Deploying a Decision Optimization model

To deploy a Decision Optimization model, create a model ready for deployment in your deployment space and then upload your model as an archive. When deployed, you can submit jobs to your model and monitor job states.

To deploy a Decision Optimization model:

1. Package your Decision Optimization model with your common data (optional) ready for deployment as a tar.gz, .zip, or .jar file.
   Your archive can include the following optional files:

   1. Your model files

      For Python or OPL models, these files usually contain your model formulation. However, you can also choose to omit these files, especially when your model and data are integrated in the same file. For example, for CPLEX (.lp files), CPO (.cpo files), and mps format models, to avoid resolving the same mode, you can send these files later in the job.

   2. Settings files

      For more information, see Run parameters (Decision Optimization).

   3. Master data

      These files contain any data that can be reused by all jobs. Including such data files can make deployment jobs more efficient. For example, you can include a data file for values that remain constant, such as distances between towns. You can include this data file in the deployment so that you provide it only once and not at each job request.

   Note: For Python models with multiple .py files, put all files in the same folder in your archive. The same folder must contain a main file called main.py. Do not use subfolders.
2. Create a model ready for deployment in Watson Machine Learning providing the following information:
   • Deployment space instance
   • the hardware specification for the available configuration sizes (small S, medium M, large L, extra large XL). See configurations.
   • the software specification (Decision Optimization runtime version):
     • do_22.1 runtime is based on CPLEX 22.1.1.0
     • do_20.1 runtime is based on CPLEX 20.1.0.1 (The do_20.1 runtime is deprecated, and will soon be removed.)

     You can extend the software specification provided by Watson Machine Learning. See the ExtendWMLSoftwareSpec notebook in the jupyter folder of the DO-samples. Select the relevant product and version subfolder. For watsonx on-premises, use samples from the Cloud Pak for Data v5.1.x subfolder.

     Updating CPLEX runtimes:

     If you have already deployed your model with a CPLEX runtime that is no longer supported, you can update your existing deployed model using either the REST API or the UI.

   • the model type:
     • opl (do-opl_<runtime version>)
     • cplex (do-cplex_<runtime version>)
     • cpo (do-cpo_<runtime version>)
     • docplex (do-docplex_<runtime version>) using Python 3.11

     (The Runtime version can be one of the available Decision Optimization runtime versions so, for example, an OPL model with runtime 22.1 would have the model type do-opl_22.1.)

3. Upload your model archive (tar.gz,.zip, or .jar file) on Watson Machine Learning. See Model input and output data file formats (Decision Optimization) for information about input file types.
   You obtain a model-URL. Your Watson Machine Learning model can then be used in one or multiple deployments.
4. Deploy your model by using the MODEL-ID, SPACE-ID, and the hardware specification for the available configuration sizes (small S, medium M, large L, extra large XL). See configurations. You can also specify the number of nodes to be used (the default value is 1).
   You obtain a deployment-id.
5. Monitor the deployment using the deployment-id. Deployment states can be: initializing, updating, ready, or failed.