Monitor Notebook Running Instances¶

The AI Lab is integrated with MLflow and supports generating models in Mlflow after running the model training codes in JupyterLab. The generated models can be directly registered and deployed in the AI Hub.

For more information about Mlflow, see MLflow Documentation.

Prerequisites¶

You should have prepared the model training codes that can run in JupyterLab.

Run Model Training Codes¶

You can run and debug the model training codes in JupyterLab by following these steps:

Open the list of notebook instances in AI Lab, and open the notebook instance.
From the left File Browser, browse and open a model training code file, e.g. train.ipynb.
Select the Run the selected cells icon above the model training code file box to execute each line of the cell code in the model training code file to ensure that the codes run successfully.

View Instance Running Results¶

After the model training code runs successfully, you can view the instance running results in MLflow by following these steps:

Expand the Running Instance window on the right side of the page to view the list of instance running results. Once a line of codes runs successfully, its running results will be displayed in the list of running instances.
Select the Go to Lab icon in the upper right corner of the Running Instance window to view all successful and failed running results of the notebook instance in MLflow.
Select a successful running result and view the newly created model in the Artifacts column. The Run ID displayed on the upper part of the page is the name of the model. When registering a model in the AI Hub, you can register a model by this name.
Optional: In the Artifacts section, select the model folder, and then select the New Version button to stage the model as a version of an existing model in AI Hub.
In the New Version pop-up window, select a registered model then select To Modelhub. The New Version window in AI Hub will be open. Follow the wizard to complete staging the model version.