NVIDIA/DALI v1.44.0 on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

The dynamic executor (exec_dynamic) is no longer experimental. It supports GPU to CPU transfers and reduces memory consumption. (#5704)
Added support for zero-copy outputs transfer with dynamic executor. (#5684, #5673)
- Eliminated the outputs copy in PyTorch plugin. (#5699)
Added dynamic executor support to TF plugin. (#5686)
Optimized pipeline's output contiguity handling. (#5677)

Fixed Issues

Restricted nvImageCodec version in DALI wheel dependencies list, as the most recent nvImageCodec (0.4.0) is incompatible. (#5709)
Fixed custom stream handling on non-default device in fn.external_source (#5690).
Fixed problem with using DALI with Python3.12 with no distutils/setuptools installed.
Fixed incorrect stream usage in fn.experimental.inputs.video (#5682)
Fixed possible hang in video decoder when rewinding near the last keyframe (#5676, #5669)
Fixed dont_use_mmap option handling in fn.readers.webdataset (#5683)
Fixed redundant usage of pinned memory in the CPU fn.readers.numpy reader (#5678)
Fixed dynamic executor's handling of operators that produce no outputs (#5674)

Improvements

Make exec_dynamic non-experimental (alternative formatting) (#5704)
Use zero-copy outputs with PyTorch (#5699)
Add Python 3.13 (experimental) support (#5692)
Add proper NVTX markers to Executor2. (#5694)
Add Efficientnet pipeline to hw_bench script (#5691)
Stream aware outputs (#5684)
Update DALI_DEPS_VERSION for new OpenSSL (#5689)
Add dynamic executor support to TF plugin. (#5686)
Make black and flake8 run independently. (#5685)
Update of FFmpeg to n7.1 (#5681)
Deps update 10 2024 (#5670)
Refactor operator output contiguity handling (#5677)
Add ready event to Tensor and TensorList. (#5673)

Bug Fixes

Fix nvimgcodec version check, do not install it separately in tests env (#5713)
Limit the upper versions of DALI wheel installation dependencies (#5710)
Limit the maximum version of nvimagecodec for current DALI (#5709)
Use exec-dynamic in RNN-t pipeline. Minor fix to exec2. (#5706)
Check JAX version and invoke dlpack manually for jax pre-0.4.16. (#5702)
Fix nose imports (#5698)
ExternalSource refactoring and fixing (#5690)
Move from deprecated distutils to packaging (#5687)
Make sure that the proper video stream index is used by the GPU decoder (#5682)
Add an ability to rewind at the end of the video (#5676)
Fix inverted mmap inside webdataset reader (#5683)
Fix the redundant usage of pinned memory in the numpy cpu reader (#5678)
Fix handling of tasks with zero outputs. (#5674)
Add an ability to retry rewind to the one before the last keyframe (#5669)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

No features were deprecated in this release.

Known issues:

The most recent nvImageCodec (0.4.0) is currently incompatible with DALI. Python wheel for DALI 1.44 pins the dependency to 0.3.0, but older releases do not specify the required version explicitly. Users of previous DALI releases may need to manually install older nvImageCodec in order to use fn.experimental.decoders.image.* or, for DALI 1.39 and 1.40, fn.decoders.image.*. The compatible version can be installed with pip install nvidia-nvimgcodec-cu12~=0.3.0.
The following operators: experimental.readers.fits, experimental.decoders.video, and experimental.inputs.video 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==1.44.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.44.0

or just:

pip install nvidia-dali-cuda120==1.44.0
pip install nvidia-dali-tf-plugin-cuda120==1.44.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.44.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.44.0

or just:

pip install nvidia-dali-cuda110==1.44.0
pip install nvidia-dali-tf-plugin-cuda110==1.44.0

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 v1.44.0 DALI v1.44.0 on GitHub