Step-by-Step: Install & Run MLflow on Windows

Here’s a step-by-step guide to install and run MLflow on Windows, covering setup, UI launch, and test tracking. This works on Windows 10/11 with Python ≥ 3.8.

✅ Step-by-Step: Install & Run MLflow on Windows

🧰 Step 1: Install Python (if not already installed)

MLflow works best with Python 3.8–3.11.

• Download Python from https://www.python.org/downloads/
• During installation, make sure to check ✅ “Add Python to PATH”

💡 Step 2 (Recommended): Create a Virtual Environment

Open Command Prompt or PowerShell and run:

python -m venv mlflow-env
.mlflow-envScriptsactivate
Code language: CSS (css)

This isolates dependencies for MLflow.

📦 Step 3: Upgrade pip

Still inside the virtual environment:

python -m pip install --upgrade pip

🚀 Step 4: Install MLflow

Run:

pip install mlflow

Verify it installed:

mlflow --version

You should see something like:

mlflow, version 2.21.3
Code language: CSS (css)

🌐 Step 5: Launch the MLflow Tracking UI

Run:

mlflow ui
or
start mlflow ui

This will start a local server at:

http://127.0.0.1:5000
Code language: JavaScript (javascript)

Visit this URL in your browser to access the MLflow UI.

🧪 Step 6: Run a Test MLflow Tracking Script

Save the following as test_mlflow.py:

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split

# 🔧 Set tracking URI to the running server
mlflow.set_tracking_uri("http://127.0.0.1:5000")

# Load dataset
data = load_diabetes()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target)

# Enable autologging
mlflow.sklearn.autolog()

# Start tracking
with mlflow.start_run():
    model = RandomForestRegressor()
    model.fit(X_train, y_train)
    score = model.score(X_test, y_test)
    print("Model score:", score)
Code language: PHP (php)

Run the script:

This opens a new command prompt window for the UI, leaving your current terminal free.

Then in the same terminal:

python mlflow_test.py

Now go back to your MLflow UI (http://127.0.0.1:5000) and you will see the run logged.

✅ What Just Happened:

• ✅ Your ML model trained (RandomForestRegressor)
• ✅ The model’s score was printed: 0.3752
• ✅ The run was logged to MLflow’s tracking server at http://127.0.0.1:5000
• ✅ A run name like learned-hound-363 was auto-generated
• ✅ The script output gave you two links:
  • Run page: View Run
  • Experiment page: View Experiment

📁 Step 7: Where is the data saved?

MLflow creates a folder named:

mlruns/

This contains all experiment logs and metrics.

🧼 Optional Cleanup

To stop the MLflow UI:

• Press Ctrl + C in the terminal

To deactivate the virtual environment:

deactivate

🛠️ Troubleshooting Tips

One Code

import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split

# 🔧 Set tracking URI to the running server
mlflow.set_tracking_uri("http://127.0.0.1:5000")
mlflow.set_experiment("MyImprovedExperiment")

# Load dataset
data = load_diabetes()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target)

# Enable autologging
mlflow.sklearn.autolog()

# Start tracking
with mlflow.start_run():
    model = RandomForestRegressor()
    model.fit(X_train, y_train)
    score = model.score(X_test, y_test)

    print("Model score:", score)

    # Optional: log manually with input example
    input_example = X_train[0:1]
    mlflow.sklearn.log_model(
        sk_model=model,
        artifact_path="model",
        input_example=input_example,
    )

mlflow.log_param("learning_rate", 0.1)
mlflow.log_metric("accuracy", 0.87)
mlflow.sklearn.log_model(model, "my_model")
Code language: PHP (php)

Rajesh Kumar

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