Ease of use, expressivity, and debuggability are among the core principles of PyTorch. One of the key drivers for the ease of use is that PyTorch execution is by default "eager, i.e. op by op execution preserves the imperative nature of the program. However, eager execution does not offer the compiler based optimization, for example, the optimizations when the computation can be expressed as a graph. LazyTensor [1], first introduced with PyTorch/XLA, helps combine these seemingly disparate approaches. While PyTorch eager execution is widely used, intuitive, and well understood, lazy execution is not as prevalent yet.

Considering how much important Artificial intelligence is, especially when it comes to transforming raw data, organizations are relying heavily on it. Artificial intelligence is one of those excellent ways to work smarter and not harder. On that note, have a look at top Google AI tools that everybody should learn in 2022. ML Kit is one of the best tools that mobile app creators can ask for. Storage, coding skills, etc. are something that need not be bothered about.

A Tensor Processing Unit (TPU) is an Accelerator Application-Specific integrated Circuit (ASIC) developed by Google for Artificial Intelligence and Neural Network Machine Learning. With Machine Learning gaining its relevance and importance every day, the conventional microprocessors have known to be unable to effectively handle the computations be it training or neural network processing. The 1st Generation TPU is a hardware chip used at Google data center for faster computation. The 2nd generation TPU is now available in cloud and empowers businesses everywhere to access this accelerator technology to speed up their machine learning workloads using its high speed network. The 3rd generation TPU is twice as powerful as its previous generation and this result in an 8-fold increase in performance.

This repository contains code for evaluating visual models on a challenging set of downstream vision tasks, coming from diverse domains: natural images, artificial environments (structured) and images captured with non-standard cameras (specialized). These tasks, together with our evaluation protocol, constitute VTAB, short for Visual Task Adaptation Benchmark. Our benchmark expects a pretrained model as an input. The model should be provided as a Hub module. The given model is independently fine tuned for solving each of the above 20 tasks.

In this experiment, we will be pre-training a state-of-the-art Natural Language Understanding model BERT on arbitrary text data using Google Cloud infrastructure. With this guide, you will be able to train a BERT model on arbitrary text data. This is useful if a pre-trained model for your language or use case is not available in open source. This guide is intended for NLP researchers who are excited with the BERT technology but are not satisfied with the performance of the available open-sourced models. For persistent storage of training data and model, you will require a Google Cloud Storage bucket.

The rise of industrial Internet of Things (IoT) and artificial intelligence (AI) are making edge computing significant for enterprises. Many industry verticals such as manufacturing, healthcare, automobile, transportation, and aviation are considering an investment in edge computing. Edge computing is fast becoming the conduit between the devices that generate data and the public cloud that processes the data. In the context of machine learning and artificial intelligence, the public cloud is used for training the models and the edge is utilized for inferencing. To accelerate ML training in the cloud, public cloud vendors such as AWS, Azure, and the Google Cloud Platform (GCP) offer GPU-backed virtual machines.

Google has officially released its Edge TPU (TPU stands for tensor processing unit) processors in its new Coral development board and USB accelerator. The Edge TPU is Google's inference-focused application specific integrated circuit (ASIC) that targets low-power "edge" devices and complements the company's "Cloud TPU," which targets data centers. Credit: GoogleLast July, Google announced that it's working on a low-power version of its Cloud TPU to cater to Internet of Things (IoT) devices. The Edge TPU's main promise is to free IoT devices from cloud dependence when it comes to intelligent analysis of data. For instance, a surveillance camera would no longer need to identify objects it sees in real-time through cloud analysis and could instead do so on its own, locally, thanks to the Edge TPU.

Did you know that Colab includes the ability to select a free Cloud TPU for training models? That's right, a whole TPU for you to use all by yourself in a notebook! As of TensorFlow 1.11, you can train Keras models with TPUs. In this post, let's take a look at what changes you need to make to your code to be able to train a Keras model on TPUs. Note that some of this may be simplified even further with the release of TensorFlow 2.0 later this year, but I thought it'd be helpful to share these tips in case you'd like to try this out now.

Roundup Here's your roundup of machine-learning news from this week, beyond what we've already covered. Axon AI Ethics board A group of civil rights groups and technology researchers has written a letter to Axon, a company that uses AI to analyze video footage aimed at law enforcement. Axon recently announced it had set up an AI ethics board to guide its products and services. In response, the letter urges the company to not develop real-time facial recognition for police body cameras to prevent misidentifying civilians as criminals, to ethically reviewing all its other products, and to reach out to "survivors of law enforcement harm and violence" for advice. You can read the letter here.

Google recently added the Tensor Processing Unit v2 (TPUv2), a custom-developed microchip to accelerate deep learning, to its cloud offering. The TPUv2 is the second generation of this chip and the first publicly available deep learning accelerator that has the potential of becoming an alternative to Nvidia GPUs. We recently reported our first experience and received a lot of requests for a more detailed comparison to Nvidia V100 GPUs. Providing a balanced and meaningful comparison for deep learning accelerators is not a trivial task. Due to the future importance of this product category and the lack of detailed comparisons we felt the need to create one on our own.