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DALI v2.2.0

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@stiepan stiepan released this 29 Jun 12:39
· 17 commits to main since this release
v2.2.0
5a6c01c

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Fixed Issues

  • Removed unnecessary samples' meta-data copies in FileReader (#6379)
  • Fixed parsing of malformed .npy headers numpy reader (#6380)
  • Added missing validation for COCO annotation bbox size (#6360)
  • Improved validation/error reporting for batch permutation in ndd (#6359)
  • Fixed validation for all operands residing on the same device in ndd (#6347)
  • Fixed handling of scalar-like values with new NumPy. (#6331)
  • Fixed memory leak when accessing Python attributes of Tensor/TensorList (data_ptr, stream, __(cuda_)array_interface__["data"]) (#6322)
  • Fixed per-sample tensor_resize argument handling (#6318)
  • Ensured consistent reporting of empty layouts in ndd (#6304)
  • Fixed handling of NVML_ERROR_NOT_SUPPORTED on partial-NVML platforms (#6296)

Improvements

  • Update VERSION to 2.2.0
  • Port libsound fixes (#6384)
  • Accept constant local variables as arguments with compile=True (#6382)
  • [Torchvision API] Remove Torchvision's dependency - InterpolationMode (#6383)
  • Improved NDD skills (#6376)
  • Torchvsion API documentation page update (#6313)
  • Document that the dali-dynamic-mode skill exists (#6358)
  • [Torchvision API] resized_crop and RandomResizedCrop (#6369)
  • Add more Python-related leak suppressions. (#6370)
  • Update video decoder & reader supported format list (#6377)
  • [Torchvision API] Input metadata (#6364)
  • Make skills/ the source of truth for skills (#6375)
  • Move to CUDA 13.3 (#6371)
  • Add checkpointing examples and skill. (#6368)
  • Add NVSkills CI workflow (#6374)
  • [Torchvision API] crop (#6353)
  • Reenable 1ch torchvision grayscale test (#6372)
  • Include reader in pipelines in transparent pipelining (#6357)
  • Hide WorkerThread implementation details (#6362)
  • Restore default hw_decoder_load to 0.65 in imgcodec decoder (#6366)
  • Document dynamic operator wrapper arguments (#6363)
  • Deps update/2026 05 14 (#6351)
  • Improve DLPack support for external tensor consumption (#6261)
  • Bump DALI_DEPS_VERSION (#6356)
  • Drop opencv_imgcodecs from libdali (#6349)
  • Convert numpy types to DALIDataType (#6350)
  • Update DALI dependencies to include security hardening in the build script. (#6346)
  • Update QA package versions for Python 3.14 (#6330)
  • Improve call site resolution (#6323)
  • Rename DALI dynamic mode skill and move to .agents directory (#6328)
  • Use upstream FFmpeg in Conda builds (#6345)
  • Torchvision API RandomApply implementation (#6342)
  • imgcodec: work around nvImageCodec ROI/orientation contract bug (#6344)
  • test: disable HW JPEG decoder in free-threading multithreading test (#6343)
  • Dynamic Mode checkpointing (#6340)
  • Source nvImageCodec, OpenCV, and libjpeg-turbo from conda-forge (#6315)
  • Exclude CodeQL cyclic import diagnostics (#6341)
  • Torchvision API RandomGrayscale operator (#6335)
  • Keep nvimgcodec wheel path in LD_LIBRARY_PATH for CPU-only test (#6338)
  • Surface dlerror() per attempted path in nvimgcodec wrap (#6337)
  • Coverity static analysis fixes - May 2026 (#6333)
  • Expose Philox4x32_10 to Python and use it in dynamic mode RNG. Fix cloning semantics. (#6334)
  • Add starter .greptile/ config for DALI reviews (#6326)
  • Move Philox RNG to dali_core. (#6332)
  • Cumulative performance fixes for Dynamic Mode (#6329)
  • Make Tegra DALI wheel declare nvimgcodec dependency (#6321)
  • Add transparent pipelining in dynamic mode (#6301)
  • Add static type inference to arithmetic ops. (#6317)
  • Coverity static analysis fixes (2026-04-21) (#6305)
  • Make nvImageCodec the default decoder and remove legacy (#6306)
  • Add dynamic mode control features docs and example (#6312)
  • OpSchema & factory refactoring part 1 (#6311)
  • Move to nose2 only (#6146)
  • Update nvCOMP to 5.2.0 (#6292)
  • Upgrade dlpack protocol. (#6307)
  • Adapt DALI to nvImageCodec 0.8.0 (#6293)
  • Update torch and torchvision package versions for CUDA 13.x (#6297)
  • ci: exclude py/unsafe-cyclic-import from CodeQL reporting (#6302)
  • Add shuffle_after_epoch to WebDataset, TFRecord, and MXNet readers (#6288)
  • Rework call site identification (#6298)
  • Move to CUDA 13.2 update 1 (#6299)
  • Add missing operators: bit shifts, bit not and modulo (#6294)
  • Fix formatting issue in keyword argument pluralization (#6300)
  • OpSchema/OpSpec metadata inference (#6280)
  • Add skill and evals for dynamic mode usage (#6271)

Bug Fixes

  • Bind FileReader samples by reference (#6379)
  • Missing _invoke in RandomApply, RandomGrayscale and Pad (#6385)
  • Fix escaped string parsing in numpy headers (#6380)
  • [Torchvision API] Exception for isolated objective operator execution (#6373)
  • Validate COCO annotation bbox size (#6360)
  • Require batch size for dynamic batch permutation (#6359)
  • Fix ndd mixing device ids (#6347)
  • Fix test_random_state_arg and add it to CI (#6339)
  • Adjust handling of scalar-like values with new NumPy. (#6331)
  • Fix and improve DeviceGuard. (#6327)
  • Fix leaked PyLong refs in pointer bindings (#6322)
  • Fix per-sample tensor_resize argument handling (#6318)
  • Fix a race condition when querying op metadata (#6309)
  • Fix handling of empty layouts in ndd.Invocation (#6304)
  • Disable default metadata policy for Python-based operators. (#6308)
  • Make NDD RN50 test runnable more than once. (#6303)
  • Handle NVML_ERROR_NOT_SUPPORTED gracefully on partial-NVML platforms (#6296)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

No features were deprecated in this release.

Known issues:

  • In some cases, the pass-through parallel external source outputs may be corrupted when used with pipelined dynamic executor. The issue occurs when all four conditions are met: 1. the pipeline uses dynamic executor exec_dynamic=True (default), 2. the external_source runs in parallel mode (parallel=True), 3. the ES output is directly returned from the pipeline, 4. the ES output is a single contiguous chunk of memory (either batch=True or batch_size=1). Currently, as a workaround, user can specify exec_dynamic=False when instantiating pipeline or add an extra fn.copy to prevent directly returning ES outputs from the pipeline.
  • A problem with insufficient static TLS allocation size has been observed on Ubuntu 22.04 for aarch64 that can result in process crash when loading dynamic libraries. Updating glibc to 2.39 or newer, or specifying higher static TLS size with GLIBC_TUNABLES=glibc.rtld.optional_static_tls=10000 should resolve the issue.
  • 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 dynamically link the CUDA toolkit. To use DALI, please install the latest (12.x or 13.x) CUDA toolkit.

DALI builds use CUDA toolkit enhanced compatibility: 
DALI is built with the latest CUDA 12.x/13.x toolkit but can be run on any stable drivers from the respective CUDA family (525 and 580).
Using the most recent driver may enable additional functionality. 
More details can be found in enhanced CUDA compatibility guide.

Install via pip for CUDA 13.0:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda130==2.2.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda130==2.2.0

or just:

pip install nvidia-dali-cuda130==2.2.0
pip install nvidia-dali-tf-plugin-cuda130==2.2.0

For CUDA 12:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==2.2.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==2.2.0

or just:

pip install nvidia-dali-cuda120==2.2.0
pip install nvidia-dali-tf-plugin-cuda120==2.2.0

Or use direct download links (CUDA 13.0):

Or use direct download links (CUDA 12.0):

FFmpeg source code:

  • This software uses code of FFmpeg licensed under the LGPLv2.1 and its source can be downloaded here

Libsndfile source code:

Contributors

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