How to Run MLflow on Databricks: A Step-by-Step Guide

How to Run MLflow on Databricks: A Step-by-Step Guide

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How to Run MLflow on Databricks

MLflow is an open-source platform designed to manage the machine learning lifecycle, from experimentation to deployment. Running MLflow on Databricks allows you to leverage the full potential of Databricks’ cloud-based capabilities, including distributed computing and seamless integrations with other cloud services. In this guide, we will walk you through how to run MLflow on Databricks step-by-step.

1. Setting Up Databricks Environment

Before starting with MLflow, you need to set up Databricks and create a workspace.

1. Sign Up for Databricks: If you don't already have a Databricks account, sign up for one at Databricks.

2. Create a Databricks Cluster:

   • Once logged in, go to the "Clusters" tab and click "Create Cluster."
   • Choose your cluster configuration (e.g., the number of nodes, instance types) based on your requirements.

3. Create a Notebook:

   • Navigate to the "Workspace" tab.
   • Create a new notebook by selecting “Create” > “Notebook”.
   • Choose Python as the default language for your notebook.

2. Install MLflow on Databricks

Databricks has built-in support for MLflow, but you may want to install or upgrade to the latest version of MLflow.

1. Install MLflow:

   • Run the following commands in a new cell in your notebook to install the MLflow package:

   python
   %pip install mlflow
   

2. Verify Installation:

   • After installation, verify it by running:

   python
   import mlflow
   print(mlflow.__version__)
   

   This will print the installed version of MLflow, confirming the installation.

3. Start Experimenting with MLflow

1. Initialize an Experiment:

   • MLflow uses the concept of “experiments” to track and organize your machine learning runs.
   • In Databricks, MLflow automatically creates an experiment for you, but you can also create your own:

   python
   mlflow.create_experiment

   Replace your_email@databricks.com with your Databricks workspace email.

2. ('/Users/your_email@databricks.com/my_experiment')

3. Start a New Run:

   • Use mlflow.start_run() to start logging your model’s performance:

   python
   with mlflow.start_run():
       # Your model code goes here
       mlflow.log_param("param_name", value)
       mlflow.log_metric("metric_name", value)
   

   • Log parameters, metrics, and artifacts (like models or datasets) within the with block to track every step of the experiment.

4. Train and Log a Machine Learning Model

Let’s train a simple machine learning model (e.g., Logistic Regression) and log the metrics using MLflow.

1. Import Required Libraries:

   • Use libraries such as sklearn for training a model:

   python
   from sklearn.model_selection import train_test_split
   from sklearn.linear_model import LogisticRegression
   from sklearn.metrics import accuracy_score
   import mlflow
   import mlflow.sklearn
   

2. Prepare the Data:

   • Load a dataset and split it into training and test sets:

   python
   from sklearn.datasets import load_iris
   
   iris = load_iris()
   X = iris.data
   y = iris.target
   X_train, X_test, y_train, y_test = train_test_split

3. (X, y, test_size=0.3, random_state=42)

4. Train the Model:

   • Train a Logistic Regression model:

   python
   model = LogisticRegression(max_iter=200)
   model.fit(X_train, y_train)
   y_pred = model.predict(X_test)
   

5. Log the Model and Metrics:

   • Log the model and accuracy score as metrics:

   python
   with mlflow.start_run():
       accuracy = accuracy_score(y_test, y_pred)
       mlflow.log_metric("accuracy", accuracy)
       mlflow.sklearn.log_model(model, "model")
   

5. View Experiment Results in Databricks

1. MLflow UI:

   • After running the experiment, go to the "Experiments" tab in Databricks.
   • You can see the list of all experiments along with their parameters, metrics, and the models that were logged.

2. Compare Runs:

   • Databricks allows you to compare multiple runs side by side, making it easier to track the progress and select the best model.

6. Deploying a Model Using MLflow

Once your model is trained and logged in MLflow, you can deploy it for inference.

1. Model Registry:

   • MLflow’s Model Registry allows you to store and manage different versions of your models.
   • Register your model in the registry with:

   python
   mlflow.register_model

2. ("runs:/<run-id>/model", "my_model")

3. Load the Model for Inference:

   • You can load the model for inference by specifying the model name and version:

   python
   model_uri = "models:/my_model/1"
   model = mlflow.sklearn.load_model(model_uri)
   predictions = model.predict(X_test)
   

4. Deploy to a Production Environment:

   • Databricks provides tools to deploy the model to a production environment with API endpoints for real-time inference.

7. Scaling MLflow Jobs

Databricks allows you to scale machine learning jobs across multiple workers in a cluster. You can easily scale up or down based on your workload by adjusting the cluster configuration.

1. Distributed Training:
   • For large-scale datasets, you can use distributed training to parallelize the workload.
   • You can configure MLflow to use Databricks’ distributed resources automatically when training models.

Conclusion

Running MLflow on Databricks enables a seamless and efficient way to manage the entire machine learning lifecycle. By following this guide, you can quickly start experimenting with MLflow, track your models and experiments, and even deploy models for real-time inference. With Databricks’ powerful cloud capabilities and MLflow’s features, you can streamline your machine learning workflows and achieve faster, more effective results.

Additional Resources

• Databricks Documentation
• MLflow Documentation

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How to Run MLflow on Databricks