Bright Computing 8.0 Adds Azure, Expands Machine Learning Support
Bright Computing, long a prominent provider of cluster management tools for HPC, today released version 8.0 of Bright Cluster Manager and Bright OpenStack. The release includes major and minor new features such as a completely rebuilt and now web-based administration interface, the addition of bursting capability to Microsoft Azure, expanded container handling, and updated machine learning tools.
Like many in the HPC technology supplier landscape, Bright Computing has been aggressively striving to expand into the enterprise and supporting OpenStack in the process. Today’s 8.0 release coincides with the OpenStack Summit being held this week in Boston.
“In our latest software release, we incorporated many new features that our users have requested,” said Martijn de Vries, Chief Technology Officer of Bright Computing. De Vries continues, “We’ve made significant improvements that provide greater ease-of-use for systems administrators as well as end-users when creating and managing their cluster and cloud environments. Our goal is to increase productivity to decrease the time to results.”
Key updates in the 8.0 release, according to the company, include:
- A major update to Bright View – with a new a web-based administrator interface and a new workflow that operates well on any web-based device, including a tablet, Bright View allows users to login anywhere, anytime to streamline the functions of deploying, managing and monitoring a cluster environment.
- A new monitoring subsystem – the subsystem was rewritten to a simplified and more sophisticated configuration that provides metric collectors and more flexibility.
- Integration to Microsoft Azure – in addition to AWS support, this new integration with Azure allows users to create virtual clusters in Azure using the Bright cluster on demand feature, and the ability to extend an on-premises cluster into Azure using the Bright cluster extension capability.
- Support for OpenStack Newton – to manage bare metal, virtual machines, and container frameworks, Bright OpenStack 8.0 features improved logging of OpenStack
- events, performance improvements, and Ironic support for deployment of instances on bare metal.
- Integration with Mesos and Marathon – to allow cloud native workloads to be deployed on Bright clusters. Bright provides a streamlined setup process, health checks, service management, and more.
Bright View is the new name for the administrator interface replacing the old CMGUI administrator, which was a standalone desktop application. de Vries said it took three programmers roughly a full year to build.
Adding Azure bursting capability gives users a choice besides AWS. Bright supports two types of cloud bursting, explains de Vries in a brief (~40min) video examining most of the changes in the new release.
One is “cluster on demand” in which the user starts a virtualized “Bright Cluster” inside the cloud. The second – called cluster extension – is when you have an on-premise cluster that you are extending it the public cloud. “In this case, you could actually extend it into both Azure and AWS and you could have multiple zones in each one of those clouds as well. You could create a very large supercomputer just which spans across many different regions around the globe and you can even run an MPI job that spans all of those nodes,” says de Vries.
To the user, all of the nodes (on-premise or in Azure or AWS) look the same because Bright uses the same software image for on-premise and in the cloud. This simplifies a variety of tasks including authentication and workload management. “The workload management system might not even be aware the nodes are in the cloud,” de Vries. He quickly adds it is probably best to operate a ‘cluster’ in one environment at a time rather than both on-premise and in the cloud at the same time so as to avoid latency-related performance slowdowns.
Like virtually all technology suppliers Bright has rapidly added support for machine learning. Release 8.0 is no exception.
“We updated all of our existing frameworks (Caffe, Torch, Theano, and Tensorflow) to the latest versions. In case you don’t know what we do for machine learning, we provide ready to use packages for all of the Linux distributions we support. So if you are looking to get some machine learning workload up on a Bright cluster you don’t have to solve all the dependencies problems that if you were to install them manually,” says de Vries. New frameworks added include CNTK, Keras, MXNet, and caffe-mpi.
“Cafe-mpi is pretty exciting because it allows you to distribute your machine learning algorithm over multiple nodes which I believe is essential in the future as we take machine learning to scale. Right now a lot of people are running machine learning algorithms on just a single box with a whole bunch of GPUs added to it. At some point that is not going to be sufficient anymore as you want to distribute this, for example, over a low latency interconnect as InfiniBand or OmniPath. Café-mpi allows for that. It’s still a bit experimental but this is the early stage,” he says.
Likewise big data, an important component of many workloads and certainly for machine learning, also received attention in 8.0. mainly with upgrades of existing Bright capabilities. Other changes include support for BeeGFS parallel file system, expanded CephFS, improved support for GPU-based systems and workloads, and a job metrics functionality that is on by default rather the needing to be set up. Bright’s ‘what’s new in 8.0’ page.