MLflow Bike Sharing Use Case

Provides an end-to-end workflow in HPE Ezmeral Unified Analytics Software for an MLflow prediction model to determine bike rentals per hour based on weather and time.

Scenario

A data scientist wants to use a Jupyter Notebook to train a model that predicts how many bikes will be rented every hour based on weather and time information.

HPE Ezmeral Unified Analytics Software includes the following components and applications to support this scenario:

Dataset
Bike sharing dataset, bike-sharing.csv, available in the /shared/mlflow directory.
Notebook (Jupyter)
Two preconfigured Jupyter notebooks:
  • bike-sharing-mlflow.ipynb - Runs code, trains models, finds the best model.
  • bike-sharing-prediction.ipynb - Predicts based on the model; deployed via KServe.
MLflow
  • Tracks the experiment and trainings/runs.
  • Logs artifacts, metrics, and parameters for each run.
  • Registers the best model
Object Storage
Stores artifacts that result after running each experiment.
KServe Deployment
Downloads and deploys a model from object storage and makes the model accessible through a web service endpoint.

Steps

Sign in to HPE Ezmeral Unified Analytics Software and perform the following steps:

Run the Bike Sharing Use Case

Track Experiment, Runs, and Register a Model in MLflow

Use the Model for Prediction

Run the Bike Sharing Use Case

  1. In the left navigation pane, click Notebooks.
  2. Connect to your notebook server instance. For this example, select hpedemo-user01-notebook.

  3. Copy the MLFlow folder from the shared directory into the /user directory.
    NOTE
    If the Mlflow folder is not available in the shared directory, perform:
    1. Go to GitHub repository for tutorials.
    2. Clone the repository.
    3. Navigate to ezua-tutorials/Data-Science.
    4. Navigate back to the shared directory.
    5. Copy the MLflow folder from the ezua-tutorials/Data-Science repository into the shared directory.
    6. Copy the /MLflow folder from shared folder to user directory.
  4. Open bike-sharing-mlflow.ipynb and import mlflow and install libraries. After you finish, restart the kernel and run all the cells, including those you previously ran.
    NOTE
    If you are using the local s3-proxy, do not set the following environment variables for MLflow. However, if you are trying to connect from outside the cluster, you must set the following environment variables.
    os.environ["AWS_ACCESS_KEY_ID"] = os.environ['MLFLOW_TRACKING_TOKEN']
os.environ["AWS_SECRET_ACCESS_KEY"] = "s3"
os.environ["AWS_ENDPOINT_URL"] = 'http://local-s3-service.ezdata-system.svc.cluster.local:30000'
os.environ["MLFLOW_S3_ENDPOINT_URL"] = os.environ["AWS_ENDPOINT_URL"]
os.environ["MLFLOW_S3_IGNORE_TLS"] = "true"
os.environ["MLFLOW_TRACKING_INSECURE_TLS"] = "true"
  5. Run the notebook cells.

    Running the notebook returns the details of the best model:



Track Experiment, Runs, and Register a Model in MLflow

  1. Navigate to the MLflow UI. You should see the bike-sharing-exp experiment.

  2. Select the best model and then select Register Model. In this example, the best model is run 2.

  3. In the Register Model window, enter Bike_Sharing_Model and click Register.

  4. Click on the Models menu to view the registered models.

Use the Model for Prediction

  1. Navigate to the notebook server and open bike-sharing-prediction.ipynb.
  2. Run the first cell and wait until the bike-sharing-predictor pod goes into the running state.

  3. Run the second cell to deploy machine learning model using KServe inference service. Note: Update DOMAIN_NAME to your domain for external access and save changes.
    The system prints the following predictions for the input:
    
Rented Bikes Per Hours:
Input Data: {'season': 1, 'year': 2, 'month': 1, 'hour_of_day': 0, 'is_holiday': 0, 'weekday': 6, 'is_workingday': 0, 'weather_situation': 1, 'temperature': 0.24, 'feels_like_temperature': 0.2879, 'humidity': 0.81, 'windspeed': 0.0} 
Bike Per Hour: 108.90178471846806
Input Data: {'season': 1, 'year': 5, 'month': 1, 'hour_of_day': 0, 'is_holiday': 0, 'weekday': 6, 'is_workingday': 1, 'weather_situation': 1, 'temperature': 0.24, 'feels_like_temperature': 0.2879, 'humidity': 0.81, 'windspeed': 0.0} 
Bike Per Hour: 84.96339548602367

End of Tutorial

You have completed this tutorial. This tutorial demonstrated how to train a model using notebooks, track experiments and runs, log artifacts with MLFlow, and use KServe to deploy and predict models.