How to beginner · 3 min read

How to track hyperparameters with MLflow

Q: How to track hyperparameters with MLflow

Use mlflow.log_params() to record hyperparameters during your training runs in MLflow. This allows you to track, compare, and reproduce experiments efficiently within the MLflow UI or API.

Quick answer

Use mlflow.log_params() to record hyperparameters during your training runs in MLflow. This allows you to track, compare, and reproduce experiments efficiently within the MLflow UI or API.

PREREQUISITES

Python 3.8+
pip install mlflow>=2.0
Basic familiarity with Python machine learning scripts

Setup

Install MLflow and set up your environment to enable experiment tracking.

bash

pip install mlflow

Step by step

This example demonstrates how to track hyperparameters and metrics in MLflow during a simple model training run.

python

import mlflow
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load data
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

# Define hyperparameters
params = {
    "n_estimators": 100,
    "max_depth": 3,
    "random_state": 42
}

# Start MLflow run
with mlflow.start_run():
    # Log hyperparameters
    mlflow.log_params(params)

    # Train model
    model = RandomForestClassifier(**params)
    model.fit(X_train, y_train)

    # Predict and evaluate
    preds = model.predict(X_test)
    acc = accuracy_score(y_test, preds)

    # Log metric
    mlflow.log_metric("accuracy", acc)

    print(f"Logged params: {params}")
    print(f"Logged accuracy: {acc}")

output

Logged params: {'n_estimators': 100, 'max_depth': 3, 'random_state': 42}
Logged accuracy: 1.0

Common variations

You can track hyperparameters asynchronously or with different MLflow tracking servers. Also, use mlflow.log_param() for single parameters or mlflow.log_params() for multiple. For advanced use, integrate MLflow with frameworks like PyTorch or TensorFlow.

python

import mlflow

# Log a single hyperparameter
mlflow.log_param("learning_rate", 0.01)

# Log multiple hyperparameters
mlflow.log_params({"batch_size": 32, "epochs": 10})

Troubleshooting

If you don't see parameters in the MLflow UI, ensure your mlflow.start_run() context is active when logging. Also, verify your tracking URI is correctly set with mlflow.set_tracking_uri() if using a remote server.

python

import mlflow

# Set tracking URI if using remote server
mlflow.set_tracking_uri("http://your-mlflow-server:5000")

✅

Key Takeaways

Use mlflow.log_params() to track multiple hyperparameters in one call.
Always wrap logging calls inside mlflow.start_run() to ensure data is recorded.
Set the MLflow tracking URI properly when using remote or cloud tracking servers.

Verified 2026-04

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