Template recipe

Overview

The ready-to-use or system templates allow you to transform the data without writing the Python code on the UI. Using these standard templates, you can prepare the data, clean the data, add features, and split the data for testing and training the data and to build models. Running each recipe will transform the data in the flow.

By default, there are hundreds of system templates available. You can use them to transform the data and build simple to complex machine learning flows and subsequently models. You can also create custom templates at the project and tenant levels from Notebook and use them in your flows.

If you want to add a standard template to the flow, see Adding a standard transform or template within a recipe.

Adding a Template recipe

Use this procedure to add a transform within a template.

1. Select the project to upload a dataset. For more information, see Connectors.

1. Click on the dataset block to run various data transformations on this dataset and build an ML model.

2. Use any of these options to add a transform within a template recipe:

   • Click the plus icon on the canvas page and then select Template.

   • Select the dataset block. This opens the side panel. Click the plus icon and select the Template recipe.

   • Select the dataset block to open the side panel in which you click View. This opens the dataset page. Click the plus icon and then select the Template recipe.

The page where you can add data Transformations is displayed.

1. Click Transformations.

Note: If you want to run a transform on a dataset, you must click on the dataset and add the recipe.

The Transformations side panel is displayed.

1. Search for the transforms or templates you want to add to the ML flow or data pipeline on the canvas:

There are set of templates available for each stage of machine learning model. All the templates associated with a particular stage are assigned to a specific tag. Possible tags:

*   Data Cleaning
*   Data Preparation
*   Data Analysis
*   Beta

6. Enter the transform name that you want to add to the data pipeline or filter the transforms by tags from the list.

1. Click on the transform name to open the transform page.

2. Specify the information in the respective fields of the selected transform. For more information, see.

In this example, we have selected suffix to add suffix to all the columns in a dataset.

1. Click Add to add the transform to the data pipeline and close the transform window.

Note:

• You can also add multiple transforms simultaneously, using the +New Transform.

• Use the Edit in Notebook option, available when adding a transform, to edit the transform code in the Notebook editor as needed. Once you've made your changes, click Save Back to Recipe to apply the updates made to the recipe on the Jupyter Notebook editor.

1. Click Test to test the transform and see the output before running this in the data pipeline. You can use the Stop button to halt the recipe execution at any point during its test run.

You can also view logs while testing the recipe by clicking the Log for Test option. This provides access to detailed records and allows you to either download the logs as a text file or open them in a new browser tab for a more detailed view.

A new tab with the generated test output for this transform is displayed.

1. Click the Run button to run this recipe in the flow. If needed, you can stop the recipe run at any time by clicking the Stop button. Once the run is successful, this generates an output dataset or a dashboard.

Keyboard Shortcuts:

• Ctrl+R (or Cmd+R on Mac) → Run a recipe

• Ctrl+S (or Cmd+S on Mac) → Save code changes

1. Click back button to navigate to the canvas from the transforms screen.

1. Click the output dataset block. This opens the pull-out window.

2. Click View to view the dataset with suffix to each column.

On this View data page, you can:

• Click the caret icon to export the output dataset to a csv file, using the Export option.
• Click the caret icon to delete the generated output, using the Delete option.
• Click the plus icon to perform the following:

  • Append a file to the source dataset, using the File option. This option is only enabled for the source dataset.

  • Add a template recipe, using the Template option.

  • Add an AI-Assisted recipe, using the AI-assisted option.

  • Add a Rapid Model recipe, using the Rapid Model option.

  • Add a segment to the source dataset, using the +Segment option. This option is enabled only for the source dataset.

Viewing and editing the recipe details

Use this procedure to view the recipe details and edit the type of transform used within a recipe.

To view the recipe details:

1. Select the recipe block that you have uploaded onto the canvas. This opens the pull-out window.

2. Click inside the recipe name to modify change the recipe name.

3. View the recipe details on the pull-out window:

Recipe type: The type of recipe used. This is the tag assigned to the transform within this recipe.

Created: The date and time at which the recipe was created.

Last modification: The last date and time at which the recipe was modified.

Last build: The last date and time at which the recipe run was performed.

Inputs: The input dataset on which the transformation is applied and the recipe run was performed.

Outputs: The output dataset generated after running the recipe.

Timeout: The duration after which the recipe stops to run. By default, the duration is set to 2 hours. You can change the duration based on the complexity of the recipe you are running in the flow. If the recipe runs longer than this, the recipe run will be terminated after the set period. It is expressed in hrs.

On this pull-out window, you can also:

• View the recipe logs, using the Log icon . This shows detailed record of all successful and failed execution of recipe runs. You can view full logs clicking the Logs option to open the logs page in a new browser tab. On this page, click View Full Log to view all logs.
• View the recipe details, using the View option. This takes you to the respective recipe page.

• Click the ellipses icon and select Delete to delete this recipe from the flow.
• Run the recipe without navigating to the recipe page, using the Run option.

1. Click View to review the details of the recipe. The recipe page is displayed.

2. Click Transformations to view the transforms list in the project and select the transform whose details you want to modify, on the Transforms tab.

1. Click UPDATE.

Exporting the output dataset

Use this procedure to export the output dataset to a csv file.

To export the output dataset:

1. Select the dataset block, be it input or output dataset that you want to export to a csv file. The pull-out window opens.

2. Click the ellipses icon

1. Select the Export option to download the dataset file onto your local system.

Deleting a recipe

Use this procedure to delete a recipe block from the canvas.

To delete a recipe block:

1. Select the recipe block that you want to delete from the canvas. This opens the pull-out window.

2. Click the ellipses icon and select the Delete option to delete the recipe.

You can also delete the recipe from the Transforms list page, using the delete icon available under Actions drop-down. This page appears when you click View on the side panel window of the recipe block.

1. A dialog box prompts that deleting the recipe deletes the recipe block, output datasets, and associated recipes.

2. Click Delete to delete the recipe permanently from the canvas view or click Cancel to discard the action.

Running a specific recipe in the data pipeline

Use this procedure to run a particular recipe in the flow or data pipeline.

To run a recipe block:

1. Select the recipe block that you want to run from the canvas. This opens the side panel.

2. Click Run to run the recipe. The status of the recipe block changes to Running. Once the recipe run is successful, the status changes to Success.

You can also view output (dataset, model, or artifact) generated after running this recipe.

Exporting the output dataset to the connector

Use this procedure to save the output dataset to the configured connector that can be a cloud storage solution or a database.

1. Select the output dataset block that you want to save to the connector, on the canvas. This opens the side panel.

2. Select the Data connector from the drop-down. You can only see the connectors you have configured in this tenant.

3. Enter the destination folder name and file name with which you want to save the file to this folder in this connector.

1. Click Save to save the destination details.

2. Click Export to export the file to the connector.

You can delete the configured connector for this output dataset, using the delete icon .