NVIDIA/DALI v1.31.0 on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Preliminary experimental support for pipeline checkpointing. (#5061, #5057)
Added data_iterator and peekable_data_iterator decorators for simplified JAX iterators definitions. (#5050, #5049)
Added the "Training neural network with DALI and Pax" tutorial. (#5060)

Fixed Issues

The fn.permute_batch operator can now be used with the conditional execution (if expressions). (#5063)
Fixed support for videos with different bit depths in the video decoder. (#5055)
Input operators with multiple outputs can be fed with data by the operator name. (#5066)

Improvements

Expose checkpointing in Python pipeline (#5061)
Update deps: RapidJSON, OpenCV (#5079)
Fix coverity issues 10/23 (#5083)
Add Pax tutorial (#5060)
Add Efficientnet pipeline to hw_decoder_bench (#5076)
Add JAX iterator decorator (#5050)
Update libwep library to remediate CVE-2023-5129 (#5075)
Replace optional stream in SaveState with AccessOrder (#5062)
Add implicit scope to batch_permutation (#5063)
Fix enumeration formatting in conditionals docs (#5067)
Update DALI key visual (#5069)
Deprecate Python 3.7 starting DALI 1.31 (#5068)
Extend HW image decoder bench script to support multiple GPUs (#5065)
Remove the avformat_find_stream_info call from the video loader when not needed (#5047)
Add ability to serialize/deserialize Checkpoint (#5057)
Remove dali::any in favor of std::any. (#5058)
Disable container overflow sanitizer all the time (#5053)
Replace PaddlePaddle ResNet50 example with one from the DeepLearningExamples (#5048)
Make the ResNet50 example compatible with TensorFlow 2.13 (#5045)
Reorganize JAX plugin (#5049)
Replace GPU dltensor per-sample copying kernel with a batched one (#5038)
September dependency update (#5043)

Bug Fixes

Add user-friendly message about missing numpy (#5081)
Set external input by op name instead of tensor name (#5066)
Fix the support of videos with different bith dept in the video reader (#5055)
Set layout from argument in external source (#5064)
Update JAX version to 0.4.13 (#5056)
Extend Numba compatibility checks. Skip Numba GPU tests on incompatibe systems. (#5054)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

Python 3.7 support is deprecated starting from DALI 1.31.

Known issues:

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==1.31.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.31.0

or for CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.31.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.31.0

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 v1.31.0 DALI v1.31.0 on GitHub