This add-on to Bright Cluster Manager provides everything you need accelerate your data science projects. Bright brings everything together in a simple, easy to deploy and manage solution that will get you up and running quickly and reliably in less time than you might imagine.
Bright Cluster Manager for Data Science enables you to deploy, monitor, and manage big data clusters with ease. Deploy it as a complete solution to support your organization’s data science.
The Bright Advantage:
Bright software makes monitoring and managing your data science clusters easy.
Easy to Deploy
- Answer a few questions and the wizard does the rest
- Can install directly over bare metal – nothing to pre-install
- Takes the pain out of deployment
Easy to Monitor
- Powerful GUI provides comprehensive metrics and alerts you when there’s trouble
- Automatic healthchecks keep an eye on the cluster for you
Easy to Manage
- Powerful graphical and command line UIs simplify management tasks
- Optimizes your use of IT resources
- Includes deep learning libraries and frameworks
How It Works
Bright Cluster Manager for Data Science lets you administer data science clusters as a single entity, provisioning the hardware, operating system, big data and deep learning software from a single interface. This makes it easier to build a reliable, clustered data science infrastructure. Once your system is up and running, the Bright cluster management daemon keeps an eye on virtually every aspect of every node, and reports any problems it detects in the software or the hardware so that you can take action and keep your infrastructure healthy. The intuitive Bright management interface makes it easy to see and take action on any issue impacting data science cluster performance.
- Installation Wizard : Installs everything you need, including your chosen distributions of Linux, Spark, libraries, and more.
- Flexible Configuration : Bright’s configuration groups make Spark configuration more flexible than ever
- Graphical User Interface : A single, intuitive interface for all cluster management functionality.
- Choice of Machine Learning Libraries and Frameworks : Includes popular machine learning libraries and frameworks such as NVIDIA cuDNN, DIGITS, MXNet, pyTorch, Caffe, Caffe2, CNTK, Torch, Tensorflow, Theano, and more.
- Supports Alternate File Systems : Take advantage of Lustre as an alternative to HDFS
- Cloud : Easily create new clusters in the cloud, or add cloud-based resources to your existing clusters on-the-fly.