NVIDIA/DALI v1.53.0 on GitHub

Key Features and Enhancements

This DALI release includes the following key features and enhancements:

Improved Dynamic Mode (ndd)
- Added ndd.imread function for reading and decoding images (#6092)
- Support dali random operators in dynamic mode (#6110, #6101, #6100, #6096, #6093, #6088)
- Added automatic argument type conversion (#6095)
- Improved documentation, annotations, and stubs (#6085, #6089, #6080, #6078)
Added fn.bbox_rotate operator (#5979)
- Thank you @5had3z for your contribution!
Added support for nvImageCodec 0.7.0 (#6105)
Improved streams re-usage to decrease spurious depenedencies between kernels (#6072)
Migrated to C++20 (#5962)

Fixed Issues

Fixed broadcasting in constants and fn.full. (#6104)
Fixed large batch handling in GPU random ops. (#6082)
Fixed device tracking in dynamic mode. (#6090)
Fixed synchronization in GPU Tensor and TensorList copy. (#6071)

Improvements

Support random ops in dynamic mode stub files (#6110)
Update VERSION to 1.53.0
Update nvcomp to libnvcomp 5.1.0.21 (#6111)
Update numba-cuda to 0.22.0, fix TL0_python-self-test-operators_1 test (#6106)
Update nvImageCodec to 0.7.0 (#6105)
Add Python RNG API for DALI dynamic mode random operators (#6101)
Fix in TL1_cutom_src_pattern_build test (#6103)
Fix numba-cuda installation for Python 3.9 (#6097)
Random operator rework (#6100)
Dependency update 2025.11 (#6094)
Add deprecation warning for Python 3.9 (#6098)
Create stub files for the dynamic mode (#6089)
Add ndd.imread function for reading and decoding images (#6092)
Remove the name "dynamic executor" from the docs and error messages (#6084)
Poisson distribution using cuRAND and STL (#6096)
Generate Dynamic Mode documentation (#6085)
Add automatic argument type conversion to Dynamic API. (#6095)
Random distributions for host and device. (#6093)
Fix clang CUDA runtime wrapper patching for multiple versions (#6091)
Add hidden _random_state argument for Dynamic Mode random operators (#6087)
Add Philox32x4_10 generator for CPU. (#6088)
Fill gaps in dynamic mode documentation - Tensor, Batch, EvalContext, Device and Readers. (#6080)
Remove texture-based video processing in NvDecoder (#6076)
Coveriity check 2025.11. (#6081)
Dynamic mode operator docstrings (#6078)
Add --no-build-isolation flag to DALI plugins (#6077)
Document executor flags StreamPolicy and OperatorConcurrency (#5988)
Add a busy list to CUDAStreamPool. Don't return busy streams from the pool. (#6072)
Make semaphore implementation selectable. Default is POSIX. (#6074)
fn.bbox_rotate (#5979)
Switch to C++20 (#5962)
Fix warnings reported by flake8 (#6059)

Bug Fixes

Fix TL3_EfficientDet_convergence & TL3_YOLO_convergence tests (#6118)
Add proper broadcasting logic to constant_value operator family. (#6104)
Fix Python 3.9 compatibility in imread type annotations (#6099)
Make _signatures.py compatible with Python 3.9 (#6102)
Add EvalContext tracking to Invocation to prevent silent device switching (#6090)
Fix large batch handling in GPU random ops. (#6082)
Always use proper CUDA stream for GPU Tensor(List) copy. Don't use stream 0. (#6071)
Fix usage of std::optional in bbox rotate. Fix conversion to numpy in bbox_rotate tests. (#6073)

Breaking API changes

There are no breaking changes in this DALI release.

Deprecated features

DALI 1.53 is the last release to support Python 3.9.

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==1.53.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda130==1.53.0

or just:

pip install nvidia-dali-cuda130==1.53.0
pip install nvidia-dali-tf-plugin-cuda130==1.53.0

For CUDA 12:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-cuda120==1.53.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/ nvidia-dali-tf-plugin-cuda120==1.53.0

or just:

pip install nvidia-dali-cuda120==1.53.0
pip install nvidia-dali-tf-plugin-cuda120==1.53.0

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 v1.53.0 DALI v1.53.0 on GitHub