NVIDIA/DALI v1.13.0 on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements.

Added support for per-frame (temporal) arguments to the Gaussian Blur and Laplacian operators (#3715 and #3723).
Optimized audio decoder resampling for ARM (#3745).
Improved the debug (immediate execution) mode:
- Added direct operator calls in debug mode (#3734).
- Added a debug mode benchmark (#3762).
Added support for GPU positional arguments in the Slice operator (#3741).
Documentation improvements:
- Split the operator documentation into separate pages (#3794).
- Added a mechanism for cross-referencing examples and operators (#3748).
- Added an FAQ section to the DALI user guide (#3761).
- Added new GTC talks (#3757).
- Added shuffling and shards handling snippets to the parallel external source examples (#3744).

Fixed Issues

Fixed the handling of samples that exceed 2GBs in the parallel external source (#3768).

Improvements

Add per-frame operator (#3723)
Add support for per-frame arguments to Gaussian Blur and Laplacian operators (#3715)
Separate the documentation pages! (#3794)
Update zlib to 1.2.12 version (#3787)
Trim TL0_tensorflow_plugin and TL0_python-self-test-readers-decoders tests (#3796)
Add _schema_name attribute in fn API (#3798)
Add resize checkerboard tests, comparing to ONNX reference precomputed data (#3792)
Update nvJPEG2000 to 0.5.0 version (#3791)
Fix header in parallel external source notebook (#3790)
Update documentation link to the '22 roadmap (#3786)
Bump Nvidia TF1 version used in tests to 22.03 (#3769)
Add mechanism for crossreferencing examples and operators (#3748)
Add direct operator calls in debug mode (#3734)
Make number of samples in batch signed (#3789)
Add debug mode benchmark (#3762)
Fix the cuBLAS version to one compatible with nvTF 22.01 (#3781)
Apply changes from TV sample encapsulation in NVJPEG2K (#3780)
Ensure sample encapsulation in Tensor Vector (#3701)
Add a TL0 test that runs on more than 1 GPU (#3772)
Add FAQ section to the DALI documentation (#3761)
Remove the compose operator from the fn API table (#3767)
Add new GTC talks. Update old link (#3757)
Update to CUDA 11.6u2 (#3764)
RNG to use pinned memory for kernel launch args (#3765)
Revert "Pin webdataset version to the last compatible with python 3.6 (#3746)" (#3763)
Fix the wrong patch for CVE-2022-0907 which by mistake duplicated CVE-2022-0909 (#3760)
Quantize GDS chunk size to 1 MB. (#3759)
Add GDS-compatible allocator with 4k alignment. (#3754)
Update error messaging of nvJPEG (#3756)
Allow GPU slice arguments (#3741)
Add filename to the error message in the numpy reader (#3753)
Fix libtiff vulnerabilities (#3752)
Update parallel external source notebook and include shuffling example.. (#3744)
Add supported python version classifier to DALI TF plugin setup.py (#3751)
Vectorize audio resampling for ARM NEON. (#3745)
Remove prints from the regular DALI execution flow (#3740)
Pin webdataset version to the last compatible with python 3.6 (#3746)
Align test expectations with slice implementation rounding logic (#3738)
Update RapidJSON (#3737)
Regenerate getting started jupyter examples (#3732)
Improve documentation for AccessOrder wait and set_order. (#3736)

Bug Fixes

Add missing copying of pinned prop when sharing buffer (#3797)
Disable PES large sample test on Xavier runner (#3788)
Fix source device in PyTorch cross-device test. (#3775)
Fix large mini-batch handling in parallel external source (#3768)
Fix Yolo v4 example non-fatal teardown error (#3739)
Rework Image Decoder example (#3731)
Check return value of a CUDA function call. (#3733)

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 frequency that is less than 10-15 frames, the returned frames might be out of sync.
The DALI TensorFlow plug-in might not be compatible with TensorFlow versions 1.15.0 and later.
To use DALI with the TensorFlow version that does not have the prebuilt plug-in binary that is shipped with DALI, ensure that the compiler that is used to build TensorFlow exists on the system during the plug-in installation. (Depending on the particular version, you can 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 the 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==1.13.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda102==1.13.0

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==1.13.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.13.0

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 v1.13.0 DALI v1.13.0 on GitHub