Table of contents

Set up runtime environments

To avoid slow compute performance within your project or runtime environments, you can allocate your resources either in the Environments page. Note that reserving CPU and memory reduces the availability of CPU and memory for other runtime environments.

Turning Reserve resources off can affect system performance if available CPU and RAM on the servers become overcommitted.

Runtime environments

A runtime environment represents an allocation of compute resource (one or more docker containers) on the Watson Studio Local cluster. You can define multiple environments for specific images such as RStudio and notebooks.

  • To change your CPU and memory allocations, click the environment name.
  • To stop an environment, click the Stop icon (Stop).
  • To see all files, processes, and clusters related to the environment, click the Info icon (Info).
  • If you installed a library or package from a Jupyter notebook or terminal, for example, conda install -y arrow, then you can save it into a new custom image on the cluster by clicking the Save icon (Save). The custom image then appears in the new My Images panel. See Manage packages as a Watson Studio Local user for details.
Tip: If you create a dataframe that loads a large data file in your notebook and receive an error that the kernel died, then you can edit the environment to increase memory.

Runtime environment

Tip: If you allocate the maximum CPU for a runtime environment, the environment might stay in Pending state indefinitely. As a workaround, reduce the CPU allocation.

To view all runtime environments in the Watson Studio Local system, go to the All Active Environments page from the menu icon ( The menu icon).