Step 1: Install MLflow
bash
pip install --upgrade "mlflow>=3.1"
Step 2: Configure Tracking
MLflow supports different backends for tracking your experiment data. Choose one of the following options to get started. Refer to the Self Hosting Guide for detailed setup and configurations.
Option A: Database (Recommended)
Set the tracking URI to a local database URI (e.g., sqlite:///mlflow.db). This is recommended option for quickstart and local development.
python
import mlflow
mlflow.set_tracking_uri("sqlite:///mlflow.db")
mlflow.set_experiment("my-first-experiment")
Option B: File System
MLflow will automatically use local file storage if no tracking URI is specified:
python
import mlflow
# Creates local mlruns directory for experiments
mlflow.set_experiment("my-first-experiment")
Option C: Remote Tracking Server
Start a remote MLflow tracking server following the Self Hosting Guide. Then configure your client to use the remote server:
python
import mlflow
# Connect to remote MLflow server
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("my-first-experiment")
Alternatively, you can configure the tracking URI and experiment using environment variables:
bash
export MLFLOW_TRACKING_URI="http://localhost:5000"
export MLFLOW_EXPERIMENT_NAME="my-first-experiment"
Step 3: Verify Your Connection
Create a test file and run this code:
python
import mlflow
# Print connection information
print(f"MLflow Tracking URI: {mlflow.get_tracking_uri()}")
print(f"Active Experiment: {mlflow.get_experiment_by_name('my-first-experiment')}")
# Test logging
with mlflow.start_run():
mlflow.log_param("test_param", "test_value")
print("✓ Successfully connected to MLflow!")
Step 4: Access MLflow UI
If you are using local tracking (option A or B), run the following command and access the MLflow UI at http://localhost:5000.
bash
# For Option A
mlflow ui --backend-store-uri sqlite:///mlflow.db --port 5000
# For Option B
mlflow ui --port 5000
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