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JupyterLab environments (Watson Studio)

JupyterLab can be launched in a Python 3.7 environment, a Python 3.7 with Spark environment, and a Python 3.7 with GPU environment.

Default environment definitions

Watson Studio offers the following default JupyterLab environment definitions with Python.

Name Hardware configuration
Default JupyterLab with Python 3.8 1 vCPU and 2 GB RAM
Default JupyterLab with Python 3.7 1 vCPU and 2 GB RAM
Default Spark 3.0 & Python 3.8 1 vCPU and 4 GB RAM
Default Spark 3.0 & Python 3.7 1 vCPU and 4 GB RAM
Default Spark 2.4 & Python 3.7 1 vCPU and 4 GB RAM

Python with GPU

The Jupyter Notebooks with Python 3.7 GPU service must be installed to use Python 3.7 with GPU. You need to create your own environment definition as Watson Studio does not include a default Python with GPU environment definition that you can select.

To create an environment definition:

  1. Ensure that the Jupyter Notebooks with Python 3.7 with GPU service was installed. See Installing the Jupyter Notebooks with Python 3.7 GPU service.
  2. Create an environment definition from the project’s Environments page and select type GPU and Software version JuypterLab.

Viewing JupyterLab environments

The JupyterLab environments are listed on the project’s Environments page in the environment definitions list. Click the environment to see the environment details. If you created your own environment definition with the software version JupyterLab, you can add a software customization.

After you start JupyterLab, the runtime that becomes active for your session is listed in the active environment runtimes list on the Environments page. You can stop the runtime from this page.

Runtime scope

A JupyterLab environment runtime is always scoped to a project and a user. Each user can only have one active JupyterLab session per project at one time.

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