Table of contents

Deploying a script

Once a script is copied to a deployment space, you can deploy it for use.

Notes for deploying a script

Supported types for scripts are Python scripts and R scripts.

  • Batch deployment is the only supported deployment type.
  • Your software specification is included when the script is promoted from a project. For details on software specifications, see Specifying a model type and software specification.
  • Deploying a script to run on a Hadoop environment is not currently supported.

For details on supported input and output types and setting environment variables, see Batch deployment details.

Deploying a script from a space

A batch deployment processes input data from a file and writes the output to a file.

Before you begin

  1. Save or promote to deployment space.
  2. Add the input file for the batch deployment to the space.

For details on making assets available in a space, see Deployment spaces.

Creating the batch deployment

  1. From the deployment space, click the name of the script you want to deploy.
  2. From the Deployments tab, click Create deployment.
  3. Choose Batch as the deployment type and enter a name for your deployment.
  4. Choose a configuration based on the CPU and RAM that should be allocated for this deployment.
  5. Click Create to create the deployment.
  6. When the status changes to Deployed, click the deployment name, then click Create job to configure how to run the deployment.
  7. Select the input data source you promoted or uploaded to the space and provide a name for the output file that will contain the results.
  8. Choose the environment for running the script. Attention: The default environment is Default spark python3.7. You must manually override this with the correct environment for your script.
  9. (Optional) Schedule when the batch job should run. Scheduled jobs display on the Jobs tab of the deployment space. You can edit the schedule and other options from the Jobs tab.
  10. Click Create to create the run. Results of the run are written to the specified output file and saved as a project asset.