Announcing Data Science Experience Environments in Beta!

Greg Filla
Feb 15, 2018 · 3 min read

I am very excited to announce that today we are releasing Data Science Experience Environments in beta for all users to try.

An environment defines a relationship between a tool, software configuration, and hardware configuration. For example, you can create a Jupyter notebook environment configured with Anaconda 5.0 with 4 vCPU and 16 GB RAM.

With DSX Environments you can quickly scale up or down your compute resources and customize your package dependencies. For this beta release, we have two sizes available for compute, and software configurations available for Python 3.5 and 2.7.

Benefits of Environments:

  • Flexible compute options — quickly change vCPU and RAM
  • Dedicated resources — environments provide dedicated resources for each project collaborator, so no more competing for resources
  • Package managementconda environment definitions are used for customization
  • Reproducible research — environments are project assets and can be shared and reused by all members of your team
  • More to come! — configuration options and supported tools will continue to expand

How to get started

We’ve made it very easy for anyone to get started using environments. Before following these steps you will need a Data Science Experience account: Sign up here!

  1. Once you’ve logged in, create a new project or go to an existing project that uses Cloud Object Storage (Older projects using Swift Object Storage do not support environments).
  2. All projects now have a new Environments tab; this is where you can create, modify, and monitor environment runtimes. DSX includes a Python 3.5 extra-small and small environment for all projects to make it quick and easy to get started.

3. Since these environments are included by default, you can go directly to create a notebook, either by using the Add to project drop down or New Notebook on the Assets tab.

4. On the New notebook screen, select the box to test the environments feature and select your environment:

5. Creating a notebook instantiates your environment’s runtime — from here you should know what to do :-)


A really helpful page to check out when using notebooks is the reached through the info (i) icon. This page contains general information, where you can change the title and description of the notebook, and environment information, as shown here:

From this section you can easily change the environment that your runtime is based on. You can also start, stop, or restart your runtime from this page. To see more details on environments, check out our documentation.


As you can see, this new functionality makes it easier than ever to customize, scale, and manage your work. This is all possible thanks to the incredible designers and engineers building Data Science Experience! 👏 👏 👏

After trying this feature please let us know your feedback in a comment below or a tweet @gdfilla .

IBM Watson Data

Build smarter applications and quickly visualize, share, and gain insights

Greg Filla

Written by

Product Manager, IBM Watson Machine Learning - helping organizations operationalize their data science.

IBM Watson Data

Build smarter applications and quickly visualize, share, and gain insights

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