Creating a workbench and selecting a workbench image
A workbench is an instance of your development and experimentation environment. When you create a workbench, you select a workbench image that is optimized with the tools and libraries that you need for developing models.
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You created a
My Storageconnection as described in Storing data with connections. -
If you intend to complete the pipelines section of this workshop, you configured a pipeline server as described in Enabling data science pipelines.
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Navigate to the project detail page for the data science project that you created in Setting up your data science project.
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Click the Workbenches tab, and then click the Create workbench button.
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Fill out the name and description.
Red Hat provides several supported workbench images. In the Workbench image section, you can choose one of the default images or a custom image that an administrator has set up for you. The Tensorflow image has the libraries needed for this workshop.
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Select the latest Tensorflow image.
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Choose a small deployment.
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If your OpenShift cluster has available GPUs, the Create workbench form includes an Accelerator option. Select None. This workshop does not require any GPUs.
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Leave the default environment variables and storage options.
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For Connections, click Attach existing connection.
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Select
My Storage(the object storage that you configured previously) and then click Attach.
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Click Create workbench.
In the Workbenches tab for the project, the status of the workbench changes from Starting to Running.
| If you made a mistake, you can edit the workbench to make changes. |
