NVIDIA/DALI v1.52.0 on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Introduced experimental Dynamic Mode: imperative execution model with lazy evaluation for easier integration into Python workflows. (#6066, #6064, #6060, #6056, #6042, #6039, #6037, #6036, #5954)
- Dynamic mode: add augmentation gallery (#6057)
- DALI Dynamic docs main page (#6052)
Added pipeline ZOO - snippets and examples for common image and video processing use cases. (#5922)
Added support for CUDA 13U2 (#6063)
Added fn.decoders.numpy (#5953) and CPU fn.paste operators (#5968).
Thank you @5had3z for your contributions.
Exposed knobs for pipeline dynamic executor:
- Exposed executor's stream_policy and concurrency options (#5983)
- Environment variable to control executor threads. (#5949)

Fixed Issues

Fixed stream ordering in Tensor::Copy and Tensor(List)GPU.as_cpu (#6070)
Fixed conversion of pinned tensors to DLPack. (#6061)
Fixed DLPack stride check if stride pointer is NULL
Fixed handling of videos without keyframes and reuse of old indices (#6058)
Fixed resize_crop_mirror video output shape (#5957)

Improvements

Update to FFmpeg 8.0
Dynamic mode: add augmentation gallery (#6057)
Add dynamic API for math functions + tests. (#6066)
Rename DALI2 to dynamic (#6064)
Move to CUDA 13.0 U2 (#6063)
Dynamic mode: operator base classes and operator call generator (#6060)
Update VERSION to 1.52.0
Update deps 25/10 (#6053)
Dynamic Mode: Tensor and Batch Types (#6056)
Remove CMake from acknowledgements. (#6020)
DALI Dynamic docs main page (#6052)
Reduce minimum throughput for experimental decoder in TL1_decoder_perf (#6050)
Fix TL0_video_plugin to run with sanitizer (#6040)
Imperative mode: Invocation (#6042)
Update LD_PRELOAD in sanitizer configuration, exclude more numba tests (#6041)
Imperative mode: EvalContext, EvalMode, Type and Device (#6039)
Update the test environment to Ubuntu 24.04 (#6033)
Update curl 3.15 -> 3.16 (#6038)
Add TensorList broadcasting constructor. (#6037)
Backend changes for imperative mode (#6036)
Add nvcc/nvjitlink version compatibility check to numba CUDA test (#6035)
Unify minimum required CMake version. (#6022)
Fix installation of Horovod in TL1_tensorflow-dali_test (#6024)
Remove confusing warning on host decoder fallback (#6029)
Add stream argument to TensorGPU DLPack constructor. (#6015)
Cumulative dependency update for September 2025. (#6017)
Silence false warnings in sanitized build (#6018)
Lower the 5% threshold in image decoder perf test to 15% to account for off iterations (#6021)
Bump CMake to 3.25.2 (#6019)
Move to CUDA 13.0 U1 (#6016)
Move to the gcc-toolset-14 (#6014)
Update test packages (#6010)
Correct support matrix entry for Orin (#6008)
Silence a false positive warning triggered by GCC 12.2.1 (#6002)
Fix CVE-2024-13978 and CVE-2025-8534 in libtiff (#6007)
Bump up OpenCV version to 4.12 in conda (#6005)
Move to the latest nvJPEG2k (#6000)
Enable more aggressive binary compression (#6001)
Use subprocess.run in get_tf_compiler_version to avoid CalledProcessError on grep (#5991)
Add functions that change the type of the tensor or tensor list to a different type of the same size. (#5995)
Update OpenCV version in tests (#5987)
Improve performance of experimental.resize (#5662)
Expose executor policy flags (#5983)
Pin CMake to max 4.0.3 in jupter_conda tests. (#5985)
Add driver version check to the usage of numba_cuda (#5982)
Fix nvComp installation in tests (#5984)
Update DALI_DEPS_VERSION to use patched libtiff (#5981)
Improve creating image batches in CV-CUDA ops (#5966)
Dependency update 07-2025 (#5978)
Make the numba operator compatible with the numba-cuda package (#5975)
Adjust TF plugin build dependencies (#5976)
fn.paste CPU impl (#5968)
Make sure that protobuf always uses own absl version instead of system one (#5974)
Thread pool with semaphore and spinlock (#5970)
Extend GetInputDevice in OpSchema python bindings. (#5972)
Remove data preparation instructions from the video superres use case (#5965)
Added fn.decoders.numpy (#5953)
Pipeline zoo - initial commit (#5922)
Expose Stream, Operator and Workspace in Python (#5954)
Fix nvcc not working with sanitizer (#5959)
Make the number of dynamic executor threads configurable via environment variables. (#5949)

Bug Fixes

Fix stream ordering in Tensor::Copy and Tensor(List)GPU.as_cpu
Fix conversion of pinned tensors to DLPack. (#6061)
Fix DLPack tests to use HWC layout instead of NHWC (#6062)
Fix handling of videos without keyframes and reuse of old indices (#6058)
Refactor layout handling in Python backend + add layout dimensionality checks in Tensor and TensorList python bindings (#6054)
Fix standalone op output streams. (#6055)
Remove EvalContext destructor. (#6043)
Fix static analysis issues (#6032)
Install newer CMake in TL0_jupyter (#6034)
Disable PYBIND11_FINDPYTHON in CMakeLists.txt (#6031)
Remove a custom patch for PyCuda, add numba_cuda version constrain (#6023)
Bugfix: Skip DLPack stride check if stride pointer is NULL
Improve error handling in ThreadPool (#6011)
Fix test_backend_impl launch command (#6003)
Remove unnecessary default values from optional arguments. (#5992)
Add missing backslash in test scripts. (#5986)
Fixes outdated DALI mannylinux tag (#5980)
resize_crop_mirror - invalid video output shape fix (#5957)

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==1.52.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda130==1.52.0

or just:

pip install nvidia-dali-cuda130==1.52.0
pip install nvidia-dali-tf-plugin-cuda130==1.52.0

For CUDA 12:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.52.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.52.0

or just:

pip install nvidia-dali-cuda120==1.52.0
pip install nvidia-dali-tf-plugin-cuda120==1.52.0

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:

https://developer.download.nvidia.com/compute/redist/nvidia-dali/libsndfile-1.2.2.tar.gz

NVIDIA/DALI v1.52.0 DALI v1.52.0 on GitHub