NVIDIA/DALI v1.16.1 on GitHub

Key Features and Enhancements

This release includes bug fixes, so there are no new features or enhancements.

Fixed Issues

The following issues were fixed in this release:

Fixed the fn.decoders.image was leaking memory on corrupted images (#4138).
- A memory leak in the libjpeg-turbo decoder implementation in case of corrupted images was fixed.
Fixed a crash in the fn.readers.numpy, when pad_last_batch is set, and more then one thread is used by DALI (#4056).
Fixed a faulty check that prevented the feed_input method from working after the pipeline was deserialized (#4096).

Improvements

None

Bug Fixes

Fix pad_last_batch in GPU NumpyReader (#4056)
Fix feed_input after deserialization (#4096)
Fix memory leak in libjpeg-turbo decoder implementation in case of corrupted images (#4138)
Add zlib to conda recipe (#4173)
Fix Numba versions in tests (#4111)
Fix device pick in Numpy reader tests (#4104)

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.
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, GPU external source is not properly synchronized with DALI internal streams. As a workaround, the user may 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

Install via pip for CUDA 10.2:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda102==1.16.1
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda102==1.16.1

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==1.16.1
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.16.1

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-1.1.0.tar.gz

NVIDIA/DALI v1.16.1 DALI v1.16.1 on GitHub