NVIDIA/DALI v1.25.0

DALI v1.25.0

on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

• Added the experimental flexible image transport system (FITS) reader (fn.experimental.readers.fits) for the CPU backend (#4591).
• Added the CPU backend for the histogram equalization operator (fn.experimental.equalize) (#4742).
• Added the CPU backend for the 2-D convolution for images and video (fn.experimental.filter) (#4764).
• Added support for feeding pipeline inputs as named arguments in Pipeline.run (#4712).
• Improved the automatic augmentations and conditional execution in the following ways:
  • Support for CPU inputs in predefined automatic augmentations (#4772).
  • Reduced memory consumption (#4697).
  • Support for conditional execution in debug mode (#4738).
  • EfficientNet training example with DALI AutoAugment (#4678).
  • More predefined policies for AutoAugment (#4753).
  • Support for numerical types in the if predicate and not expression (#4715).
• Operator improvements:
  • Improved the performance of CPU brightness and contrast operators for uint8 samples (#4737).
  • Improved the fn.readers.webdataset performance (#4708).
  • Support booleans in fn.readers.numpy (#4745).
  • Added support for booleans in the DALI iterator for PyTorch (#4757).

Fixed Issues

• Fixed possible hangs on a pipeline build or teardown when using fn.experimental.decoder.image (#4727).
• Fixed D2D copy synchronization that might result in fn.experimental.decoders.video returning incorrect frames for high-resolution videos (#4717).
• Fixed buffer exhaustion in fn.experimental.decoder.image (#4723).
• Fixed GPU unary arithmetic operators (for example, math.abs and math.floor) incorrectly processing non-scalar samples (#4746).
• Fixed host JPEG decoder leaking memory on incorrect files (#4748).
• Fixed missing source information in the numpy reader output (#4714).
• Fixed error message in assertion in base_iterator.py (#4726).

Improvements

• Expose Automatic Augmentation docs (#4760)
• Rename sample to data in automatic augmentation APIs (#4774)
• Support CPU samples in predefined automatic augmentations (#4772)
• Make conditionals work in debug mode (#4738)
• Improve PyPi DALI description (#4769)
• Use lookup table for uint8 inputs in mul-add kernel (#4737)
• Add more AutoAugment policies (#4753)
• Add CPU filter operator (#4764)
• Simplify AutoAugment graph (#4751)
• Add links to DALI related GTC'23 talks (#4743)
• Make python output unbuffered in tests (#4766)
• Adjust docs config for newer Sphinx version (#4765)
• Update DALI_DEPS sha version (#4763)
• Move enable_conditionals option to regular @pipeline_def (#4747)
• Adds bool type support to PyTorch DALI integration (#4757)
• Update deps: pybind, FFmpeg, zstd (#4749)
• Update TensorFlow version used in tests (#4739)
• Stop building the DALI TF plugin for conda (#4741)
• Enable bool support in the numpy reader operator (#4745)
• Add CPU equalize operator (#4742)
• Add experimental FITS reader for CPU backend (#4591)
• Adjust RN50 TF performance test threshold (#4734)
• Add timestamps to QA test output. (#4733)
• Update nvJPEG2k to 0.7 version (#4728)
• Add a requirement for CUDA toolkit for CUDA 12 builds (#4588)
• DALI Pipeline inputs as named arguments to Pipeline.run() (#4712)
• Update RN50 PyTorch test speed threshold (#4724)
• Add links for DALI installations to docs (#4716)
• Support numerical types in if predicate and not expression (#4715)
• Reduce memory footprint of conditional execution (#4697)
• Add EfficientNet example using automatic augmentations with DALI (#4678)
• Change WDS index version representation to integer + refactor version utilties. (#4708)
• Update OpenCV build recipe (#4693)
• Update GTC 2022 sessions' links in the README (#4705)

Bug Fixes

• Update CLANG version (#4768)
• Fix the lack of proper error handling in selected tests (#4759)
• Update fix assert error messages in base_iterator.py (#4726)
• Fix bug in Arithmetic unary op implementation (#4746)
• Fix memory leak in host JPEG decoder (#4748)
• Fix missing source info in the numpy reader output (#4714)
• Fix buffer exhaustion in the frames_decoder_gpu (#4723)
• Fix nightly tests after merging RunArg (#4732)
• Move the --pending and cv.notify_all() inside the critical section to prevent the notification from going unobserved. (#4727)
• Pass the correct shape to auto augs in the EfficientNet example (#4721)
• Fix pytorch-lightning example with Python3.6 (#4722)
• Fix pytroch-lightning notebook example (#4719)
• Fix D2D copy in the GPU frames decoder (#4717)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

No features were deprecated in this 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, 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==1.25.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.25.0

or for CUDA 11:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda110==1.25.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda110==1.25.0

Or use direct download links (CUDA 12.0):

• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda120/nvidia_dali_cuda120-1.25.0-7922358-py3-none-manylinux2014_x86_64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda120/nvidia_dali_cuda120-1.25.0-7922358-py3-none-manylinux2014_aarch64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-tf-plugin-cuda120/nvidia-dali-tf-plugin-cuda120-1.25.0.tar.gz

Or use direct download links (CUDA 11.0):

• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda110/nvidia_dali_cuda110-1.25.0-7922357-py3-none-manylinux2014_x86_64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-cuda110/nvidia_dali_cuda110-1.25.0-7922357-py3-none-manylinux2014_aarch64.whl
• https://developer.download.nvidia.com/compute/redist/nvidia-dali-tf-plugin-cuda110/nvidia-dali-tf-plugin-cuda110-1.25.0.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-1.2.0.tar.gz