Mia  Marquardt

Mia Marquardt


Recap of TensorFlow at Google I/O 2021

Thanks to everyone who joined our virtual I/O 2021 livestream! While we couldn’t meet in person, we hope we were able to make the event more accessible than ever. In this article, we’re recapping a few of the updates we shared during the keynote. You can watch the keynote below, and you can find recordings of every talk on the TensorFlow [YouTube channel](https://www.youtube.com/tensorflow). Here’s a summary of a few announcements by product area (and there’s more in the videos, so be sure to check them out, too).

## **TensorFlow for Mobile and Web**

The TensorFlow Lite runtime will be bundled with Google Play services

Let’s start with the announcement that the TensorFlow Lite runtime is going to be bundled with Google Play services, meaning you don’t need to distribute it with your app. This can greatly reduce your app’s bundle size. Now you can distribute your model without needing to worry about the runtime. You can [sign up](https://g.co/androidml-eap) for an early access program today, and we expect a full rollout later this year.

You can now run TensorFlow Lite models on the web

All your TensorFlow Lite models can now directly be run on the web in the browser with the new [TFLite Web APIs](https://github.com/tensorflow/tfjs-models/tree/master/tasks) that are unified with [TensorFlow.js](https://www.tensorflow.org/js). This task-based API supports running all [TFLite Task Library](https://www.tensorflow.org/lite/inference_with_metadata/task_library/overview) models for image classification, objection detection, image segmentation, and many NLP problems. It also supports running arbitrary, [custom TFLite models](https://github.com/tensorflow/tfjs/tree/master/tfjs-tflite) with easy, intuitive TensorFlow.js compatible APIs. With this option, you can unify your mobile and web ML development with a single stack.

A new On-Device Machine Learning site

We understand that the most effective developer path to reach Android, the Web and iOS isn’t always the most obvious. That’s why we created a new [On-Device Machine Learning site](https://g.co/on-device-ml) to help you navigate your options, from turnkey to custom models, from cross platform mobile, to in-browser. It includes pathways to take you from an idea to a deployed app, with all the steps in between.

Performance profiling

When it comes to performance, we’re also working on additional tooling for Android developers. [TensorFlow Lite ](https://www.tensorflow.org/lite/performance/measurement)includes built-in support for Systrace, integrating seamlessly with perfetto for Android 10.

And perf improvements aren’t limited to Android – for iOS developers TensorFlow Lite comes with built-in support for signpost-based profiling. When you build your app with the trace option enabled, you can run the Xcode profiler to see the signpost events, letting you dive deeper, and seeing all the way down to individual ops during execution.


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Buddha Community

Recap of TensorFlow at Google I/O 2021

Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

Mckenzie  Osiki

Mckenzie Osiki


Inside MoveNet, Google’s Latest Pose Detection Model

Ahead of Google I/O, Google Research launched a new pose detection model in TensorFlow.js called MoveNet. This ultra-fast and accurate model can detect 17 key points in the human body. MoveNet is currently available on TF Hub with two variants — Lightning and Thunder.

While Lightning is intended for latency-critical applications, Thunder is for applications that call for higher accuracy. Both models claim to run faster than real-time (30+ frames per second (FPS)) on most personal computers, laptops and phones.

The model can be launched in the browser using TensorFlow.js architecture with no server calls needed after the initial page load or external packages. The live demo version is available here.

Currently, the MoveNet model works for the individual in the camera field-of-view. But, soon, Google Research looks to extend the MoveNet model to the multi-person domain so that developers can support applications with multiple people.

#developers corner #body movements online #body movements virtual #fitness machine learning #google i/o #google latest #google new development #google research latest #machine learning models body poses #ose detection model #remote healthcare solutions #tensorflow latest model #track body movements #wellness machine learning

What Are Google Compute Engine ? - Explained

What Are Google Compute Engine ? - Explained

The Google computer engine exchanges a large number of scalable virtual machines to serve as clusters used for that purpose. GCE can be managed through a RESTful API, command line interface, or web console. The computing engine is serviced for a minimum of 10-minutes per use. There is no up or front fee or time commitment. GCE competes with Amazon’s Elastic Compute Cloud (EC2) and Microsoft Azure.


#google compute engine #google compute engine tutorial #google app engine #google cloud console #google cloud storage #google compute engine documentation

Embedding your <image> in google colab <markdown>

This article is a quick guide to help you embed images in google colab markdown without mounting your google drive!

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Just a quick intro to google colab

Google colab is a cloud service that offers FREE python notebook environments to developers and learners, along with FREE GPU and TPU. Users can write and execute Python code in the browser itself without any pre-configuration. It offers two types of cells: text and code. The ‘code’ cells act like code editor, coding and execution in done this block. The ‘text’ cells are used to embed textual description/explanation along with code, it is formatted using a simple markup language called ‘markdown’.

Embedding Images in markdown

If you are a regular colab user, like me, using markdown to add additional details to your code will be your habit too! While working on colab, I tried to embed images along with text in markdown, but it took me almost an hour to figure out the way to do it. So here is an easy guide that will help you.


The first step is to get the image into your google drive. So upload all the images you want to embed in markdown in your google drive.

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Step 2:

Google Drive gives you the option to share the image via a sharable link. Right-click your image and you will find an option to get a sharable link.

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On selecting ‘Get shareable link’, Google will create and display sharable link for the particular image.

#google-cloud-platform #google-collaboratory #google-colaboratory #google-cloud #google-colab #cloud

Alex Riley

Alex Riley


Best Web App Ideas To Make Money In 2021 - Application Startup Guide

Some Popular Web App Ideas for 2021

Are you looking for best web application business ideas that make money in 2021?

There are lots of simple web app ideas but all those web application business ideas do not make money.

Read More

#trending web app ideas 2021 #trending web application ideas 2021 #web application ideas 2021 #web app ideas 2021 #new web app ideas 2021 #evergreen web app ideas 2021