Creating a workbench
A workbench is an instance of your development and experimentation environment. When you create a workbench, you select a workbench image (sometimes referred to as a notebook 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 Storage Setup.
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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 Notebook image section, you can choose one of the default images or a custom image that an administrator has set up for you. The Standard Data Science image has the libraries needed for this workshop.
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Select the latest Standard Data Science image.
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Choose a small deployment.
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Edit the storage options and increase the storage size to 100GB.
This is optional and only needed if you plan on downloading models to object storage later in the workshop.
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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.
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Click the link for your workbench. If prompted, log in and allow the Notebook to authorize your user.
Your JupyterLab environment window opens.
This file-browser window shows the files and folders that are saved inside your own personal space in OpenShift AI.
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Bring the content of this workshop inside your JupyterLab environment:
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On the toolbar, click the Git Clone icon:
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Enter the following workshop Git https URL:
https://github.com/cfchase/serving-models.git
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Select the Include submodules option, and then click Clone.
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In the file browser, double-click the newly-created serving-models folder.
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