NVIDIA/DALI v1.1.0

DALI v1.1.0

on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements.

• Documentation improvements (#2834, #2824, #2831, #2758, #2820, and #2822).
• The following operators were added:
  • The experimental numba_func operator that allows the use of Numba functions in the DALI pipeline (#2804).
  • The expand_dims and squeeze operators for shape manipulation (GPU and CPU) (#2800, #2791, #2792).
  • The multi_paste operator (GPU) (#2681).
• The following kernels were added:
  • JPEG compression distortion (GPU) (#2801, #2830, and #2839).
  • JPEG color conversion and chroma subsampling (GPU) (#2771).
• Enabled CUDA kernels compression to decrease the DALI binaries size (#2833).
• Added the src_dims argument to the reshape operator (#2788).

Fixed issues

This DALI release includes the following fixes:

• Fixed a race condition in readers.nemo_asr when pad_last_batch is set to True (#2828).
• Fixed the optical flow initialization issue (#2816).
• Fixed a race condition in the data loader (#2773).

Improvements

• Remove 0 default value from mean/std arguments of normalize. (#2834)
• Add JpegCompressionDistortionGPU kernel (#2830)
• Updates the pipeline docs page (#2824)
• Enable CUDA kernels compression in the final binary (#2833)
• Updates build documentation (#2831)
• Update key visual (#2822)
• Add NumbaFunc operator (#2804)
• Add JPEG distortion kernel (#2801)
• Add AddArg overloads for enum types (#2819)
• Update third party dependencies to latest release versions (#2811)
• Add an ability to provide a custom DALI_extra sha via env variable (#2810)
• Move all deps into subrepos (#2756)
• Reshape, Reinterpret, Squeeze and ExpandDims tutorial. (#2791)
• Separate creation of dependency creation and CUDA installation (#2786)
• Remove intermediate stage from CUDA toolkit dockerfile (#2803)
• Add Expand dims operator (#2800)
• Update TensorFlow ResNet50 example to the latest horovod 21.03 (#2793)
• Add squeeze operator (#2792)
• Add JPEG color conversion and chroma subsampling kernel (#2771)
• Add src_dims to reshape operator (#2788)
• GPU MultiPaste (#2681)
• Add --upgrade to pip install commands in documentation (#2758)
• Use flattened view of the array for copying to shared memory. (#2783)

Bug fixes

• Fix JPEG distortion kernel quality parameter handling (#2839)
• Fix typo "funcions" <- "funcions" in math doc (#2820)
• Update DALI_deps to include FLAC security patch (#2826)
• Fix coverity issues (#2812)
• Fix optical flow parameter initialization. (#2816)
• Add host fallback when nvjpegDecodeJpegDevice and nvjpegDecodeJpegHost fail (#2805)
• ExternalSource - discard data from all callbacks when one raises StopIteration (#2784)
• Exclude PyTorch-lighting test with MNIST (#2785)
• Fix iteration number tracking with pipeline.reset (#2777)
• Fix a race when loader starts reading even the metadata is not ready yet (#2773)
• Fix race condition in NemoAsrReader when pad_last_batch is set to True (#2828)

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 lesser frequency, then the returned frames may be out of sync.
• 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, 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 best performance when run 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:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda100==1.1.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda100==1.1.0

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==1.1.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.1.0

Or use direct download links (CUDA 10.0):

• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda100/nvidia_dali_cuda100-1.1.0-2159051-py3-none-manylinux2014_x86_64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-tf-plugin-cuda100/nvidia-dali-tf-plugin-cuda100-1.1.0.tar.gz

Or use direct download links (CUDA 11.0):

• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda110/nvidia_dali_cuda110-1.1.0-2159930-py3-none-manylinux2014_x86_64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda110/nvidia_dali_cuda110-1.1.0-2159930-py3-none-manylinux2014_aarch64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-tf-plugin-cuda110/nvidia-dali-tf-plugin-cuda110-1.1.0.tar.gz

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-1.0.28.tar.gz