Getting started with the Watson Pipelines editor
Getting started with the Watson Pipelines editor The Watson Pipelines editor is a graphical canvas where you can drag and drop nodes that you connect together into a pipeline for automating machine model operations. You can open the Pipelines editor by creating a new Pipelines asset or editing an existing Pipelines asset. To create a new asset in your project from the Assets tab, click New asset > Automate model lifecycle. To edit an existing asset, click the pipeline asset name on the Assets tab. The canvas opens with a set of annotated tools for you to use to create a pipeline. The canvas includes the following components: ![Pipeline canvas components](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/Pipeline-canvas.svg) * The node palette provides nodes that represent various actions for manipulating assets and altering the flow of control in a pipeline. For example, you can add nodes to create assets such as data files, AutoAI experiments, or deployment spaces. You can configure node actions based on conditions if files import successfully, such as feeding data into a notebook. You can also use nodes to run and update assets. As you build your pipeline, you connect the nodes, then configure operations on the nodes to create the pipeline. These pipelines create a dynamic flow that addresses specific stages of the machine learning lifecycle. * The toolbar includes shortcuts to options related to running, editing, and viewing the pipeline. * The parameters pane provides context-sensitive options for configuring the elements of your pipeline. The toolbar ![Pipeline toolbar](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/images/Pipeline-toolbar.png) Use the Pipeline editor toolbar to: * Run the pipeline as a trial run or a scheduled job * View the history of pipeline runs * Cut, copy, or paste canvas objects * Delete a selected node * Drop a comment onto the canvas * Configure global objects, such as pipeline parameters or user variables * Manage default settings * Arrange nodes vertically * View last saved timestamp * Zoom in or out * Fit the pipeline to the view * Show or hide global messages Hover over an icon on the toolbar to view the shortcut text. The node palette The node palette provides the objects that you need to create an end-to-end pipeline. Click a top-level node in the palette to see the related nodes. Node category Description Node type Copy Use nodes to copy an asset or file, import assets, or export assets Copy assets <br>Export assets <br>Import assets Create Create assets or containers for assets Create AutoAI experiment <br>Create AutoAI time series experiment <br>Create batch deployment <br>Create data asset <br>Create deployment space <br>Create online deployment Wait Specify node-level conditions for advancing the pipeline run Wait for all results <br>Wait for any result <br>Wait for file Control Specify error handling Loop in parallel <br>Loop in sequence <br>Set user variables <br>Terminate pipeline Update Update the configuration settings for a space, asset, or job. Update AutoAI experiment <br>Update batch deployment <br>Update deployment space <br>Update online deployment Delete Remove a specified asset, job, or space. Delete AutoAI experiment <br>Delete batch deployment <br>Delete deployment space <br>Delete online deployment Run Run an existing or ad hoc job. Run AutoAI experiment <br>Run Bash script <br>Run batch deployment <br>Run Data Refinery job <br>Run notebook job <br>Run pipeline job <br>Run Pipelines component job <br>Run SPSS Modeler job The parameters pane Double-click a node to edit its configuration options. Depending on the type, a node can define various input and output options or even allow the user to add inputs or outputs dynamically. You can define the source of values in various ways. For example, you can specify that the source of value for "ML asset" input for a batch deployment must be the output from a run notebook node. For more information on parameters, see [Configuring pipeline components](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-orchestration-config.html). Next steps * [Planning a pipeline](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-orchestration-planning.html) * [Explore the sample pipeline](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-orchestration-sample.html) * [Create a pipeline](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-orchestration-create.html) Parent topic:[Watson Pipelines](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-orchestration-overview.html)