TensorFlow Lite, the lightweight version of Google’s popular open source machine learning platform, now supports Ubuntu. This means that developers can now use TensorFlow Lite to run machine learning models on devices running Ubuntu, making it easier to deploy AI applications on a wide range of devices.
For more information check out this video:
TensorFlow Lite: what is it and why you should care
TensorFlow Lite is a new framework from Google designed for running machine learning models on mobile and embedded devices. This enables developers to deploy models developed using TensorFlow on devices with limited computational power and memory.
TensorFlow Lite supports a variety of platforms, including Android, iOS, and now Ubuntu. This makes it a great option for developing applications that need to run on multiple platform types.
One of the key benefits of TensorFlow Lite is that it is much smaller in size than the full TensorFlow framework. This makes it ideal for devices with limited storage and memory. Additionally, TensorFlow Lite can run faster than the full TensorFlow framework, making it ideal for real-time applications such as video and image recognition.
What’s new in TensorFlow Lite
TensorFlow Lite, the light-weight version of Google’s TensorFlow open source machine learning platform, now supports Ubuntu.
This means that developers can now use TensorFlow Lite to run machine learning models on devices running Ubuntu, making it easier to deploy apps that use image classification, text recognition, and other AI capabilities.
TensorFlow Lite is already available for Windows and Android, and support for macOS and iOS is in the works.
TensorFlow Lite now supports Ubuntu
We’re excited to announce that TensorFlow Lite now supports Ubuntu devices. You can now use TensorFlow Lite on Ubuntu to run machine learning models on low-power devices like the Raspberry Pi.
TensorFlow Lite is a powerful tool for running machine learning models on mobile devices. It’s efficient, meaning that it can run on devices with limited resources. And it’s flexible, allowing you to deploy models built with different frameworks (including TensorFlow, Keras, and XGBoost).
With the release of TensorFlow Lite 1.5, we’ve added support for running on Ubuntu devices. This means that you can now use TensorFlow Lite to run your models on a wide range of devices, including the Raspberry Pi.
To get started, check out our documentation. And if you have any questions, head over to our forum.
How to get started with TensorFlow Lite
TensorFlow Lite is an open-source deep learning platform that allows you to deploy models on a wide variety of devices, including Ubuntu. In this article, we’ll show you how to get started with TensorFlow Lite on Ubuntu so you can start building your own deep learning applications.
Installing TensorFlow Lite on Ubuntu is easy; you can use the provided packages or install from source. We’ll show you both methods.
If you just want to try out TensorFlow Lite, we’ve provided some pre-compiled binaries that you can use. These binaries are not optimized for performance and are not supported by the TensorFlow team. They are, however, built with all of the needed dependencies so you can get started quickly.
To install the pre-compiled binaries, simply download the appropriate package for your system and architecture and extract it somewhere convenient:
curl -OL https://github.com/lHowell/tensorflow-lite-ubuntu/releases/download/v1.0/tensorflow-lite-cpu-1.0-ubuntu1804-amd64.tar.gz
tar xzf tensorflow-lite-cpu-1.0-ubuntu1804-amd64.tar.gz
What’s next for TensorFlow Lite
TensorFlow Lite, the lightweight version of TensorFlow for mobile and embedded devices, now supports Ubuntu. TensorFlow Lite can be used for on-device inference with low latency and a small binary size.
With this release, you can now run TensorFlow Lite on any device with an Arm processor. This support includes popular development boards such as the Raspberry Pi 3 Model B+. In addition to Arm, TensorFlow Lite also supports x86 processors.
To get started with TensorFlow Lite on Ubuntu, check out the tutorial. For more information on TensorFlow Lite, see the documentation.
How TensorFlow Lite can help you
TensorFlow Lite is now available for Ubuntu, making it easier than ever to get started with deep learning on your device. This new support allows developers to experiment with and deploy TensorFlow Lite models on Ubuntu-based devices such as the Raspberry Pi and NVIDIA Jetson Nano.
TensorFlow Lite: getting the most out of it
TensorFlow Lite is an open source deep learning framework for on-device inference. It’s designed to be run on mobile and embedded devices, and allows you to easily deploy models trained using TensorFlow.
TensorFlow Lite now supports Ubuntu! This means that you can now use TensorFlow Lite on devices running Ubuntu, making it easier than ever to deploy your models to a wide range of devices.
To get started with TensorFlow Lite on Ubuntu, check out the official documentation.
TensorFlow Lite: making the most of it
We are excited to announce that TensorFlow Lite now supports Ubuntu! TensorFlow Lite is a powerful tool for on-device machine learning. It allows you to take advantage of the speed and low power consumption of an embedded processor, while still getting the benefits of TensorFlow, such as strong models and easy-to-use APIs.
With this support, you can now use TensorFlow Lite to develop applications for Ubuntu devices, including the popular Raspberry Pi. This support is available in TensorFlow Lite version 1.14. To get started, check out the TensorFlow Lite tutorials.
TensorFlow Lite: tips and tricks
TensorFlow Lite is a set of tools that help developers run TensorFlow models on mobile, embedded, and IoT devices. TensorFlow Lite supports a wide range of devices, including Android and iOS phones and tablets, Linux-based cameras, Raspberry Pi boards, and other devices with low-power processors. The latest version of TensorFlow Lite (1.4) adds support for running inference on Ubuntu-based devices.
To get started with TensorFlow Lite on Ubuntu, you’ll need to install the TensorFlow Lite library. You can do this using apt:
sudo apt install libtensorflow-lite1
Once the library is installed, you can start using it in your applications. The TensorFlow Lite repository contains a set of example applications that you can use to test your installation. These examples are located in the tensorflow/lite/examples/ directory.
To run an example application, first change into the directory containing the source code for that application. For example, to run the hello_tflite application, you would change into the tensorflow/lite/examples/hello_tflite/ directory. Then, you can build and run the application using the following commands:
make # Builds the hello_tflite executable
TensorFlow Lite: the future
TensorFlow Lite, the lightweight version of Google’s TensorFlow open source machine learning platform, now supports Ubuntu. That means developers can now use TensorFlow Lite to build and deploy AI models on Ubuntu-based devices, such as the Raspberry Pi.
TensorFlow Lite is designed for on-device inference, meaning that it can run machine learning models on edge devices like smartphones and IoT devices. This is important because it allows developers to build AI applications that work offline and don’t need to rely on cloud services.
The support for Ubuntu is still in beta, but it’s a sign that TensorFlow Lite is becoming more widely available. In the future, we expect TensorFlow Lite to become the standard platform for deploying AI models on edge devices.
Keyword: TensorFlow Lite Now Supports Ubuntu