NVIDIA/DALI v1.9.0 on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements.

Extended the jpeg_compression_distortion operator to support video inputs (#3482 and #3447).
Added the file_filter argument to the readers.file operator that allows you to filter files by names (#3459).
Extended the slice operator to support per-sample axes arguments and negative axis indexing (#3516).
Extended the pad operator to support per-sample axes, fill_value arguments, and negative axis indexing (#3534).
Improved the performance of the slice operator for small batch sizes (#3557).
Added the Laplacian CPU kernel (#3565, #3535, and #3518).

Fixed Issues

This DALI release includes the following fixes:

Fixed a race condition that randomly caused incorrect outputs in the TensorFlow plugin (#3547).
Fixed synchronization issues in the PaddlePaddle plugin that may have caused incorrect results (#3498 and #3487).

Improvements

Make Slice kernel tiling adaptive (#3557)
Add Laplacian CPU kernel (#3518)
Allows DALI to dlopen dependent CUDA toolkit libraries: NPP, cuFFT and nvJPEG (#3519)
Fix test code to be compatible with python 3.6 (#3550)
Fix a typo in warp jupyter notebook. (#3554)
Add Cast and CoinFlip GPU benchmarks (#3541)
Fix DALI TL3 test for 21.11 (#3529)
Pad operator: Add support for per-sample axes and fill_value arguments, and negative axes (#3534)
Add FlipGPU and GaussianBlurGPU benchmarks (#3538)
Make bundle-wheel.sh more configurable (#3539)
Enable DALI test on python 3.9 and add 3.10 support (#3522)
Add transform parameter to convolution cpu (#3535)
Remove nvJPEG leak sanitizer workaround in tests (#3532)
Dependency update Nov 2021 (#3523)
Add support for per-sample axes and negative axes in Slice (#3516)
Refactor ArgValue to support empty samples and batch shape expectations (#3528)
Move to CUDA 11.5 update 1 (#3526)
Add Copy GPU benchmark (#3517)
Move to CUDA_CALL for nvJPEG, nvJPEG2k, and NPP (#3521)
Silence warning in LookupTable (#3508)
Move unfold_outer_dim to common utilities. (#3486)
Remove Context from memory resources. (#3485)
Set minimum python version to 3.7 for TF 2.7 (#3489)
Allow video inputs to JpegCompressionDistortion (#3482)
Bump up TensorFlow version to 2.7 in tests (#3475)
Change the way how NVML wrapper is linked internally (#3481)
Add support for file_filters in FileReader (#3459)
Allow video inputs to JpegCompressionDistortion (#3447)
Move to Ubuntu 20.04 for cuda 10.2 toolkit image (#3477)
Move to Ubuntu 20.04 for cuda toolkit image (#3476)
Pin Keras version for TensorFlow 2.6 (#3474)
Add support for BatchInfo in experimental TF DALI Dataset (#3468)

Bug Fixes

Replace equality with EqualEpsRel in Laplacian kernel tests (#3565)
Synchronize CUDA stream once in operator benchmark (#3525)
Ensure that num_devices and device are stored in correct order. (#3560)
Fix conda test for CUDA 10.x (#3556)
Fix race condition when initializing per-device default memory resources (#3555)
Fix data race when copying outputs in TF plugin (#3547)
CUDA VM resource bugfixes (#3545)
Fix build of DALI TensorFlow plugin during installation (#3546)
Fix issues found during static analysis (#3524)
Fix lack of proper device id used to obtain relevant cuda stream in paddle plugin (#3498)
Add type check to last_batch_policy argument (#3490)
Fix DALI paddle plugin stream synchronization error (#3487)
Reuse GaussianBlur windows between iterations (#3484)
Add synchronization when destroying the Executor. Make all destructors noexcept. (#3492)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

There are no deprecated features in this DALI 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 lesser frequency, then the returned frames may be out of sync.
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, use GCC 4.8.4, GCC 4.8.5, or GCC 5.4.)
Due to some known issues with meltdown/spectra mitigations and DALI, DALI shows best performance when run 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==1.9.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda102==1.9.0

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==1.9.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.9.0

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-1.0.31.tar.gz

NVIDIA/DALI v1.9.0 DALI v1.9.0 on GitHub