NVIDIA/DALI v1.34.0 on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added support for CUDA 12.3 U2 (#5262)
Added support for checkpointing in fn.random_resized_crop (#5246)

Fixed Issues

Fixed synchronization problem when restoring GPU random operator checkpoints (#5273).
Fixed warnings on pipeline teardown in debug mode. (#5267)
Added check for reentrant version of CFITSIO for fits reader. (#5239)
Fixed scalar inputs handling in GPU fn.lookup_table. (#5257)
Added missing validation for bboxes in fn.ssd_random_crop (#5240)
Added validation that prevents running parallel externeral source without Python workers (#5238)

Improvements

Split conda built into core and python bindings (#5259)
Dependency update - 2024/01 (#5271)
Add framework attributes to DLFW iterator tests. (#5266)
Move to CUDA 12.3 U2 (#5262)
Add links to DALI success stories in README (#5247)
Add missing imports to AA simple examples in docs (#5243)
Support checkpointing in random_resized_crop (#5246)
Add linter GitHub Action (#5236)
Adjust the error message on failed IsDenseTensor check (#5237)
Format docs directory with black (#5214)

Bug Fixes

Fix PaddlePaddle plugin docs (#5276)
Fix synchronization issues in RNG checkpointing utils (#5273)
Fix missing Shutdown method warning in debug pipeline. (#5267)
Fix issues detected by Coverity (2024.01) (#5272)
Check if reentrant version of CFITSIO is used (#5239)
Fix LookupTable GPU for scalar inputs (#5257)
Add dimension check in ssd_random_crop (#5240)
Add validation preventing running PES without Python workers (#5238)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

No features were deprecated in this release.

Known issues:

The following operators: experimental.readers.fits, experimental.decoders.video, experimental.inputs.video, and experimental.decoders.image_random_crop 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==1.34.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.34.0

or just:

pip install nvidia-dali-cuda120==1.34.0
pip install nvidia-dali-tf-plugin-cuda120==1.34.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.34.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.34.0

or just:

pip install nvidia-dali-cuda110==1.34.0
pip install nvidia-dali-tf-plugin-cuda110==1.34.0

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 v1.34.0 DALI v1.34.0 on GitHub