import mlflow
from sklearn.metrics import accuracy_score, precision_score, recall_score
 
# Assume you have a trained model and test data
y_true = [...]  # actual tag
y_pred = [...]  # prediction tag
 
# Start MLflow run
with mlflow.start_run():
    # Computation model performance metric
    accuracy = accuracy_score(y_true, y_pred)
    precision = precision_score(y_true, y_pred)
    recall = recall_score(y_true, y_pred)
 
    # Log model performance metrics
    mlflow.log_metric("accuracy", accuracy)
    mlflow.log_metric("precision", precision)
    mlflow.log_metric("recall", recall)
...


import mlflow
import psutil  # to obtain system performance metrics
 
#Enable method 1: use environment variables
import os
os.environ["MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING"] = "true"
 
#Enable method 2: use mlflow.enable_system_metrics_logging() to log
mlflow.enable_system_metrics_logging()
 
#Enable method 3: target a designated run to start
with mlflow.start_run(log_system_metrics=True):
    ...