NVIDIA/DALI v1.42.0 on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Introduced more flexible execution in the DALI pipeline with the experimental_exec_dynamic flag (#5635, #5631, #5593, #5528, #5620, #5602, #5529, #5595):
- Enabled support for GPU-to-CPU transfers in a pipeline.
- Added support for accessing CPU metadata of GPU outputs (e.g. shape of GPU decoded images/videos).
Added support for CUDA 12.6U1 (#5616).
Added an option to return the number of frames in the experimental video reader (#5628).

Fixed Issues

Fixed handling of optical flow initialization failure (#5624).

Improvements

Add metadata-only inputs. (#5635)
Schema-based input device check (#5631)
Enable GPU->CPU transfers (#5593)
Adds enable_frame_num to the experimental video reader (#5628)
Executor2 class implementation & tests (#5528)
Executor 2.0: Per-operator stream assignment policy (#5620)
Move to CUDA 12.6U1 (#5616)
Executor 2.0: Stream assignment (#5602)
Tasking: Test returning multiple outputs of type std::any. (#5529)
Patch OSS vulnerabilities (#5612)
Executor 2.0: Graph lowering (#5595)
Make DALI tests compatible with Python 3.12 (#5452)
Adjust the L3 perf test threshold for H100 runners (#5606)
Add L1 image decoder DALI test (#5601)

Bug Fixes

Fix multiple initialization attempts in optical flow operator. (#5624)
Fix null pointer access when clearing incomplete workspace payload. (#5622)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

No features were deprecated in this release.

Known issues:

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==1.42.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.42.0

or just:

pip install nvidia-dali-cuda120==1.42.0
pip install nvidia-dali-tf-plugin-cuda120==1.42.0

For CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.42.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.42.0

or just:

pip install nvidia-dali-cuda110==1.42.0
pip install nvidia-dali-tf-plugin-cuda110==1.42.0

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 v1.42.0 DALI v1.42.0 on GitHub