Creating a pipeline

You can create a simple pipeline by using the GUI pipeline editor. The pipeline uses the notebook that you used earlier to train a model and then save it to S3 storage.

Prerequisites

  • You configured a pipeline server as described in Enabling data science pipelines.

  • If you configured the pipeline server after you created your workbench, you stopped and then started your workbench.

Procedure

  1. Open your workbench’s JupyterLab environment. If the launcher is not visible, click + to open it.

    Pipeline buttons

  2. Click Pipeline Editor.

    Pipeline Editor button

    You have created a blank pipeline.

  3. Set the default runtime image for when you run your notebook or Python code.

    1. In the pipeline editor, click Open Panel.

      Open Panel

    2. Select the Pipeline Properties tab.

      Pipeline Properties Tab

    3. In the Pipeline Properties panel, scroll down to Generic Node Defaults and Runtime Image. Set the value to Tensorflow with Cuda and Python 3.11 (UBI 9).

      Pipeline Runtime Image0

  4. Select File → Save Pipeline.

Verification

  • In the file-browser window, you can see the pipeline file that you created.

Next step

Adding nodes to your pipeline