This tutorial covers the basic flow to help you get started with MLflow in PrimeHub.
First, you need to install it in the
Apps tab. Please check the Overview section to learn how to install an App. In the installing process, you can change the
backend store and
artifact store environment variables. If you don't know the meaning of the environment variables, can just use the default values or check the MLflow Official Doc and Our Setting for more details.
PrimeHub shows the app's state in the
Apps tab. You can open the MLflow UI by clicking
Open after the state becomes
It will open a new window and show the MLflow UI. You can see your experiments and runs in this UI. We will show you how to record a run in an experiment by using the PrimeHub Notebook function in the next section.
Use MLflow Tracking in PrimeHub
What we need?
An instance type >= minimal requirement (CPU=1, GPU=0, Mem=2G)
The prepared notebook file of the example
Choose a group with enabled Shared Volume (a.k.a Group Volume)
Please have the image, the instance type on PrimeHub, or request administrators for assistance before we start.
Enter Notebook from User Portal, select the image, the instance type, and start a notebook.
From File Browser of Notebook, navigate into the directory of
<group_name>which is a Group Volume; here mlflow is our working group.
While inside the group volume, copy/drag the downloaded
app_tutorial_mlflow_demo_notebook.ipynbthere in File Browser and open it.
Open the notebook, and change the line
mlflow.set_tracking_uri("http://app-mlflow-32adp:5000/")into the proper link based on the detail page in the
Copy the Service Endpoint value and replace
app-mlflow-32adp:5000in the notebook to this value.
Run All Cells in the notebook, you will see a new run in
internal-experimentin the MLflow UI.
That's the basic use of how to track your machine learning experiments by using MLflow and PrimeHub.
Binding MLflow App to Models
With a running MLflow App, we can bind MLflow service to Models Management. Once binding, on Models, we can view registered models, furthermore deploy these models via Deployments at ease on PrimeHub. See Group Setting - MLflow to bind your MLflow App.