What's new in TensorFlow 2.20

What's new in TensorFlow 2.19

March 13, 2025 — Posted by the TensorFlow teamTensorFlow 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, plea… 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/. The public constants tflite::Interpreter:kTensorsReservedCapacity and tflite::Interpreter:kTensorsCapacityHeadroom are now const references, rather than constexpr compile-time constants. (This is to enable…

Introducing Wake Vision: A High-Quality, Large-Scale Dataset for TinyML Computer Vision Applications

December 05, 2024 — Posted by Colby Banbury, Emil Njor, Andrea Mattia Garavagno, Vijay Janapa Reddi – Harvard UniversityTinyML is an exciting frontier in machine learning, enabling models to run on extremely low-power devices such as microcontrollers and edge devices. However, the growth of this field has been stifled by a lack of tailored large and high-quality datasets. That's where Wake Vision comes in—a new … TinyML is an exciting frontier in machine learning, enabling models to run on extremely low-power devices such as microcontrollers and edge devices. However, the growth of this field has been stifled by a lack of tailored large and high-quality datasets. That's where Wake Vision comes in—a new dataset designed to accelerate research and development in TinyML. The development of TinyML requires compact and efficient models, often only a few hundred kilobytes in size. The…

MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering

November 19, 2024 — Posted by Jason Jabbour, Kai Kleinbard and Vijay Janapa Reddi (Harvard University)Everyone wants to do the modeling work, but no one wants to do the engineering.If ML developers are like astronauts exploring new frontiers, ML systems engineers are the rocket scientists designing and building the engines that take them there.Introduction"Everyone wants to do modeling, but no one wants to do t… If ML developers are like astronauts exploring new frontiers, ML systems engineers are the rocket scientists designing and building the engines that take them there. "Everyone wants to do modeling, but no one wants to do the engineering," highlights a stark reality in the machine learning (ML) world: the allure of building sophisticated models often overshadows the critical task of engineering them into robust, scalable, and efficient systems. The reality is that ML and systems…

What's new in TensorFlow 2.18

October 28, 2024 — Posted by the TensorFlow teamTensorFlow 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/.TensorFl… 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/. 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…

What's new in TensorFlow 2.17

July 18, 2024 — Posted by the TensorFlow teamTensorFlow 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 CoreCUDA UpdateTensor… 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 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…

Faster Dynamically Quantized Inference with XNNPack

April 09, 2024 — Posted by Alan Kelly, Software EngineerWe are excited to announce that XNNPack’s Fully Connected and Convolution 2D operators now support dynamic range quantization. XNNPack is TensorFlow Lite’s CPU backend and CPUs deliver the widest reach for ML inference and remain the default target for TensorFlow Lite. Consequently, improving CPU inference performance is a top priority. We quadrupled inferen… We are excited to announce that XNNPack’s Fully Connected and Convolution 2D operators now support dynamic range quantization. XNNPack is TensorFlow Lite’s CPU backend and CPUs deliver the widest reach for ML inference and remain the default target for TensorFlow Lite. Consequently, improving CPU inference performance is a top priority. We quadrupled inference performance in TensorFlow Lite’s XNNPack backend compared to the single precision baseline by adding support for dynamic range quantization to the Fully Connected and Convolution…

What's new in TensorFlow 2.16

March 13, 2024 — Posted by the TensorFlow teamTensorFlow 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 … 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/. Clang is now the preferred compiler to…

Graph neural networks in TensorFlow

February 06, 2024 — Posted by Dustin Zelle – Software Engineer, Research and Arno Eigenwillig – Software Engineer, CoreMLThis article is also shared on the Google Research Blog Objects and their relationships are ubiquitous in the world around us, and relationships can be as important to understanding an object as its own attributes viewed in isolation — for example: transportation networks, production networks, know… This article is also shared on the Google Research Blog Objects and their relationships are ubiquitous in the world around us, and relationships can be as important to understanding an object as its own attributes viewed in isolation — for example: transportation networks, production networks, knowledge graphs, or social networks. Discrete mathematics and computer science have a long history of formalizing such networks them as graphs, consisting of nodes arbitrarily connected by edges in various irregular…

TensorFlow 2.15 update: hot-fix for Linux installation issue

December 05, 2023 — Posted by the TensorFlow teamWe 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 alongsi… 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…

Half-precision Inference Doubles On-Device Inference Performance

November 29, 2023 — Posted by Marat Dukhan and Frank Barchard, Software EngineersCPUs deliver the widest reach for ML inference and remain the default target for TensorFlow Lite. Consequently, improving CPU inference performance is a top priority, and we are excited to announce that we doubled floating-point inference performance in TensorFlow Lite’s XNNPack backend by enabling half-precision inference on ARM CPUs. … Posted by Marat Dukhan and Frank Barchard, Software Engineers CPUs deliver the widest reach for ML inference and remain the default target for TensorFlow Lite. Consequently, improving CPU inference performance is a top priority, and we are excited to announce that we doubled floating-point inference performance in TensorFlow Lite’s XNNPack backend by enabling half-precision inference on ARM CPUs. This means that more AI powered features may be deployed to older and lower tier devices. Traditionally, TensorFlow Lite supported…

Join us at the third Women in ML Symposium!

November 17, 2023 — Posted by Sharbani Roy – Senior Director, Product Management, Google We're back with the third annual Women in Machine Learning Symposium on December 7, 2023! Join us virtuallyfrom 9:30 am to 1:00 pm PT for an immersive and insightful set of deep dives for every level of Machine Learning experience. The Women in ML Symposium is an inclusive event for anyone passionate about the transformative … The Women in ML Symposium is an inclusive event for anyone passionate about the transformative fields of Machine Learning (ML) and Artificial Intelligence (AI). Dive into the latest advancements in generative AI, explore the intricacies of privacy-preserving AI, dig into the underlying accelerators and ML frameworks that power models, and uncover practical applications of ML across multiple industries. Our event offers sessions for all expertise levels, from beginners to advanced…

What's new in TensorFlow 2.15

November 17, 2023 — Posted by the TensorFlow teamTensorFlow 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 th… 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. The tensorflow pip package has a new, optional installation method for Linux that installs necessary NVIDIA CUDA libraries through pip. As long…

Simulated Spotify Listening Experiences for Reinforcement Learning with TensorFlow and TF-Agents

October 19, 2023 — Posted by Surya Kanoria, Joseph Cauteruccio, Federico Tomasi, Kamil Ciosek, Matteo Rinaldi, and Zhenwen Dai – SpotifyIntroductionMany of our music recommendation problems involve providing users with ordered sets of items that satisfy users’ listening preferences and intent at that point in time. We base current recommendations on previous interactions with our application and, in the abstract, a… Many of our music recommendation problems involve providing users with ordered sets of items that satisfy users’ listening preferences and intent at that point in time. We base current recommendations on previous interactions with our application and, in the abstract, are faced with a sequential decision making process as we continually recommend content to users. Reinforcement Learning (RL) is an established tool for sequential decision making that can be leveraged to solve sequential recommendation problems. We decided to explore…

Building a board game with the TFLite plugin for Flutter

October 18, 2023 — Posted by Wei Wei, Developer AdvocateIn our previous blog posts Building a board game app with TensorFlow: a new TensorFlow Lite reference app and Building a reinforcement learning agent with JAX, and deploying it on Android with TensorFlow Lite, we demonstrated how to train a reinforcement learning (RL) agent with TensorFlow, TensorFlow Agents and JAX respectively, and then deploy the converted … Posted by Wei Wei, Developer Advocate In our previous blog posts Building a board game app with TensorFlow: a new TensorFlow Lite reference app and Building a reinforcement learning agent with JAX, and deploying it on Android with TensorFlow Lite, we demonstrated how to train a reinforcement learning (RL) agent with TensorFlow, TensorFlow Agents and JAX respectively, and then deploy the converted TFLite model in an Android app using TensorFlow Lite, to play a…

People of AI: Season 2

October 17, 2023 — Posted by Ashley OldacreIf you are joining us for the first time, you can binge listen to our amazing 8 episodes from Season 1 wherever you get your podcasts.We are back for another season of People of AI with a new lineup of incredible guests! I am so excited to introduce my new co-host Luiz Gustavo Martins as we meet inspiring people with interesting stories in the field of Artificial Intellige… Posted by Ashley Oldacre If you are joining us for the first time, you can binge listen to our amazing 8 episodes from Season 1 wherever you get your podcasts. We are back for another season of People of AI with a new lineup of incredible guests! I am so excited to introduce my new co-host Luiz Gustavo Martins as we meet inspiring people with…

Pre-processing temporal data made easier with TensorFlow Decision Forests and Temporian

September 11, 2023 — Posted by Google: Mathieu Guillame-Bert, Richard Stotz, Robert Crowe, Luiz GUStavo Martins (Gus), Ashley Oldacre, Kris Tonthat, Glenn Cameron, and Tryolabs: Ian Spektor, Braulio Rios, Guillermo Etchebarne, Diego Marvid, Lucas Micol, Gonzalo Marín, Alan Descoins, Agustina Pizarro, Lucía Aguilar, Martin Alcala RubiTemporal data is omnipresent in applied machine learning applications. Data often cha… Time series are the most commonly used representation for temporal data. They consist of uniformly sampled values, which can be useful for representing aggregate signals. However, time series are sometimes not sufficient to represent the richness of available data. Instead, multivariate time series can represent multiple signals together, while time sequences or event sets can represent non-uniformly sampled measurements. Multi-index time sequences can be used to represent relations between different time sequences. In this blog post, we will use the multivariate multi-index time…

Distributed Fast Fourier Transform in TensorFlow

August 24, 2023 — Posted by Ruijiao Sun, Google Intern - DTensor teamFast 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 … 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…

The TensorFlow Lite Plugin for Flutter is Officially Available

August 18, 2023 — Posted by Paul Ruiz, Developer Relations EngineerWe're excited to announce that the TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released! Three years ago, Amish Garg, one of our talented Google Summer of Code contributors, wrote a widely used TensorFlow Lite plugin for Flutter. The plugin was so popular that we decided to migrate it to… We're excited to announce that the TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released! Three years ago, Amish Garg, one of our talented Google Summer of Code contributors, wrote a widely used TensorFlow Lite plugin for Flutter. The plugin was so popular that we decided to migrate it to our official repo, making it easier to maintain directly by the Google team. We are grateful…

Simpleperf case study: Fast initialization of TFLite’s Memory Arena

August 09, 2023 — Posted by Alan Kelly, Software EngineerOne of our previous articles, Optimizing TensorFlow Lite Runtime Memory, discusses how TFLite’s memory arena minimizes memory usage by sharing buffers between tensors. This means we can run models on even smaller edge devices. In today’s article, I will describe the performance optimization of the memory arena initialization so that our users get the benefit… One of our previous articles, Optimizing TensorFlow Lite Runtime Memory, discusses how TFLite’s memory arena minimizes memory usage by sharing buffers between tensors. This means we can run models on even smaller edge devices. In today’s article, I will describe the performance optimization of the memory arena initialization so that our users get the benefit of low memory usage with little additional overhead. ML is normally deployed on-device as part of a larger pipeline. TFLite is…

What's new in TensorFlow 2.13 and Keras 2.13?

July 25, 2023 — Posted by the TensorFlow and Keras TeamsTensorFlow 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 CoreApple Silicon wheels for TensorFlowTensorFlow 2.13 is the first version to provide Apple Silicon wheels, which means when you install TensorFlow o… 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 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…

On-device fetal ultrasound assessment with TensorFlow Lite

June 20, 2023 — Posted by Angelica Willis and Akib Uddin, Health AI Team, Google ResearchHow researchers at Google are working to expand global access to maternal healthcare with the help of AITensorFlow Lite* is an open-source framework to run machine learning models on mobile and edge devices. It’s popular for use cases ranging from image classification, object detection, speech recognition, natural language t… TensorFlow Lite* is an open-source framework to run machine learning models on mobile and edge devices. It’s popular for use cases ranging from image classification, object detection, speech recognition, natural language tasks, and more. From helping parents of deaf children learn sign language, to predicting air quality, projects using TensorFlow Lite are demonstrating how on-device ML could directly and positively impact lives by making these socially beneficial applications of AI more accessible, globally. In this post,…

Augmenting recommendation systems with LLMs

June 06, 2023 — Posted by Wei Wei, Developer AdvocateLarge language models (LLMs) are taking the world by storm, thanks to their powerful ability to generate text, translate languages, and answer questions in a coherent and informative way. At Google I/O 2023, we released the PaLM API as ‘public preview’ so that many developers can start building apps with it. While PaLM API already has excellent documentation o… Large language models (LLMs) are taking the world by storm, thanks to their powerful ability to generate text, translate languages, and answer questions in a coherent and informative way. At Google I/O 2023, we released the PaLM API as ‘public preview’ so that many developers can start building apps with it. While PaLM API already has excellent documentation on its extensive usage and best practices, in this blog we are going to…

Visualizing and interpreting decision trees

June 06, 2023 — Posted by Terence Parr, GoogleDecision trees are the fundamental building block of Gradient Boosted Trees and Random Forests, the two most popular machine learning models for tabular data. To learn how decision trees work and how to interpret your models, visualization is essential.TensorFlow recently published a new tutorial that shows how to use dtreeviz, a state-of-the-art visualization librar… Decision trees are the fundamental building block of Gradient Boosted Trees and Random Forests, the two most popular machine learning models for tabular data. To learn how decision trees work and how to interpret your models, visualization is essential. TensorFlow recently published a new tutorial that shows how to use dtreeviz, a state-of-the-art visualization library, to visualize and interpret TensorFlow Decision Forest Trees. The dtreeviz library, first released in 2018, is now the most popular visualization library for…

Attend our first Developer Summit on Recommendation Systems

May 26, 2023 — Posted by Wei Wei, Developer AdvocateRegister for the Summit here!Recommendation systems are everywhere. They power our favorite websites, apps, and services, helping us find the things we enjoy. But how do modern recommenders work? What are the key components and how do they fit together? How can we make them even better?Since we launched our recommendation system landing page last year, we have… Register for the Summit here! Recommendation systems are everywhere. They power our favorite websites, apps, and services, helping us find the things we enjoy. But how do modern recommenders work? What are the key components and how do they fit together? How can we make them even better? Since we launched our recommendation system landing page last year, we have heard many positive feedback from our developer community. While many developers find the…