Ubuntu LTS Release Tuned for AI and ML Workloads

By pratosh |Email | May 4, 2018 | 8076 Views

The recent long-term-support release of Canonicals‚?? Ubuntu  Ubuntu 18.04, code-named  Bionic Beaver, proclaimed last week, adds features and upgrades planned to make the company's Linux distro a more attractive platform for artificial intelligence  AI  development. Much more.
Among those upgrades: Kubeflow, the Google approach to TensorFlow on Kubernetes, and a variety of CI/CD tools were integrated in Canonical's distribution of Kubernetes and affiliated with the Google Kubernetes Engine  GKE  for on-premises and on-cloud AI development.
Kubeflow is the open source project focused on making deployments of machine learning  ML  workflows on Kubernetes  simple, portable, and scalable,  the project page states.
Support for AI and ML was a focus in this release, said Canonical CEO Mark Shuttleworth during a global conference call, both directly and indirectly.
Multicloud operations are the new normal,  Shuttleworth said.  Boot time and performance-optimized images of Ubuntu 18.04 LTS on every major public cloud make it the fastest and most-efficient OS for cloud computing, especially for storage and compute-intensive tasks like machine learning. 
The Canonical Distribution of Kubernetes  CDK  supports GPU quickening of workloads using the NVIDIA device plugin for Kubernetes. Complex workloads like Kubeflow that leverage NVIDIA GPUs  just work  on CDK, the company said,  reflecting joint efforts with Google to accelerate machine learning in the enterprise and providing a portable way to develop and deploy ML applications at scale.  Applications built and tested with Kubeflow and CDK are transportable to Google Cloud.
Developers working on Ubuntu can create applications on their workstations, test them on private bare-metal Kubernetes with CDK, and run them across data sets on Google's GKE.  The resulting models and inference engines can be delivered to Ubuntu devices at the edge of the network, the company said, creating a perfect pipeline for machine learning from workstation, to rack, to cloud and device. 
Having an OS that is tuned for advanced workloads such as AI and ML is critical to a high velocity team  said David Aronchick, Product Manager in Google's Cloud AI group.  With the release of Ubuntu 18.04 LTS and Canonical's collaborations to the Kubeflow project, Canonical has provided both a familiar and highly performant operating system that works everywhere. Whether on-premise or in the cloud, software engineers and data scientists can use tools they are already familiar with, such as Ubuntu, Kubernetes and Kubeflow, and greatly accelerate their ability to deliver value for their customers. 

Source: HOB