from C API - it probably never worked anyway. (#5893)
Fix invalid stack read in legacy C API (#5892)
Use Polygon(..., closed=true) instead of Polygon(..., true) (#5842)
Fix handling of scalars in TF sparse tensors. (#5887)

Breaking API changes

DALI 1.49 was the last release to support Python 3.8

Deprecated features

Support for CUDA 11 will end in the upcoming releases.

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==1.50.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.50.0

or just:

pip install nvidia-dali-cuda120==1.50.0
pip install nvidia-dali-tf-plugin-cuda120==1.50.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.50.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.50.0

or just:

pip install nvidia-dali-cuda110==1.50.0
pip install nvidia-dali-tf-plugin-cuda110==1.50.0

Or use direct download links (CUDA 12.0):

Or use direct download links (CUDA 11.0):

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 v1.50.0 DALI v1.50.0 on GitHub