Cloudera Altus Director helps you deploy, scale, and manage Cloudera clusters on AWS, Microsoft Azure, or Google Cloud Platform. Altus Director both enables and enforces the best practices of big data deployments and cloud infrastructure. Altus Director’s enterprise-grade features deliver a mechanism for establishing production-ready clusters in the cloud for big data workloads and applications in a simple, reliable, automated fashion. In this post, you will learn about new functionality and changes in release 6.2.
Cloudera has put a significant amount of work into upgrading the third-party libraries used in our just-released C6 version. This major upgrade of our software has given us the opportunity to upgrade many of the libraries we use. These upgrades allow us to avoid security vulnerabilities, use modern versions of libraries, and to standardize versions of libraries across CDH.
Modern software development relies on reusing other people’s code. Code reused in this fashion is called a “third-party library.”
Cloudera Altus Director provides the simplest way to deploy and manage Cloudera Enterprise in the cloud. It enables customers to unlock the benefits of enterprise-grade Hadoop while leveraging the flexibility, scalability, and affordability of the cloud. It integrates seamlessly with Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, and provides support to build custom plugins for other public or private cloud environments.
While automating the provisioning of a cluster on the cloud using Altus Director,
With the abundance of deep learning frameworks available today, it can be difficult to know what to choose for any particular application. Given the contrasting strengths and weaknesses of these frameworks, the ability to work with and switch between more than one is particularly important. Recent Cloudera blogs have shown how examples of applying deep learning on the Cloudera ecosystem using popular frameworks Deeplearning4j, BigDL, and Keras+TensorFlow.
Cloudera recently published a blog post on how to use Deeplearning4J (DL4J) along with Apache Hadoop and Apache Spark to get state-of-the-art results on an image recognition task. Continuing on a similar stream of work, in this post we discuss a viable alternative that is specifically designed to be used with Spark, and data available in Spark and Hadoop clusters via a Scala or Python API.
The Deep Learning landscape is still evolving.