The Clear Linux* Project for Intel® Architecture is a distribution built for cloud and IoT use cases. We want to showcase the best of Intel architecture technology and performance, from low-level kernel features to complex applications that span across the entire OS stack. We're putting emphasis on Power and Performance optimizations throughout the operating system as a whole.
Let us guide you through installing and using a Jupyter* notebook to set up and execute a TensorFlow* machine learning example using the MNIST data for handwriting recognition using the Clear Linux* OS for Intel® Architecture.
Clear Linux OS for Intel Architecture is focused on the Cloud. Our aim was not to make yet another general-purpose Linux distribution; sometimes lean-and-fast is better than big-and-universal.
While we do our best to describe marquee technologies on this website, there are many smaller optimizations and configurations to explore. We invite anyone with an interest in the Cloud to take a look and give it a spin.
Automatic Feedback-Directed Optimizer
Use a sampling-based profile to drive feedback-directed optimizations.
Operate without any custom configuration, for example, a generic host with an empty /etc directory. Stateless systems strictly separate the OS configuration, the per-system configuration, and the VT user-data stored on that system.
The release of GLIBC version 2.27 marks a new step on the Linux technology roadmap, with major new features that will allow Linux developers to create and enhance applications. This blog post describes several key new features and how to use them.
When learning computer programming languages, the first example tutorial given is usually ‘hello world’. Similarly, to learn about machine learning, the first example is often image recognition of handwritten numbers based on the MNIST dataset.