# Tensorflow Published articles for Tensorflow. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## PyTorch vs. TensorFlow: Differences, Performance, and How to Choose DevFeed: [PyTorch vs. TensorFlow: Differences, Performance, and How to Choose]() Original publisher: [Read original article]() Author: NICOLAS PIRO, DATA SCIENTIST AND AI DEVELOPER @ TOPTAL Published: 2026-08-27T04:00:00Z Content type: comparison Language: en Sources: [Toptal Blog]() Topics: [Training AI Models]() Tags: [deep-learning](), [deployment](), [experimentation](), [infrastructure](), [performance](), [pytorch](), [tensorflow](), [tooling]() ### AI overview A comparison of PyTorch and TensorFlow for deep-learning experimentation, model design, production workflows, tooling, and infrastructure considerations. ### Source excerpt This comprehensive guide explores how PyTorch and TensorFlow shape deep-learning work in 2026, from experimentation and model design to production workflows, ecosystem tooling, and infrastructure considerations. ## Third Stage Engineering DevFeed: [Third Stage Engineering]() Original publisher: [Read original article]() Published: 2025-11-16T13:00:00Z Content type: opinion Language: en Sources: [Brendan Gregg's Blog]() Topics: [Hardware](), [benchmarking](), [Software](), [intel](), [Tensorflow]() Tags: [benchmark](), [blog](), [engineering](), [evaluation](), [hardware](), [intel](), [performance](), [software](), [tensorflow]() ### AI overview The article argues that production hardware performance depends on three stages: hardware, software, and tuning. It presents tuning, or "third-stage engineering," as involving people, training, tools, and capabilities, and recommends evaluating all hardware options with appropriate software and tuning for realistic workload performance. ### Source excerpt The real performance of any computer hardware in production is the result of the hardware, software, and tuning; the investment and sequence of these efforts can be pictured as a three-stage rocket: I recently presented this embarrassingly simple diagram to Intel's executive leadership, and at the time realized the value of sharing it publicly. The Internet is awash with comparisons about Intel (and other vendors') product performance based on hardware performance alone, but the performance of software and then tuning can make a huge difference for your particular workload. You need all three stages to reach the highest, and most competitive, performance. It's obvious why this is important for HW vendors to understand internally - they, like the Internet, can get overly focused on HW alone. But customers need to understand it as well. If a benchmark is comparing TensorFlow performance between HW vendors, was the Intel hardware tested using the Intel Extension for TensorFlow Software, and was it then tuned? The most accurate and realistic evaluation for HW involves selecting the best software and then tuning it, and doing this for all HW options. I spend a lot of time on the final stage, tuning - what I call third-stage engineering. It's composed of roughly four parts: People, training, tools, and capabilities. You need staff, you need them trained to understand performance methodologies and SW and HW internals, they need tools to analyze the system (both observational and experimental), and finally they need capabilities to tune (tunable parameters, settings, config, code changes, etc.). I see too many HW evaluations that are trying to understand customer performance but are considering HW alone, which is like only testing the first stage of a rocket. This doesn't help vendors or customers. I hope that's what my simple diagram makes obvious: We need all three stages to reach the highest altitude. ## 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 ## How to Generate New Art From Existing Images Using Deep Learning? DevFeed: [How to Generate New Art From Existing Images Using Deep Learning?]() Original publisher: [Read original article]() Author: Damilare Keshinro Published: 2025-07-24T04:56:41Z Content type: tutorial Language: en Sources: [Baeldung - CS]() Topics: [Deep neural networks](), [Tensorflow](), [Tutorial](), [Image](), [Computer science]() Tags: [building](), [computer-science](), [computer-vision](), [computer-vision-image-processing](), [deep-learning](), [generate](), [how-to](), [image-processing](), [images](), [model](), [techniques](), [tensorflow]() ### AI overview A tutorial on neural style transfer using TensorFlow and a pretrained VGG16 model. It explains how to combine the content of an owl image with the artistic style of Edvard Munch's "Scream" by extracting features, defining losses, preprocessing images, and optimizing the result. ### Source excerpt A walkthrough of building an art style transfer program using deep learning techniques and tools like TensorFlow and the VGG16 model. The post How to Generate New Art From Existing Images Using Deep Learning? first appeared on Baeldung on Computer Science. ## How Deliveroo Built Its Machine Learning Platform DevFeed: [How Deliveroo Built Its Machine Learning Platform]() Original publisher: [Read original article]() Author: Saikrishna Desaraju Published: 2025-07-02T00:00:00Z Content type: article Language: en Sources: [Deliveroo]() Topics: [Machine Learning & Artificial Intelligence](), [Deployment](), [model-deployment](), [Kubernetes](), [Amazon Web Services](), [distributed-training](), [Inference](), [Python](), [PyTorch](), [Tensorflow]() Tags: [aws](), [deployment](), [distributed-training](), [inference](), [kubernetes](), [machine-learning](), [machine-learning-platform](), [python](), [pytorch](), [rest](), [tensorflow]() ### AI overview Deliveroo describes its machine learning platform for standardizing model-building and deployment workflows. The platform combines open-source tools, in-house components, Kubernetes on AWS EKS, TensorFlow, PyTorch, Metaflow, and Argo; it also supports GPU and distributed training and includes Inferoo for real-time model predictions. ### Source excerpt Enter Deliveroo's ML Platform For the past three years, we have been building Deliveroo's Machine Learning Platform, or the ML Platform as we like to call it. The ML Platform boosts our model-building and deployment capabilities by standardising ML workflows, streamlining the end-to-end development process and simplifying model deployment. Besides saving software engineering effort through centralising tooling, the ML Platform also reduces the time that our ML engineers spend on infrastructure tasks. As a result, our ML engineers can now iterate their ML models 2-3x faster than before. What makes our ML Platform tick? At the core of our ML Platform lies a carefully curated tech stack - an integrated suite of infrastructure tools, services, and libraries. It blends robust open source technologies with purpose-built, in-house components. Key open source tools include Kubernetes, Argo, and Metaflow, all seamlessly connected with leading ML frameworks like TensorFlow and PyTorch. We choose mature, community-driven solutions and actively contribute back where we can. This entire ecosystem is powered by AWS, running on EKS, and anchored by our data warehouse. In the sections that follow, we'll take a closer look at the key components that drive our ML Platform. Metaflow To give ML engineers seamless access to scalable compute on Kubernetes, we use Metaflow, a powerful Python library that helps break down complex model-building workflows into smaller, manageable jobs. These jobs are orchestrated by Argo, one of the backbone tools in our infrastructure. One of Metaflow's biggest strengths is its flexibility. It allows engineers to move effortlessly between local development, staging on Kubernetes, and production deployment--helping teams iterate quickly as they experiment and scale. Recently, we added support for GPUs and distributed training, enabling faster training times and the ability to work with much larger datasets. Inferoo For models that require real-time predictio ## 4M Models Scanned: Protect AI + Hugging Face 6 Months In DevFeed: [4M Models Scanned: Protect AI + Hugging Face 6 Months In]() Original publisher: [Read original article]() Author: Sean Morgan Published: 2025-04-14T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [Machine Learning, Security Attacks](), [Threat Research](), [Security](), [threat detection](), [Zero Trust](), [obfuscation](), [vulnerability](), [payload](), [Script](), [llamafile](), [Tensorflow]() Tags: [ai-ml](), [hub](), [hugging-face](), [keras](), [llamafile](), [obfuscation](), [partnerships](), [payload](), [security](), [tensorflow](), [threat-detection](), [vulnerability](), [zero-trust]() ### AI overview Hugging Face and Protect AI report six months of collaboration on Guardian, a scanning system that improves security checks for models hosted on the Hugging Face Hub. Guardian added four detection modules, expanded format and obfuscation coverage, identified a Keras vulnerability, and provides inline alerts and vulnerability reports. The system uses a zero trust approach that treats arbitrary code execution as unsafe, including code hidden through obfuscation. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## 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. ## MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering DevFeed: [MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-11-19T17:00:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [AI, ML & Data Engineering](), [Training AI Models]() Tags: [ai](), [community](), [data-engineering](), [deployment](), [hardware](), [inference](), [infrastructure](), [large-language-models-llms](), [machine-learning](), [model-development](), [monitoring-maintenance](), [optimization](), [socratiq](), [systems-engineering](), [tensorflow]() ### AI overview An overview of MLSysBook.ai's machine-learning systems engineering concepts, connected to the TensorFlow ecosystem. It emphasizes the infrastructure, hardware, scaling, deployment, efficiency, and reliability considerations required to build and operate machine-learning systems. ### Source excerpt 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 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 are inextricably linked. Models, no matter how innovative, are computationally demanding and require substantial resources--with the rise of generative AI and increasingly complex models, understanding how ML infrastructure scales becomes even more critical. Ignoring the system's limitations during model development is a recipe for disaster. Unfortunately, educational resources on the systems side of machine learning are lacking. There are plenty of textbooks and materials on deep learning theory and concepts. However, we truly need more resources on the infrastructure and systems side of machine learning. Critical questions--such as how to optimize models for specific hardware, deploy them at scale, and ensure system efficiency and reliability--are still not adequately understood by ML practitioners. This lack of understanding is not due to disinterest but rather a gap in available knowledge. One significant resource addressing this gap is MLSysBook.ai. This blog post explores key ML systems engineering concepts from MLSysBook.ai and maps them to the TensorFlow ecosystem to provide practical insights for building efficient ML systems. The Connection Between Machine Learning and Systems Many think machine learning is solely about extracting patterns and insights from data. While this is fundamental, it's only part of the s ## 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 ## "Llama 3.2 in Keras" DevFeed: ["Llama 3.2 in Keras"]() Original publisher: [Read original article]() Author: Martin Görner Published: 2024-10-21T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Keras](), [llama3](), [Large Language Model](), [tokenization](), [Fine-tuning](), [PyTorch](), [Tensorflow](), [Transformers]() Tags: [fine-tuning](), [keras](), [llama3](), [llm](), [llms](), [open-source](), [pytorch](), [tensorflow](), [tokenization](), [training](), [transformers]() ### AI overview The article introduces Llama 3.2 support in Keras, explaining that Hugging Face checkpoints can be loaded directly and converted on the fly when needed. It also describes Keras and keras-hub support for multiple backends, pretrained models, tokenization, preprocessing, training, and fine-tuning. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## 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. ## Announcing New Hugging Face and KerasHub integration DevFeed: [Announcing New Hugging Face and KerasHub integration]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty Published: 2024-07-10T00:00:00Z Content type: news Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [Keras](), [Transformers](), [gemma](), [llama](), [Open Source]() Tags: [gemma](), [hugging-face](), [json](), [keras](), [llama](), [nlp](), [open-source](), [open-source-collab](), [pytorch](), [tensorflow](), [transformers]() ### AI overview The article announces an integration between Hugging Face Transformers and KerasHub through a shared model save format. It allows KerasHub users to load many Transformers checkpoints, initially including Gemma, Llama 3, and PaliGemma, and use them with TensorFlow, JAX, or PyTorch backends. The integration handles conversion of configuration variables, weight names, and tokenizer vocabularies internally. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Deep Learning Frameworks for Beginners: A Comparison of TensorFlow vs. PyTorch vs. Keras DevFeed: [Deep Learning Frameworks for Beginners: A Comparison of TensorFlow vs. PyTorch vs. Keras]() Original publisher: [Read original article]() Author: admin Published: 2024-05-14T10:48:43Z Content type: comparison Language: en Sources: [Posts on Chris Banes]() Topics: [Deep learning](), [Frameworks](), [Tensorflow](), [PyTorch]() Tags: [ai](), [comparison](), [deep-learning](), [frameworks](), [pytorch](), [tensorflow](), [uncategorized](), [vs]() ### AI overview This beginner-oriented comparison introduces deep learning frameworks and discusses TensorFlow, PyTorch, and Keras. It explains how frameworks support the design, training, and testing of deep learning models, with particular attention to TensorFlow's scalability, mobile deployment through TensorFlow Lite, and production pipelines through TFX. ### Source excerpt Have you ever wondered how computers can recognize faces, understand your voice commands, or even beat you at a game of chess? That's deep learning in action! Deep learning is a powerful part of artificial intelligence (AI) that teaches computers to learn from data, just like humans do. Deep learning frameworks are handy tools, enabling [...] ## Faster Dynamically Quantized Inference with XNNPack DevFeed: [Faster Dynamically Quantized Inference with XNNPack]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-04-09T16:00:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [Inference Performance](), [quantization](), [TensorFlow Lite](), [cpu](), [Machine learning](), [model-deployment]() Tags: [ai](), [announcement](), [article](), [cpu](), [inference](), [inference-performance](), [learn](), [ml](), [performance](), [precision](), [quantization](), [range](), [scale](), [tensorflow](), [tensorflow-lite](), [tensors]() ### AI overview This article explains how dynamic range quantization for XNNPack's Fully Connected and Convolution 2D operators improves CPU inference performance in TensorFlow Lite. The approach quantizes weights to 8-bit integers during model conversion and dynamically quantizes activations during inference, delivering most of the performance benefits of full quantization while retaining higher overall accuracy. The article reports a fourfold performance improvement over the single-precision baseline and describes how this can enable AI-powered features on older and lower-tier devices. ### Source excerpt Posted by Alan Kelly, Software Engineer 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 operators. This means that more AI powered features may be deployed to older and lower tier devices. Previously, XNNPack offered users the choice between either full integer quantization, where the weights and activations are stored as signed 8-bit integers, or half-precision (fp16) or single-precision (fp32) floating-point inference. In this article we demonstrate the benefits of dynamic range quantization. Dynamic Range Quantization Dynamically quantized models are similar to fully-quantized models in that the weights for the Fully Connected and Convolution operators are quantized to 8-bit integers during model conversion. All other tensors are not quantized, they remain as float32 tensors. During model inference, the floating-point layer activations are converted to 8-bit integers before being passed to the Fully Connected and Convolution operators. The quantization parameters (the zero point and scale) for each row of the activation tensor are calculated dynamically based on the observed range of activations. This maximizes the accuracy of the quantization process as the activations make full use of the 8 quantized bits. In fully-quantized models, these parameters are fixed during model conversion, based on the range of the activation values observed using a representative dataset. The second difference between full quantization and dynamic range quantization is that the output of the Fully ## Total noob's intro to Hugging Face Transformers DevFeed: [Total noob's intro to Hugging Face Transformers]() Original publisher: [Read original article]() Author: Andrew Jardine Published: 2024-03-22T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Transformers](), [hugging face](), [Machine learning](), [Open Source](), [Natural language processing](), [Library](), [Python](), [spaces](), [datasets](), [Computer vision](), [PyTorch](), [Tensorflow]() Tags: [artificial-intelligence](), [community](), [computer-vision](), [datasets](), [frameworks](), [github](), [guide](), [hugging-face](), [library](), [machine-learning](), [open-source](), [python](), [pytorch](), [science](), [source](), [spaces](), [tensorflow](), [transformers](), [vision]() ### AI overview This beginner-friendly guide introduces Hugging Face Transformers and open-source machine learning to readers without prior technical or Python knowledge. It explains the Transformers library, pretrained models, supported tasks and frameworks, the Hugging Face Hub for sharing models and datasets, and Spaces for building and hosting web-based machine learning demos and applications. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Graph neural networks in TensorFlow DevFeed: [Graph neural networks in TensorFlow]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-02-06T19:17:00Z Content type: release Language: en Sources: [Google Research]() Topics: [Graphs](), [Tensorflow](), [Library](), [Google](), [Neural Network]() Tags: [google](), [graph](), [graph-mining](), [graphs](), [library](), [machine-learning](), [neural-networks](), [release](), [tensorflow]() ### AI overview Google Research announces TensorFlow GNN 1.0, a production-tested library for building and training graph neural networks at large scales. The library supports heterogeneous graphs, TensorFlow modeling and training, and extracting input graphs from large data stores. ### Source excerpt Posted by Dustin Zelle, Software Engineer, Google Research, and Arno Eigenwillig, Software Engineer, CoreML 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 -- take for example transportation networks, production networks, knowledge graphs, or social networks. Discrete mathematics and computer science have a long history of formalizing such networks as graphs, consisting of nodes connected by edges in various irregular ways. Yet most machine learning (ML) algorithms allow only for regular and uniform relations between input objects, such as a grid of pixels, a sequence of words, or no relation at all. Graph neural networks, or GNNs for short, have emerged as a powerful technique to leverage both the graph's connectivity (as in the older algorithms DeepWalk and Node2Vec) and the input features on the various nodes and edges. GNNs can make predictions for graphs as a whole (Does this molecule react in a certain way?), for individual nodes (What's the topic of this document, given its citations?) or for potential edges (Is this product likely to be purchased together with that product?). Apart from making predictions about graphs, GNNs are a powerful tool used to bridge the chasm to more typical neural network use cases. They encode a graph's discrete, relational information in a continuous way so that it can be included naturally in another deep learning system. We are excited to announce the release of TensorFlow GNN 1.0 (TF-GNN), a production-tested library for building GNNs at large scales. It supports both modeling and training in TensorFlow as well as the extraction of input graphs from huge data stores. TF-GNN is built from the ground up for heterogeneous graphs, where types of objects and relations are represented by distinct sets of nodes and edges. Real-world objects and their relations occur in distinct types, and TF-GNN's heterogen ## Graph neural networks in TensorFlow DevFeed: [Graph neural networks in TensorFlow]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-02-06T19:00:00Z Content type: release Language: en Sources: [The TensorFlow Blog]() Topics: [AI, ML & Data Engineering](), [Training AI Models]() Tags: [ai](), [deep-learning](), [graph-mining](), [graphs](), [keras](), [learn](), [machine-learning](), [networks](), [python](), [release](), [release-notes](), [tensorflow](), [training]() ### AI overview Announces TensorFlow GNN 1.0, a production-tested library for building and training large-scale graph neural networks, including heterogeneous graphs. ### Source excerpt Posted by Dustin Zelle - Software Engineer, Research and Arno Eigenwillig - Software Engineer, CoreML 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 ways. Yet most machine learning (ML) algorithms allow only for regular and uniform relations between input objects, such as a grid of pixels, a sequence of words, or no relation at all. Graph neural networks, or GNNs for short, have emerged as a powerful technique to leverage both the graph's connectivity (as in the older algorithms DeepWalk and Node2Vec) and the input features on the various nodes and edges. GNNs can make predictions for graphs as a whole (Does this molecule react in a certain way?), for individual nodes (What's the topic of this document, given its citations?) or for potential edges (Is this product likely to be purchased together with that product?). Apart from making predictions about graphs, GNNs are a powerful tool used to bridge the chasm to more typical neural network use cases. They encode a graph's discrete, relational information in a continuous way so that it can be included naturally in another deep learning system. We are excited to announce the release of TensorFlow GNN 1.0 (TF-GNN), a production-tested library for building GNNs at large scale. It supports both modeling and training in TensorFlow as well as the extraction of input graphs from huge data stores. TF-GNN is built from the ground up for heterogeneous graphs where types and relations are represented by distinct sets of nodes and edges. Real-world objects and their relatio ## 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. ## Python support for Cloud Functions for Firebase reaches general availability DevFeed: [Python support for Cloud Functions for Firebase reaches general availability]() Original publisher: [Read original article]() Author: Chris Gill; Jeff Huleatt Published: 2023-11-16T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Python](), [Cloud Functions](), [Back end](), [JavaScript](), [Machine learning]() Tags: [backend](), [cloud-functions](), [data-processing](), [firebase](), [firestore](), [github](), [guide](), [javascript](), [launch](), [machine-learning](), [python](), [tensorflow](), [web]() ### AI overview Cloud Functions for Firebase now offers Python support in general availability, enabling developers to write backend functions in Python for production apps. The article describes integrations with machine learning libraries and services, HTTP and callable triggers, Firebase background events, structured logging, and getting-started resources. ### Source excerpt Python server code for your production Firebase apps. ## Simulated Spotify Listening Experiences for Reinforcement Learning with TensorFlow and TF-Agents DevFeed: [Simulated Spotify Listening Experiences for Reinforcement Learning with TensorFlow and TF-Agents]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-10-19T19:00:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [TensorFlow Agents](), [recommendation systems](), [AI, ML & Data Engineering](), [Training AI Models](), [TFX]() Tags: [ai](), [documentation](), [keras](), [learn](), [machine-learning](), [offline](), [reinforcement-learning](), [tensorflow](), [tensorflow-agents](), [tfx]() ### AI overview Spotify describes using TensorFlow and TF-Agents to build an offline simulator for reinforcement-learning-based music recommendations. The simulator supported training and evaluating recommendation models and RL agents, with offline estimates reported as strongly correlated with live experimental results. ### Source excerpt Posted by Surya Kanoria, Joseph Cauteruccio, Federico Tomasi, Kamil Ciosek, Matteo Rinaldi, and Zhenwen Dai - Spotify Introduction 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 how RL could be used to craft listening experiences for users. Before we could start training Agents, we needed to pick a RL library that allowed us to easily prototype, test, and potentially deploy our solutions. At Spotify we leverage TensorFlow and the extended TensorFlow Ecosystem (TFX, TensorFlow Serving, and so on) as part of our production Machine Learning Stack. We made the decision early on to leverage TensorFlow Agents as our RL Library of choice, knowing that integrating our experiments with our production systems would be vastly more efficient down the line. One missing bit of technology we required was an offline Spotify environment we could use to prototype, analyze, explore, and train Agents offline prior to online testing. The flexibility of the TF-Agents library, coupled with the broader advantages of TensorFlow and its ecosystem, allowed us to cleanly design a robust and extendable offline Spotify simulator. We based our simulator design on TF-Agents Environment primitives and using this simulator we developed, trained and evaluated sequential models for item recommendations, vanilla RL Agents (PPG, DQN) and a modified deep Q-Network, which we call the Action-Head DQN (AH-DQN), that addressed the specific challenges imposed by the large state and action space of our RL formulation. Through li ## Building a board game with the TFLite plugin for Flutter DevFeed: [Building a board game with the TFLite plugin for Flutter]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-10-18T17:00:00Z Content type: tutorial Language: en Sources: [The TensorFlow Blog]() Topics: [Flutter](), [TensorFlow Lite](), [Inference](), [Reinforcement learning](), [Tensorflow](), [TensorFlow Agents](), [cross-platform](), [Mobile](), [board-game](), [Android](), [iOS]() Tags: [android](), [building](), [cross-platform](), [flutter](), [games](), [how-to](), [inference](), [ios](), [learn](), [mobile](), [plugin](), [reinforcement-learning](), [tensorflow](), [tensorflow-agents](), [tensorflow-lite](), [tutorial](), [tutorials]() ### AI overview This tutorial shows how to port a TensorFlow Lite reinforcement-learning board game to Flutter. It loads a converted model, runs inference to choose the next move, and renders the game for Android and iOS using the Flutter frontend and TensorFlow Lite plugin. ### Source excerpt 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 simple board game 'Plane Strike'. While these end-to-end tutorials are helpful for Android developers, we have heard from the Flutter developer community that it would be interesting to make the app cross-platform. Inspired by the officially released TensorFlow Lite Plugin for Flutter recently, we are going to write one last tutorial and port the app to Flutter. Since we already have the model trained with TensorFlow and converted to TFLite, we can just load the model with TFLite interpreter: void _loadModel() async { // Create the interpreter _interpreter = await Interpreter.fromAsset(_modelFile); } Then we pass in the user board state and help the game agent identify the most promising position to strike next (please refer to our previous blog posts if you need a refresher on the game rules) by running TFLite inference: int predict(List> boardState) { var input = [boardState]; var output = List.filled(_boardSize * _boardSize, 0) .reshape([1, _boardSize * _boardSize]); // Run inference _interpreter.run(input, output); // Argmax double max = output[0][0 ]; int maxIdx = 0; for (int i = 1; i < _boardSize * _boardSize; i++) { if (max < output[0][i]) { maxIdx = i; max = output[0][i]; } } return maxIdx; } That's it! With some additional Flutter frontend code to render the game boards and track game progress, we can immediately run the game on both Android and iOS (currently the plugin only supports these two mobile platforms). You can find the complete code on GitHub. If you want to dig digger, there ar ## 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)], ## The TensorFlow Lite Plugin for Flutter is Officially Available DevFeed: [The TensorFlow Lite Plugin for Flutter is Officially Available]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-08-18T16:00:00Z Content type: news Language: en Sources: [The TensorFlow Blog]() Topics: [Flutter](), [TensorFlow Lite](), [Tensorflow](), [cross-platform](), [Mobile](), [object-detection](), [GitHub](), [Google](), [Kaggle]() Tags: [announcement](), [cross-platform](), [explore](), [flutter](), [github](), [google](), [inference](), [kaggle](), [mobile](), [object-detection](), [plugin](), [tensorflow](), [tensorflow-lite](), [tensorflowlite](), [tutorial]() ### AI overview The TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released. The updated plugin adds features and example apps, including live-camera object detection, and enables local TensorFlow model inference in Flutter apps across supported platforms. ### Source excerpt Posted by Paul Ruiz, Developer Relations Engineer 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 to Amish for his contributions to the TensorFlow Lite Flutter plugin. Through the efforts of developers in the community, the plugin has been updated to the latest version of TensorFlow Lite, and a collection of new features and example apps have been added, such as object detection through a live camera feed. So what is TensorFlow Lite? TensorFlow Lite is a way to run TensorFlow models on devices locally, supporting mobile, embedded, web, and edge devices. TensorFlow Lite's cross-platform support and on-device performance optimizations make it a great addition to the Flutter development toolbox. Our goal with this plugin is to make it easy to integrate TensorFlow Lite models into Flutter apps across mobile platforms, with desktop support currently in development through the efforts of our developer community. Find pre-trained TensorFlow Lite models on model repos like Kaggle Models or create your own custom TensorFlow Lite models. Let's take a look at how you could use the Flutter TensorFlow Lite plugin for image classification: TensorFlow Lite Image Classification with Flutter First you will need to install the plugin from pub.dev. Once the plugin is installed, you can load a TensorFlow Lite model into your Flutter app and define the input and output tensor shapes. If you're using the MobileNet model, then the input tensor will be a 224 by 224 RGB image, and the output will be a list of confidence scores for the trained labels. // Load model Future _loadModel() async { final opt [Next page]()