# TensorFlow Core Published articles for TensorFlow Core. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## What's new in TensorFlow 2.20 DevFeed: [What's new in TensorFlow 2.20]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2025-08-19T16:00:00Z Content type: news Language: en Sources: [The TensorFlow Blog]() Topics: [LiteRT](), [On-device AI](), [Inference](), [Latency](), [IO](), [GPU](), [data](), [Google](), [Cloud](), [Python](), [C++](), [Kotlin]() Tags: [announcement](), [c-plus-plus](), [data](), [google](), [gpu](), [inference](), [io](), [kotlin](), [latency](), [litert](), [npu](), [on-device](), [python](), [tensorflow](), [tensorflow-core](), [tensorflow-lite]() ### AI overview TensorFlow 2.20 introduces the independent LiteRT repository as the future home for on-device inference, replacing the deprecated tf.lite module. LiteRT adds Kotlin and C++ APIs, improves NPU and GPU acceleration, and supports lower-latency inference with fewer memory copies. The release also adds tf.data input-pipeline warm-up controls and makes Google Cloud Storage support optional. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.20 has been released! For ongoing updates related to the multi-backend Keras, please note that all news and releases, starting with Keras 3.0, are now published directly on keras.io. You can find a complete list of all changes in the full release notes on GitHub. tf.lite is being replaced by LiteRT The tf.lite module will be deprecated with development for on-device inference moving to a new, independent repository: LiteRT. The new APIs are available in Kotlin and C++. This code base will decouple from the TensorFlow repository and tf.lite will be removed from future TensorFlow Python packages, so we encourage migration of projects to LiteRT to receive the latest updates. More details to follow. As announced at Google I/O '25, LiteRT improves upon TFLite, particularly for NPU and GPU hardware acceleration and performance for on-device ML and AI applications. LiteRT provides a unified interface for Neural Processing Units (NPUs), removing the need to navigate vendor-specific compilers or libraries. This approach avoids many device-specific complications, boosts performance for real-time and large-model inference, and minimizes memory copies through zero-copy hardware buffer usage. For more information on the new repository and to sign up for the NPU Early Access Program, please reach out to the team at g.co/ai/LiteRT-NPU-EAP. Faster input pipeline warm-up with tf.data To help reduce latency, especially the time it takes for your model to process the first element of a dataset, we've added autotune.min_parallelism in tf.data.Options. This new option allows asynchronous dataset operations like .map and .batch to immediately start with a specified minimum level of parallelism, speeding up the initial warm-up time for your input pipelines. Changes to I/O GCS filesystem package The tensorflow-io-gcs-filesystem package for Google Cloud Storage support is now optional. Previously, it was installed, by default, with TensorFlow. If y ## What's new in TensorFlow 2.19 DevFeed: [What's new in TensorFlow 2.19]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2025-03-13T16:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [Tensorflow](), [LiteRT](), [releases](), [API](), [C++](), [Release notes](), [Keras](), [migration]() Tags: [2025](), [announcement](), [api](), [c-plus-plus](), [keras](), [libentensorflow](), [litert](), [migration-guide](), [pypi](), [release](), [release-notes](), [tensorflow](), [tensorflow-core](), [tensorflowlite]() ### AI overview TensorFlow 2.19 introduces C++ API changes in LiteRT, adds bfloat16 support for the tflite casting operation, and deprecates tf.lite.Interpreter in favor of ai_edge_litert.interpreter. The release also stops publishing libtensorflow packages, although they remain extractable from the PyPI package. Updates for multi-backend Keras are directed to keras.io. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.19 has been released! Highlights of this release include changes to the C++ API in LiteRT, bfloat16 support for tflite casting, discontinue of releasing libtensorflow packages. Learn more by reading the full release notes. Note: Release updates on the new multi-backend Keras will be published on keras.io, starting with Keras 3.0. For more information, please see https://keras.io/keras_3/. TensorFlow Core LiteRT The public constants tflite::Interpreter:kTensorsReservedCapacity and tflite::Interpreter:kTensorsCapacityHeadroom are now const references, rather than constexpr compile-time constants. (This is to enable better API compatibility for TFLite in Play services while preserving the implementation flexibility to change the values of these constants in the future.) TF-Lite tfl.Cast op is now supporting bfloat16 in the runtime kernel. tf.lite.Interpreter gives a deprecation warning redirecting to its new location at ai_edge_litert.interpreter, as the API tf.lite.Interpreter will be deleted in TF 2.20. See the migration guide for details. Libtensorflow We have stopped publishing libtensorflow packages but it can still be unpacked from the PyPI package. ## What's new in TensorFlow 2.18 DevFeed: [What's new in TensorFlow 2.18]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-10-28T19:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [Tensorflow](), [releases](), [LiteRT](), [NumPy](), [CUDA](), [cudnn](), [NCCL](), [Google]() Tags: [announcement](), [cuda](), [cudnn](), [gpu](), [litert](), [migration](), [nccl](), [nvidia](), [nvidia-rtx](), [python](), [release](), [releases](), [reproducible-builds](), [tensorflow](), [tensorflow-core]() ### AI overview TensorFlow 2.18 introduces NumPy 2.0 compatibility updates, transitions TFLite development to the LiteRT repository, and adds hermetic CUDA, cuDNN, and NCCL dependencies for more reproducible source builds. Binary packages add dedicated kernels for compute capability 8.9 GPUs, including NVIDIA RTX 40 series, L4, and L40, while dropping precompiled support for Maxwell GPUs. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.18 has been released! Highlights of this release (and 2.17) include NumPy 2.0, LiteRT repository, CUDA Update, Hermetic CUDA and more. For the full release notes, please click here. Note: Release updates on the new multi-backend Keras will be published on keras.io, starting with Keras 3.0. For more information, please see https://keras.io/keras_3/. TensorFlow Core NumPy 2.0 The upcoming TensorFlow 2.18 release will include support for NumPy 2.0. While the majority of TensorFlow APIs will function seamlessly with NumPy 2.0, this may break some edge cases of usage, e.g., out-of-boundary conversion errors and numpy scalar representation errors. You can consult the following common solutions. Note that NumPy's type promotion rules have been changed (See NEP 50 for details). This may change the precision at which computations happen, leading either to type errors or to numerical changes to results. Please see the NumPy 2 migration guide. We've updated some TensorFlow tensor APIs to maintain compatibility with NumPy 2.0 while preserving the out-of-boundary conversion behavior in NumPy 1.x. LiteRT Repository We're making some changes to how LiteRT (formerly known as TFLite) is developed. Over the coming months, we'll be gradually transitioning TFLite's codebase to LiteRT. Once the migration is complete, we'll start accepting contributions directly through the LiteRT repository. There will no longer be any binary TFLite releases and developers should switch to LiteRT for the latest updates. Hermetic CUDA If you build TensorFlow from source, Bazel will now download specific versions of CUDA, CUDNN and NCCL distributions, and then use those tools as dependencies in various Bazel targets. This enables more reproducible builds for Google ML projects and supported CUDA versions because the build no longer relies on the locally installed versions. More details are provided here. CUDA Update TensorFlow binary distributions now ship with d ## What's new in TensorFlow 2.17 DevFeed: [What's new in TensorFlow 2.17]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-07-18T16:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [releases]() Tags: [announcement](), [cuda](), [gpu](), [keras](), [python](), [tensorflow](), [tensorflow-core](), [tensorrt]() ### AI overview TensorFlow 2.17 adds dedicated CUDA kernels for compute capability 8.9 GPUs and ends precompiled Python-package support for compute capability 5.0. The article also previews NumPy 2.0 support in TensorFlow 2.18 and the removal of TensorRT support after 2.17. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.17 has been released! Highlights of this release (and 2.16) include CUDA update, upcoming Numpy 2.0, and more. For the full release notes, please click here. Note: Release updates on the new multi-backend Keras will be published on keras.io, starting with Keras 3.0. For more information, please see https://keras.io/keras_3/. TensorFlow Core CUDA Update TensorFlow binary distributions now ship with dedicated CUDA kernels for GPUs with a compute capability of 8.9. This improves the performance on the popular Ada-Generation GPUs like NVIDIA RTX 40**, L4 and L40. To keep Python wheel sizes in check, we made the decision to no longer ship CUDA kernels for compute capability 5.0. That means the oldest NVIDIA GPU generation supported by the precompiled Python packages is now the Pascal generation (compute capability 6.0). For Maxwell support, we either recommend sticking with TensorFlow version 2.16, or compiling TensorFlow from source. The latter will be possible as long as the used CUDA version still supports Maxwell GPUs. Numpy 2.0 Upcoming TensorFlow 2.18 release will include support for Numpy 2.0. This may break some edge cases of TensorFlow API usage. Drop TensorRT support Starting with TensorFlow 2.18, support for TensorRT will be dropped. TensorFlow 2.17 will be the last version to include it. ## What's new in TensorFlow 2.16 DevFeed: [What's new in TensorFlow 2.16]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-03-13T20:11:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Core](), [Keras](), [Compiler](), [LLVM](), [Windows](), [pip](), [Python](), [MSVC]() Tags: [announcement](), [compiler](), [estimator](), [keras](), [llvm](), [msvc](), [python](), [release](), [tensorflow-core](), [windows]() ### AI overview TensorFlow 2.16 introduces Clang as the default compiler for TensorFlow CPU wheels on Windows, makes Keras 3 the default Keras version, and adds Python 3.12 support. The release also removes the tf.estimator API and changes the recommended installation method for Apple Silicon. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.16 has been released! Highlights of this release (and 2.15) include Clang as default compiler for building TensorFlow CPU wheels on Windows, Keras 3 as default version, support for Python 3.12, and much more! For the full release note, please click here. Note: Release updates on the new multi-backend Keras will be published on keras.io starting with Keras 3.0. For more information, please see https://keras.io/keras_3/. TensorFlow Core Clang 17 Clang is now the preferred compiler to build TensorFlow CPU wheels on the Windows Platform starting with this release. The currently supported version is LLVM/clang 17. The official Wheels-published on PyPI will be based on Clang; however, users retain the option to build wheels using the MSVC compiler following the steps mentioned, as has been the case before. Intel owned the implementation and delivery of this change within the 3P Official Build program. Keras 3 Keras 3 will be the default Keras version for TensorFlow 2.16 onwards. You may need to update your script to use Keras 3. Please refer to the new Keras documentation for Keras 3 (https://keras.io/keras_3). Keras 2 will continue to be released alongside TensorFlow as tf_keras. To continue using Keras 2 with TensorFlow 2.16+: Install tf-keras vía pip install tf-keras~=2.16 Switch tf.keras to use Keras 2 (tf-keras), by setting environment variable TF_USE_LEGACY_KERAS=1 directly or in your Python program by doing import os;os.environ["TF_USE_LEGACY_KERAS"]="1". Please note that this needs to be set before importing TensorFlow and will set it for all packages in your Python runtime program. Estimator API tf.estimator API is removed. If you need to use the estimator API, you need to use TF 2.15 or an earlier version. Apple Silicon If you previously installed TensorFlow using pip install tensorflow-macos, please update your installation method. Use pip install tensorflow from now on. tensorflow-macos package will no longer receive ## TensorFlow 2.15 update: hot-fix for Linux installation issue DevFeed: [TensorFlow 2.15 update: hot-fix for Linux installation issue]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-12-05T22:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [pip](), [Linux](), [TensorRT](), [CUDA](), [Python]() Tags: [announcement](), [dependencies](), [explore](), [installation](), [keras](), [linux](), [nvdia](), [nvidia](), [packages](), [release](), [tensorflow](), [tensorflow-core](), [tensorrt](), [update](), [x86-64]() ### AI overview TensorFlow released version 2.15.0.post1 to fix Linux x86_64 installation problems caused by missing TensorRT-related Python dependencies when installing TensorFlow 2.15 with NVIDIA CUDA dependencies via pip. The hotfix restores the intended installation behavior, while users pinning versions must specify 2.15.0.post1 explicitly. ### Source excerpt Posted by the TensorFlow team We are releasing a hot-fix for an installation issue affecting the TensorFlow installation process. The TensorFlow 2.15.0 Python package was released such that it requested tensorrt-related packages that cannot be found unless the user installs them beforehand or provides additional installation flags. This dependency affected anyone installing TensorFlow 2.15 alongside NVIDIA CUDA dependencies via pip install tensorflow[and-cuda]. Depending on the installation method, TensorFlow 2.14 would be installed instead of 2.15, or users could receive an installation error due to those missing dependencies. To solve this issue as quickly as possible, we have released TensorFlow 2.15.0.post1 for the Linux x86_64 platform. This version removes the tensorrt Python package dependencies from the tensorflow[and-cuda] installation method. Support for TensorRT is otherwise unaffected as long as TensorRT is already installed on the system. Now, pip install tensorflow[and-cuda] works as originally intended for TensorFlow 2.15. Using .post1 instead of a full minor release allowed us to push this release out quickly. However, please be aware of the following caveat: for users wishing to pin their Python dependency in a requirements file or other situation, under Python's version specification rules, tensorflow[and-cuda]==2.15.0 will not install this fixed version. Please use ==2.15.0.post1 to specify this exact version on Linux platforms, or a fuzzy version specification, such as ==2.15.*, to specify the most recent compatible version of TensorFlow 2.15 on all platforms. ## What's new in TensorFlow 2.15 DevFeed: [What's new in TensorFlow 2.15]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-11-17T18:55:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Core](), [CUDA](), [cpu](), [Linux](), [pip](), [Windows](), [Compiler]() Tags: [announcement](), [compiler](), [cpu](), [cuda](), [explore](), [installation](), [keras](), [linux](), [nvdia](), [performance](), [release](), [tensorflow](), [tensorflow-core](), [windows]() ### AI overview TensorFlow 2.15 introduces simpler optional installation of NVIDIA CUDA libraries through pip on Linux, provided the NVIDIA driver is already installed. It also enables oneDNN CPU optimizations by default for Windows x64 and x86 packages, adds full tf.function type support, upgrades CUDA to 12.2, and uses Clang 17 for package builds and source compilation. ### Source excerpt Posted by the TensorFlow team TensorFlow 2.15 has been released! Highlights of this release (and 2.14) include a much simpler installation method for NVIDIA CUDA libraries for Linux, oneDNN CPU performance optimizations for Windows x64 and x86, full availability of tf.function types, an upgrade to Clang 17.0.1, and much more! For the full release note, please check here. Note: Release updates on the new multi-backend Keras will be published on keras.io starting with Keras 3.0. For more information, please check here. TensorFlow Core NVIDIA CUDA libraries for Linux The tensorflow pip package has a new, optional installation method for Linux that installs necessary NVIDIA CUDA libraries through pip. As long as the NVIDIA driver is already installed on the system, you may now run pip install tensorflow[and-cuda] to install TensorFlow's NVIDIA CUDA library dependencies in the Python environment. Aside from the NVIDIA driver, no other pre-existing NVIDIA CUDA packages are necessary. In TensorFlow 2.15, CUDA has been upgraded to version 12.2. oneDNN CPU performance optimizations For Windows x64 & x86 packages, oneDNN optimizations are now enabled by default on X86 CPUs. These optimizations can be enabled or disabled by setting the environment variable TF_ENABLE_ONEDNN_OPTS to 1 (enable) or 0 (disable) before running TensorFlow. To fall back to default settings, simply unset the environment variable. tf.function tf.function types are now fully available. tf.types.experimental.TraceType now allows custom tf.function inputs to declare Tensor decomposition and type casting support. Introducing tf.types.experimental.FunctionType as the comprehensive representation of the signature of tf.function callables. It can be accessed through the function_type property of tf.function's and ConcreteFunctions. See the tf.types.experimental.FunctionType documentation for more details. Introducing tf.types.experimental.AtomicFunction as the fastest way to perform TF computations in Python. ## Distributed Fast Fourier Transform in TensorFlow DevFeed: [Distributed Fast Fourier Transform in TensorFlow]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-08-24T17:00:00Z Content type: tutorial Language: en Sources: [The TensorFlow Blog]() Topics: [AI, ML & Data Engineering](), [GPU](), [NCCL]() Tags: [api](), [gpu](), [machine-learning](), [nccl](), [performance](), [tensorflow](), [tensorflow-core]() ### AI overview TensorFlow v2 adds native Distributed FFT support through DTensor. The article explains how sharded tensors can use existing FFT operations across multiple devices, and reports that communication and data shuffling dominate runtime in the demonstrated setup. ### Source excerpt Posted by Ruijiao Sun, Google Intern - DTensor team Fast Fourier Transform is an important method of signal processing, which is commonly used in a number of ways, including speeding up convolutions, extracting features, and regularizing models. Distributed Fast Fourier Transform (Distributed FFT) offers a way to compute Fourier Transforms in models that work with image-like datasets that are too large to fit into the memory of a single accelerator device. In a previous Google Research Paper, "Large-Scale Discrete Fourier Transform on TPUs" by Tianjian Lu, a Distributed FFT algorithm was implemented for TensorFlow v1 as a library. This work presents the newly added native support in TensorFlow v2 for Distributed FFT, through the new TensorFlow distribution API, DTensor. About DTensor DTensor is an extension to TensorFlow for synchronous distributed computing. It distributes the program and tensors through a procedure called Single program, multiple data (SPMD) extension. DTensor offers an uniform API for traditional data and model parallelism patterns used widely in Machine Learning. Example Usage The API interface for distributed FFT is the same as the original FFT in TensorFlow. Users just need to pass a sharded tensor as an input to the existing FFT ops in TensorFlow, such as tf.signal.fft2d. The output of a distributed FFT becomes sharded too. import TensorFlow as tf from TensorFlow.experimental import dtensor # Set up devices device_type = dtensor.preferred_device_type() if device_type == 'CPU': cpu = tf.config.list_physical_devices(device_type) tf.config.set_logical_device_configuration(cpu[0], [tf.config.LogicalDeviceConfiguration()] * 8) if device_type == 'GPU': gpu = tf.config.list_physical_devices(device_type) tf.config.set_logical_device_configuration(gpu[0], [tf.config.LogicalDeviceConfiguration(memory_limit=1000)] * 8) dtensor.initialize_accelerator_system() # Create a mesh mesh = dtensor.create_distributed_mesh(mesh_dims=[('x', 1), ('y', 2), ('z', 4)], ## What's new in TensorFlow 2.13 and Keras 2.13? DevFeed: [What's new in TensorFlow 2.13 and Keras 2.13?]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-07-25T16:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Core](), [Keras](), [TensorFlow Lite](), [datasets](), [API]() Tags: [apple](), [datasets](), [google](), [keras](), [mac](), [python](), [quantization](), [release](), [tensorflow](), [tensorflow-core](), [tensorflow-lite](), [usability]() ### AI overview TensorFlow 2.13 and Keras 2.13 introduce Apple Silicon wheels, make the Keras V3 format the default for .keras files, and add improvements to TensorFlow Lite, tf.data, dataset padding, and CPU BF16 execution. ### Source excerpt Posted by the TensorFlow and Keras Teams TensorFlow 2.13 and Keras 2.13 have been released! Highlights of this release include publishing Apple Silicon wheels, the new Keras V3 format being default for .keras extension files and many more! TensorFlow Core Apple Silicon wheels for TensorFlow TensorFlow 2.13 is the first version to provide Apple Silicon wheels, which means when you install TensorFlow on an Apple Silicon Mac, you will be able to use the latest version of TensorFlow. The nightly builds for Apple Silicon wheels were released in March 2023 and this new support will enable more fine-grained testing, thanks to technical collaboration between Apple, MacStadium, and Google. tf.lite The Python TensorFlow Lite Interpreter bindings now have an option to use experimental_disable_delegate_clustering flag to turn-off delegate clustering during delegate graph partitioning phase. You can set this flag in TensorFlow Lite interpreter Python API interpreter = new Interpreter(file_of_a_tensorflowlite_model, experimental_preserve_all_tensors=False) The flag is set to False by default. This is an advanced feature in experimental that is designed for people who insert explicit control dependencies via with tf.control_dependencies() or need to change graph execution order. Besides, there are several operator improvements in TensorFlow Lite in 2.13 add operation now supports broadcasting up to 6 dimensions. This will remove explicit broadcast ops from many models. The new implementation is also much faster than the current one which calculates the entire index for both inputs the the input instead of only calculating the part that changes. Improve the coverage for 16x8 quantization by enabling int16x8 ops for exp, mirror_pad, space_to_batch_nd, batch_to_space_nd Increase the coverage of integer data types enabled int16 for less, greater_than, equal, bitcast, bitwise_xor, right_shift, top_k, mul, and int16 indices for gather and gather_nd enabled int8 for floor_div and floor_m