NVIDIA/DALI v1.38.0 on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Added support for AWS S3 urls in DALI readers (#5415, #5434).
Improved support for enum types in types.Constant, fn.cast, fn.random.choice (#5422).
Improved error reporting (#5428).

Fixed Issues

Fixed checkpoint clean-up in C API. (#5453)

Improvements

Dependency update for May 2024 - black, boost-pp, cv-cuda, pybind11, rapidjson (#5458)
Introduce DALI_PRELOAD_PLUGINS (#5457)
Move old ImageDecoder to legacy module and make the nvImageCodec based ImageDecoder the default (#5445)
Bump up NUMBA version used in tests to 0.59.1 (#5451)
Extend the documentation footer (#5454)
Remove the use of (soon deprecated) aligned_storage. (#5455)
Make shared IterationData a first class member of Workspace. (#5447)
Tasking module (#5436)
Add AWS SDK support to all file readers (FileReader, NumpyReader, WebdatasetReader...) (#5415)
Fix test_enum_types.py for Python3.11 (#5443)
Remove files related to QNX that are no longer used (#5438)
Remove usage of THRUST host&device vector (#5439)
Add CMake to aarch64 base docker images (#5437)
Refactoring of File Reader classes to accommodate for AWS SDK S3 integration (#5434)
Replace Ops class name with proper operator API name (#5428)
Use CMake binary release (#5435)
Improve support for DALI enum types (#5422)
Disable some JAX iterator tests in sanitizer run (#5427)

Bug Fixes

Fix GTest Death Style Tests and LoadDirectory test in conda (#5469)
Revert "Move old ImageDecoder to legacy module and make the nvImageCodec based ImageDecoder the default (#5445)" (#5464)
Pin JAX version for multigpu test (#5460)
Use C++17 standard in nodeps test. (#5459)
Fix Coverity issues (May/2024) (#5453)
Fix equalize unit test (#5456)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

DALI 1.39 will be the last release to support MXNet integration.

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==1.38.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.38.0

or just:

pip install nvidia-dali-cuda120==1.38.0
pip install nvidia-dali-tf-plugin-cuda120==1.38.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.38.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.38.0

or just:

pip install nvidia-dali-cuda110==1.38.0
pip install nvidia-dali-tf-plugin-cuda110==1.38.0

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 v1.38.0 DALI v1.38.0 on GitHub