How Google Uses PyTorch

LiteRT has evolved from its TensorFlow Lite foundation into a universal on-device AI inference framework, now offering production-ready GPU acceleration across six platforms and streamlined NPU int...

The article introduces Coral NPU, a full-stack, open-source platform designed to enhance Edge AI capabilities on low-power devices.

This article provides a comprehensive guide on how to train a GPT-2 model using JAX on TPU, highlighting the ease of leveraging Google TPUs for free.

The article discusses the increasing adoption of JAX in robotics, highlighting its efficiency in optimal control and simulation. It features insights from Max Muchen Sun, a Robotics Ph. D.

The article discusses how to use KerasHub for loading model weights from SafeTensors into Keras, enabling flexible end-to-end machine learning workflows across different frameworks like JAX, PyTorc...

The article introduces Keras Recommenders, a new library designed to simplify the creation of state-of-the-art recommendation systems using Keras with JAX, TensorFlow, or PyTorch.

PaliGemma 2 mix is an advanced vision-language model designed for multiple tasks, allowing developers to utilize a single model for various applications such as image captioning, object detection, ...

PaliGemma 2 is the latest vision-language model from Google, designed to simplify the process of building advanced AI that can interpret visual inputs.

The article announces that Francois Chollet, the creator of Keras, is leaving Google to pursue new opportunities.

The article introduces Keras Hub, a unified library for pretrained models that simplifies access to both natural language processing (NLP) and computer vision (CV) architectures.

LiteRT, formerly known as TensorFlow Lite, is a high-performance runtime for on-device AI that now supports models from multiple frameworks including PyTorch, JAX, and Keras.

This article provides a comprehensive guide on using Gemma with Ray on Vertex AI, detailing the steps to set up, fine-tune, and deploy machine learning models.

The article discusses the release of the Gemma 2 model with 27 billion parameters, highlighting its capabilities in Keras and integration with JAX for efficient model training.

Model Explorer is a powerful graph visualization tool designed to simplify the development and optimization of machine learning models for edge devices.

The article introduces the AI Edge Torch Generative API, designed to enable developers to create high-performance LLMs in PyTorch for deployment on edge devices using the TensorFlow Lite runtime.

Google AI Edge Torch provides a seamless integration from PyTorch to TensorFlow Lite (TFLite), enhancing model coverage and CPU performance for mobile devices.

The article recaps the Google I/O 2024 event, highlighting advancements in AI technologies aimed at making AI accessible for developers.

This article discusses how to publish Keras models on Kaggle and Hugging Face, highlighting the ease of sharing fine-tuned models with the community.

The article introduces the expansion of the Gemma family with two new models, CodeGemma and RecurrentGemma, designed specifically for developers and researchers.

The article introduces Gemma models in Keras, a family of lightweight, state-of-the-art open models that leverage the same technology as the Gemini models.