AutoML
Please review the overview section to understand details about the end-to-end Katib Experiment you will implement in the steps below.
Step 1: Login¶
In this step, you will login to your MLOps Platform.
- Navigate to the URL (This will be provided by your platform team)
- Login using your local credentials or SSO credentials (Identity Provider such as Okta)
Once logged in, you will see the home dashboard screen.
Step 2: Create a Notebook¶
In this step, you will create a Jupyter Notebook. The notebook will be used to create a Katib experiment.
- Navigate to Notebooks
- Click New Notebook
- Enter a name for the notebook
- Select JupyterLab
- Set the minimum CPU to .5
- Set the minimum memory to 1
- Click Launch
It will take 1-2 minutes to create the notebook.
Step 3: Generate Experiment¶
In this step, you will use the notebook to create a Katib experiment which will evaluate function parameters to maximize the function value.
- Navigate to Notebooks
- Click Connect on the previously created notebook
- Download the following notebook file
- In the left hand folder tree, click on the upload files icon
- Upload the previously downloaded katib.ipynb file
- Double click the katib.ipynb file in the folder tree to open the notebook
- Select the first cell and click the run icon
- Once the first cell has finished running, click the Restart Kernel and Run All Cells icon
- After ~2 minutes, the experiment will be complete
Step 4: View AutoML Results¶
In this step, we will view the results of the Katib experiment within the Katib UI to view the trials and results.
- Navigate back to the Kubeflow dashboard
- Click Experiments (AutoML)
- Click on the experiment name
You will see the parameters that delivered the best result.
- Click on Trials to see the parameters and results of each trials
Recap¶
Congratulations! At this point, you have successfully created a Jupyter notebook to create a Katib experiment for hyperparameter optimization.