NVIDIA/DALI v1.21.0 on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added experimental image decoding operators with support for the following higher dynamic ranges (#4223):
- experimental.decoders.image
- experimental.decoders.image_crop
- experimental.decoders.image_random_crop
- experimental.decoders.image_slice
Added the GPU debayer operator (#4495, #4486).

Fixed Issues

The following issues were fixed in this release:

Fixed the issue where the GPU numpy reader was crashing on a DALI process teardown with cufile 1.4.0 (#4466).
Fixed the issue where the GPU video decoder was failing in multi-GPU settings (#4517).

Improvements

Optimizing ShiftPixelCenter kernel configuration (#4430).
Update "Compiling from source" tutorial (#4010).
Imgcodec's decode operator (#4223).
Move to use CMake in DALI deps where possible (#4445).
Bump supported tf version (#4459).
Optimize inflate tests (#4456).
Execute whole Keras code in the expected device scope (#4462).
Update the TensorFlow test to work with 2.11.x (#4460).
Crop rounding argument to control the conversion of anchors to integral values (#4461).
Make Transpose's perm argument optional (by default, reverse dims) (#4465).
Add CastLike operator (#4467).
Accept negative axis in Cat and Stack operators (#4468).
Code drop AutoGraph based on TensorFlow 2.10.0 (#4485).
Remove build and doc files from AutoGraph (#4489).
Rearrange AutoGraph tests (#4490).
Adjust the documentation template for the latest sphinx_rtd_theme (#4481).
Bump the nvidia-tensorflow to 22.11 in tests (#4472).
Improve error reporting in the video decoder (#4484).
Move to generic CUDA_CALL for nvCOMP (#4474).
Extend the warning about the lack of the necessary CUDA libraries (#4473).
Allow negative axes in reductions module (#4470).
Add kernel-wrapper around NPP debayer calls (#4486).
Remove TF-specific codepaths from AutoGraph (#4491).
Lint the AutoGraph code (#4494).
Add bytes_per_sample_hint parameter to parallel external source (#4155).
Add debayer operator (#4495).
Remove trailing comments from .flake.ag (#4497).
Update DALI_DEPS_VERSION (#4496).
Deprecate CUDA 10.2 (#4503).
Extract CachingList from ExternalSource (#4501).

Bug Fixes

Do not call nvcomp with no input (#4434).
Fix libtiff CVE-2022-3970 (#4448).
TL3 SSD Install pycocotools from latest NVIDIA cocoapi repo (#4457).
Fix numpy reader crash (#4466).
Fix stub generation for dynamic linking (#4478).
Fix issues found by static analysis (#4477).
Fix PES tests with Python3.6/3.7 (#4500).
Patch FFmpeg for CVE-2022-3965, CVE-2022-3964 (#4499).
Fix video decoder cache for multiple GPUs (#4517).

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

DALI 1.21 is the final release that will support CUDA 10.2.

Known issues:

The GPU numpy reader might crash during the DALI process teardown with cufile 1.4.0.
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

Install via pip for CUDA 10.2:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda102==1.21.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda102==1.210.0

or for CUDA 11:

CUDA 11.0 build uses CUDA toolkit enhanced compatibility. It is built with the latest CUDA 11.x toolkit
while it can run on the latest, stable CUDA 11.0 capable drivers (450.80 or later). 
Using the latest driver may enable additional functionality. 
More details can be found in enhanced CUDA compatibility guide.

pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.21.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.21.0

Or use direct download links (CUDA 10.2):

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-1.1.0.tar.gz

NVIDIA/DALI v1.21.0 DALI v1.21.0 on GitHub