NVIDIA/DALI v1.28.0 on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added CUDA 12.2 support (#4930, #4938, and #4939).
Added cudaMallocAsync support (#4900, #4923, and #4921).
Improved JAX multiprocessing support (#4929, #4927, #4919, #4906, and #4920).
Added DALIRaggedIterator, a DALI Pytorch plugin iterator that supports non-uniform tensors (#4911).

Fixed Issues

No major fixes are included in this release.

Improvements

Fix OpticalFlow test premature exit on sm < 8 (#4933)
Remove dependency on forked libcudacxx (#4938)
Add JAX multinode multigpu tests (#4929)
Adding handling of non-uniform tensors in DALI Pytorch plugin (#4911)
Reworks supported Python versions (#4924)
Disable cudaMemPoolReuseAllowOpportunistic in cudaMallocAsync for <r470.60 (#4931)
Move to CUDA 12.2 (#4930)
Remove template from tensor rule-of-five for c++20 compat (#4928)
Add JAX container test job (#4927)
Extends guards against intercepting by asan certain functions (#4925)
Fix CUDA_remove_toolkit_include_dirs CMake function (#4922)
Add alignment to cuda_malloc_async_memory_resource. (#4923)
Add source_info to the tensors produced by video readers (#4916)
Add JAX multigpu sharding tests (#4919)
Add basic JAX multi process test (#4906)
Add libabseil as a runtime DALI dependency in conda (#4907)
Remove pinning Cython version from PyThon SSD test (#4913)
Add a memory resource based on cudaMallocAsync (#4900)

Bug Fixes

Fix memory_resource compilation in conda build (#4939)
Disable JAX iterator tests in ASAN build (#4920)
Fix number of devices for JAX multigpu test (#4921)
Remove unnecessary cudaDeviceSynchronize from memory resource perf test. (#4908)
Fix broken assertion in sequence operator (#4905)

Breaking API changes

DALI 1.27 was the final release that supported Python 3.6.

Deprecated features

No features were deprecated in this release.

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==1.28.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.28.0

or for CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.28.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.28.0

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-1.2.0.tar.gz

NVIDIA/DALI v1.28.0 DALI v1.28.0 on GitHub