Cloudera is delighted to announce the release of Cloudera Data Science Workbench 1.6.0. Some major features shipping with this release include:
Bring Your Own Editor
Cloudera Data Science Workbench enables team collaboration on an end-to-end data science workflow, from data exploration and data engineering to model development and deployment in production. This can involve collaboration among data engineers, data scientists and ML engineers who often have different editor and IDE preferences. With version 1.6, diverse teams can now tap into the benefits of self-service data science for the enterprise with CDSW, all while working within their most familiar or preferred IDE. This feature supports both, third-party IDEs such as PyCharm that run on your local machine, and browser-based IDEs such as Jupyter and RStudio. For details, see Editors.
Expanded Support for Distributed Machine Learning
Cloudera Data Science Workbench 1.6 allows you to run distributed workloads with frameworks such as TensorFlowOnSpark, H2O, XGBoost, and so on. This is similar to what you can already do with Spark workloads that run on the attached CDH/HDP cluster. For a simple example, see Running Distributed ML Workloads on YARN.
Multiple CDSW Deployments Per-Cloudera Manager Instance
You can now have multiple Cloudera Data Science Workbench CSD deployments associated with a single instance of Cloudera Manager.
For the complete list of new features, changes, and bug fixes shipping with this release, please see the Release Notes.
For more information on downloading, installing, and using Cloudera Data Science Workbench, see the links below:
As always, we welcome your feedback. Please send your comments and suggestions on our community forums.
