Release Notes

This document describes the key features, software enhancements and improvements, and known issues for DALI 1.32.0. For previously released DALI documentation, see DALI Archives.

Overview

DALI offers both performance and flexibility of accelerating different data pipelines (graphs that can have multiple outputs and inputs), as a single library, that can be easily integrated into different deep learning training and inference applications.

Using DALI

Note

DALI builds for NVIDIA® CUDA® 12 dynamically link the CUDA toolkit. To use DALI, install the latest CUDA toolkit.

To upgrade to DALI 1.32.0 from a previous version of DALI, follow the installation and usage information in the DALI User Guide.

Note

The internal DALI C++ API used for operator’s implementation, and the C++ API that enables using DALI as a library from native code, is not yet officially supported. Hence these APIs may change in the next release without advance notice.

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

  • Added Python signatures/type hints to the DALI Python API (#5096, #5039), #5112,) #5118, #5124, #5143).

  • Added experimental support for checkpointing DALI pipelines at arbitrary iterations (fn.readers.file, CPU fn.random generators, and stateless operators) (#5085, #5088, #5103, #5114, #5113, #5142, #5128, #5144,).

  • Added support for CUDA 12.3 (#5106).

Fixed Issues

The following fixes are included in this release:

  • Fixed a potential crash on process teardown when using the fn.python_function in the DALI pipeline (#5138).

  • Removed unused arguments from fn.fast_resize_crop_mirror.

  • The operator was deprecated in favor of fn.resize_crop_mirror (#5123).

  • Fixed a potential crash on process teardown when using the CPU fn.resize in the DALI pipeline (#5133).

  • Fixed constructing tensors from stream-aware __cuda_array_interface__ v3 (#5125).

  • Fixed the crop_pos_z handling for a fixed crop window in the fn.crop operator (#5119).

  • Fixed releasing Python tensors without GIL in fn.external_source.

  • The problem led to crashes when using fn.external_source in no_copy or parallel mode with conditional execution enabled in the pipeline (#5101).

Breaking Changes

  • DALI 1.31.0 was the final release to support Python 3.7.

Deprecated Features

  • The fn.fast_resize_crop_mirror operator was deprecated in favor of fn.resize_crop_mirror.

Known Issues

This DALI release includes the following 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.

  • The 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 plug-in might not be compatible with TensorFlow versions 1.15.0 and later.

    To use DALI with the TensorFlow version that does not have the prebuilt plug-in binary that is shipped with DALI, ensure that the compiler that is used to build TensorFlow exists on the system during the plug-in 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 the 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