Configuration and System Management

def set_tracking_uri(uri):
    """
    Set the tracking server URI for MLflow operations.
    
    Parameters:
    - uri: str - Tracking server URI (file://, http://, https://, databricks://, etc.)
    
    Supported URI formats:
    - file:///absolute/path/to/directory (local filesystem)
    - http://hostname:port (MLflow tracking server)
    - https://hostname:port (secure MLflow tracking server)  
    - databricks:// (Databricks workspace)
    - sqlite:///path/to/database.db (SQLite database)
    - mysql://user:password@host:port/database (MySQL)
    - postgresql://user:password@host:port/database (PostgreSQL)
    """

def get_tracking_uri():
    """
    Get current MLflow tracking URI.
    
    Returns:
    str - Current tracking URI or default if not set
    """

def set_registry_uri(uri):
    """
    Set the model registry URI for MLflow model registry operations.
    
    Parameters:
    - uri: str - Model registry URI (same formats as tracking URI)
    """

def get_registry_uri():
    """
    Get current MLflow model registry URI.
    
    Returns:
    str - Current registry URI or tracking URI if not set separately
    """

def get_artifact_uri(run_id=None):
    """
    Get artifact storage URI for run or current run.
    
    Parameters:
    - run_id: str, optional - Run ID (defaults to current active run)
    
    Returns:
    str - Artifact storage URI for the specified run
    """

def set_system_metrics_node_id(node_id):
    """
    Set node identifier for system metrics collection.
    
    Parameters:
    - node_id: str - Unique identifier for the current node
    """

def set_system_metrics_sampling_interval(interval):
    """
    Set sampling interval for system metrics collection.
    
    Parameters:
    - interval: float - Sampling interval in seconds
    """

def set_system_metrics_samples_before_logging(samples):
    """
    Set number of samples to collect before logging system metrics.
    
    Parameters:
    - samples: int - Number of samples to buffer before logging
    """

def set_experiment_id(experiment_id):
    """
    Set active experiment ID for current session.
    
    Parameters:
    - experiment_id: str - Experiment ID to set as active
    """

def get_experiment_id():
    """
    Get current active experiment ID.
    
    Returns:
    str - Active experiment ID or None if not set
    """

def set_run_id(run_id):
    """
    Set active run ID for current session.
    
    Parameters:
    - run_id: str - Run ID to set as active
    """

def get_run_id():
    """
    Get current active run ID.
    
    Returns:
    str - Active run ID or None if not set
    """

def set_default_artifact_root(artifact_root):
    """
    Set default artifact root for experiments.
    
    Parameters:
    - artifact_root: str - Default artifact storage location
    
    Supported formats:
    - s3://bucket/path (Amazon S3)
    - gs://bucket/path (Google Cloud Storage)
    - wasbs://container@account.blob.core.windows.net/path (Azure Blob)
    - hdfs://namenode:port/path (HDFS)
    - file:///absolute/path (local filesystem)
    """

def get_default_artifact_root():
    """
    Get default artifact root configuration.
    
    Returns:
    str - Default artifact root path
    """

def enable_system_metrics_logging():
    """
    Enable automatic system metrics logging for MLflow runs.
    Collects CPU, memory, disk, and network metrics during run execution.
    """

def disable_system_metrics_logging():
    """
    Disable automatic system metrics logging.
    """

def is_system_metrics_logging_enabled():
    """
    Check if system metrics logging is enabled.
    
    Returns:
    bool - True if system metrics logging is enabled
    """

def is_tracking_uri_set():
    """
    Check if tracking URI has been explicitly set.
    
    Returns:
    bool - True if tracking URI is explicitly configured
    """

def get_db_profile_from_uri(db_uri):
    """
    Extract database connection profile from URI.
    
    Parameters:
    - db_uri: str - Database URI
    
    Returns:
    dict - Database connection profile with host, port, database, etc.
    """

def construct_db_uri_from_profile(profile):
    """
    Construct database URI from connection profile.
    
    Parameters:
    - profile: dict - Database connection profile
    
    Returns:
    str - Constructed database URI
    """

def validate_tracking_uri(uri):
    """
    Validate tracking URI format and accessibility.
    
    Parameters:
    - uri: str - Tracking URI to validate
    
    Returns:
    bool - True if URI is valid and accessible
    
    Raises:
    MlflowException - If URI is invalid or inaccessible
    """

def validate_artifact_root(artifact_root):
    """
    Validate artifact storage root configuration.
    
    Parameters:
    - artifact_root: str - Artifact root path to validate
    
    Returns:
    bool - True if artifact root is valid and accessible
    """

def check_server_connection(tracking_uri=None):
    """
    Check connection to MLflow tracking server.
    
    Parameters:
    - tracking_uri: str, optional - Server URI to check (defaults to current)
    
    Returns:
    dict - Server status and version information
    """

class MLflowEnvironment:
    """
    MLflow environment variable constants and utilities.
    """
    
    # Tracking and Registry URIs
    MLFLOW_TRACKING_URI = "MLFLOW_TRACKING_URI"
    MLFLOW_REGISTRY_URI = "MLFLOW_REGISTRY_URI"
    
    # Authentication
    MLFLOW_TRACKING_USERNAME = "MLFLOW_TRACKING_USERNAME"
    MLFLOW_TRACKING_PASSWORD = "MLFLOW_TRACKING_PASSWORD"
    MLFLOW_TRACKING_TOKEN = "MLFLOW_TRACKING_TOKEN"
    MLFLOW_TRACKING_INSECURE_TLS = "MLFLOW_TRACKING_INSECURE_TLS"
    
    # Artifact Storage
    MLFLOW_DEFAULT_ARTIFACT_ROOT = "MLFLOW_DEFAULT_ARTIFACT_ROOT"
    MLFLOW_ARTIFACT_UPLOAD_DOWNLOAD_TIMEOUT = "MLFLOW_ARTIFACT_UPLOAD_DOWNLOAD_TIMEOUT"
    
    # System Configuration  
    MLFLOW_EXPERIMENT_ID = "MLFLOW_EXPERIMENT_ID"
    MLFLOW_RUN_ID = "MLFLOW_RUN_ID"
    MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING = "MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING"
    
    # S3 Configuration
    AWS_ACCESS_KEY_ID = "AWS_ACCESS_KEY_ID"
    AWS_SECRET_ACCESS_KEY = "AWS_SECRET_ACCESS_KEY"
    AWS_SESSION_TOKEN = "AWS_SESSION_TOKEN"
    MLFLOW_S3_ENDPOINT_URL = "MLFLOW_S3_ENDPOINT_URL"
    MLFLOW_S3_IGNORE_TLS = "MLFLOW_S3_IGNORE_TLS"
    
    # Azure Configuration
    AZURE_STORAGE_CONNECTION_STRING = "AZURE_STORAGE_CONNECTION_STRING"
    AZURE_STORAGE_ACCESS_KEY = "AZURE_STORAGE_ACCESS_KEY"
    
    # Google Cloud Configuration
    GOOGLE_APPLICATION_CREDENTIALS = "GOOGLE_APPLICATION_CREDENTIALS"
    
    @staticmethod
    def get_env_var(key, default=None):
        """Get environment variable value."""
        import os
        return os.getenv(key, default)
    
    @staticmethod
    def set_env_var(key, value):
        """Set environment variable value.""" 
        import os
        os.environ[key] = str(value)

def get_env_vars_dict():
    """
    Get dictionary of all MLflow-related environment variables.
    
    Returns:
    dict - Environment variables and their values
    """

def set_env_vars_from_dict(env_dict):
    """
    Set environment variables from dictionary.
    
    Parameters:
    - env_dict: dict - Environment variables to set
    """

@contextmanager
def temporary_tracking_uri(uri):
    """
    Temporarily set tracking URI for block execution.
    
    Parameters:
    - uri: str - Temporary tracking URI
    
    Usage:
    with temporary_tracking_uri("sqlite:///temp.db"):
        # Operations use temporary URI
        mlflow.create_experiment("temp_exp")
    # Original URI restored
    """

@contextmanager  
def temporary_experiment_id(experiment_id):
    """
    Temporarily set experiment ID for block execution.
    
    Parameters:
    - experiment_id: str - Temporary experiment ID
    """

@contextmanager
def isolated_mlflow_config():
    """
    Create isolated MLflow configuration context.
    All configuration changes are isolated and restored on exit.
    """

import mlflow
import os

# Set tracking URI
mlflow.set_tracking_uri("http://localhost:5000")

# Verify connection
print(f"Tracking URI: {mlflow.get_tracking_uri()}")

# Set registry URI (if different from tracking)
mlflow.set_registry_uri("sqlite:///model_registry.db")

# Set default artifact location
mlflow.set_default_artifact_root("s3://my-mlflow-bucket/artifacts")

# Enable system metrics logging
mlflow.enable_system_metrics_logging()

# Check current configuration
print(f"Registry URI: {mlflow.get_registry_uri()}")
print(f"Artifact root: {mlflow.get_default_artifact_root()}")
print(f"System metrics enabled: {mlflow.is_system_metrics_logging_enabled()}")

import os
import mlflow

# Set configuration via environment variables
os.environ["MLFLOW_TRACKING_URI"] = "https://mlflow.company.com"
os.environ["MLFLOW_TRACKING_USERNAME"] = "user@company.com"  
os.environ["MLFLOW_TRACKING_PASSWORD"] = "secure_password"
os.environ["MLFLOW_DEFAULT_ARTIFACT_ROOT"] = "s3://company-mlflow/artifacts"
os.environ["MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING"] = "true"

# MLflow automatically uses environment variables
print(f"Tracking URI: {mlflow.get_tracking_uri()}")

# Alternative: Use MLflowEnvironment helper
from mlflow.config import MLflowEnvironment

MLflowEnvironment.set_env_var("MLFLOW_EXPERIMENT_ID", "123")
experiment_id = MLflowEnvironment.get_env_var("MLFLOW_EXPERIMENT_ID")
print(f"Experiment ID: {experiment_id}")

# Get all MLflow environment variables
env_vars = mlflow.get_env_vars_dict()
for key, value in env_vars.items():
    if value:  # Only show set variables
        print(f"{key}: {value}")

import mlflow

# PostgreSQL backend
postgres_uri = "postgresql://mlflow:password@localhost:5432/mlflow_db"
mlflow.set_tracking_uri(postgres_uri)

# MySQL backend  
mysql_uri = "mysql://mlflow:password@localhost:3306/mlflow_db"
mlflow.set_tracking_uri(mysql_uri)

# SQLite backend (for development)
sqlite_uri = "sqlite:///mlflow.db"
mlflow.set_tracking_uri(sqlite_uri)

# Validate configuration
try:
    mlflow.validate_tracking_uri(mlflow.get_tracking_uri())
    print("Tracking URI is valid")
    
    # Check server connection
    server_status = mlflow.check_server_connection()
    print(f"Server version: {server_status.get('version')}")
    print(f"Server status: {server_status.get('status')}")
    
except Exception as e:
    print(f"Configuration error: {e}")

import mlflow
import os

# AWS S3 Configuration
os.environ["AWS_ACCESS_KEY_ID"] = "your_access_key"
os.environ["AWS_SECRET_ACCESS_KEY"] = "your_secret_key"
os.environ["MLFLOW_S3_ENDPOINT_URL"] = "https://s3.amazonaws.com"

mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_default_artifact_root("s3://my-mlflow-bucket/artifacts")

# Azure Blob Storage Configuration
os.environ["AZURE_STORAGE_CONNECTION_STRING"] = "DefaultEndpointsProtocol=https;..."
mlflow.set_default_artifact_root("wasbs://container@account.blob.core.windows.net/artifacts")

# Google Cloud Storage Configuration  
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/service-account.json"
mlflow.set_default_artifact_root("gs://my-mlflow-bucket/artifacts")

# Validate artifact storage
try:
    mlflow.validate_artifact_root(mlflow.get_default_artifact_root())
    print("Artifact root is accessible")
except Exception as e:
    print(f"Artifact storage error: {e}")

import mlflow

# Configure system metrics collection
mlflow.set_system_metrics_node_id("worker-node-1")
mlflow.set_system_metrics_sampling_interval(1.0)  # 1 second intervals
mlflow.set_system_metrics_samples_before_logging(60)  # Log every 60 samples

# Enable system metrics
mlflow.enable_system_metrics_logging()

# Start experiment with system metrics
mlflow.set_experiment("system-metrics-experiment")

with mlflow.start_run():
    # Run your ML code - system metrics collected automatically
    import time
    import numpy as np
    
    # Simulate ML training workload
    for epoch in range(10):
        # Simulate CPU/memory intensive work
        data = np.random.randn(10000, 100)
        result = np.dot(data, data.T)
        
        mlflow.log_metric("epoch", epoch)
        time.sleep(2)  # System metrics collected during sleep
    
    print("System metrics logged automatically")

# Check system metrics in MLflow UI

import mlflow
from mlflow.config import temporary_tracking_uri, temporary_experiment_id

# Set default configuration
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("default-experiment")

# Use temporary configuration
with temporary_tracking_uri("sqlite:///temp_experiments.db"):
    # This block uses temporary SQLite backend
    temp_exp_id = mlflow.create_experiment("temporary-experiment")
    
    with mlflow.start_run():
        mlflow.log_metric("temp_metric", 42)
        print("Logged to temporary backend")

# Back to original configuration
print(f"Original tracking URI: {mlflow.get_tracking_uri()}")

# Temporary experiment ID
with temporary_experiment_id("123"):
    with mlflow.start_run():
        mlflow.log_metric("experiment_123_metric", 100)
        print("Logged to experiment 123")

# Original experiment restored
print(f"Current experiment: {mlflow.get_experiment_id()}")

import mlflow
from mlflow.config import MLflowEnvironment

def diagnose_mlflow_config():
    """Comprehensive MLflow configuration diagnostic."""
    
    print("=== MLflow Configuration Diagnostic ===")
    
    # Check basic configuration
    print(f"Tracking URI: {mlflow.get_tracking_uri()}")
    print(f"Registry URI: {mlflow.get_registry_uri()}")
    print(f"Artifact Root: {mlflow.get_default_artifact_root()}")
    
    # Check environment variables
    print("\n=== Environment Variables ===")
    env_vars = mlflow.get_env_vars_dict()
    for key, value in sorted(env_vars.items()):
        if value:
            # Mask sensitive values
            if "password" in key.lower() or "token" in key.lower() or "key" in key.lower():
                value = "***masked***"
            print(f"{key}: {value}")
    
    # Validate configuration
    print("\n=== Validation ===")
    try:
        mlflow.validate_tracking_uri(mlflow.get_tracking_uri())
        print("✓ Tracking URI is valid")
    except Exception as e:
        print(f"✗ Tracking URI error: {e}")
    
    try:
        mlflow.validate_artifact_root(mlflow.get_default_artifact_root())
        print("✓ Artifact root is accessible")
    except Exception as e:
        print(f"✗ Artifact root error: {e}")
    
    # Test server connection
    try:
        server_info = mlflow.check_server_connection()
        print(f"✓ Server connection successful")
        print(f"  Version: {server_info.get('version', 'unknown')}")
    except Exception as e:
        print(f"✗ Server connection failed: {e}")
    
    # System metrics status
    print(f"\nSystem metrics enabled: {mlflow.is_system_metrics_logging_enabled()}")

# Run diagnostic
diagnose_mlflow_config()

import mlflow
from mlflow.config import MLflowEnvironment, isolated_mlflow_config
import json

class MLflowConfigManager:
    """Advanced MLflow configuration management."""
    
    def __init__(self):
        self.configs = {}
    
    def save_config(self, name, config_dict):
        """Save configuration profile."""
        self.configs[name] = config_dict
    
    def load_config(self, name):
        """Load and apply configuration profile."""
        if name not in self.configs:
            raise ValueError(f"Configuration '{name}' not found")
        
        config = self.configs[name]
        
        if "tracking_uri" in config:
            mlflow.set_tracking_uri(config["tracking_uri"])
        if "registry_uri" in config:
            mlflow.set_registry_uri(config["registry_uri"])
        if "artifact_root" in config:
            mlflow.set_default_artifact_root(config["artifact_root"])
        if "system_metrics" in config:
            if config["system_metrics"]:
                mlflow.enable_system_metrics_logging()
            else:
                mlflow.disable_system_metrics_logging()
    
    def export_config(self, name, filepath):
        """Export configuration to file."""
        if name in self.configs:
            with open(filepath, 'w') as f:
                json.dump(self.configs[name], f, indent=2)
    
    def import_config(self, name, filepath):
        """Import configuration from file."""
        with open(filepath, 'r') as f:
            self.configs[name] = json.load(f)

# Usage example
config_manager = MLflowConfigManager()

# Define configurations for different environments
config_manager.save_config("development", {
    "tracking_uri": "sqlite:///dev_mlflow.db",
    "artifact_root": "./dev_artifacts",
    "system_metrics": True
})

config_manager.save_config("staging", {
    "tracking_uri": "http://staging-mlflow:5000",
    "registry_uri": "postgresql://user:pass@staging-db:5432/mlflow",
    "artifact_root": "s3://staging-mlflow-bucket/artifacts",
    "system_metrics": False
})

config_manager.save_config("production", {
    "tracking_uri": "https://prod-mlflow.company.com",
    "registry_uri": "postgresql://user:pass@prod-db:5432/mlflow",
    "artifact_root": "s3://prod-mlflow-bucket/artifacts",
    "system_metrics": True
})

# Switch between configurations
config_manager.load_config("development")
print(f"Development config loaded: {mlflow.get_tracking_uri()}")

config_manager.load_config("production")
print(f"Production config loaded: {mlflow.get_tracking_uri()}")

# Export configuration for sharing
config_manager.export_config("production", "prod_config.json")

from typing import Dict, Any, Optional, ContextManager
import os

class MLflowEnvironment:
    # Environment variable constants
    MLFLOW_TRACKING_URI: str
    MLFLOW_REGISTRY_URI: str
    MLFLOW_TRACKING_USERNAME: str
    MLFLOW_TRACKING_PASSWORD: str
    MLFLOW_TRACKING_TOKEN: str
    MLFLOW_DEFAULT_ARTIFACT_ROOT: str
    MLFLOW_EXPERIMENT_ID: str
    MLFLOW_RUN_ID: str
    MLFLOW_ENABLE_SYSTEM_METRICS_LOGGING: str
    
    @staticmethod
    def get_env_var(key: str, default: Optional[str] = None) -> Optional[str]: ...
    
    @staticmethod  
    def set_env_var(key: str, value: str) -> None: ...

# Configuration functions return types
def get_tracking_uri() -> str: ...
def get_registry_uri() -> str: ...
def get_artifact_uri(run_id: Optional[str] = None) -> str: ...
def get_default_artifact_root() -> str: ...
def get_experiment_id() -> Optional[str]: ...
def get_run_id() -> Optional[str]: ...

def is_tracking_uri_set() -> bool: ...
def is_system_metrics_logging_enabled() -> bool: ...

def validate_tracking_uri(uri: str) -> bool: ...
def validate_artifact_root(artifact_root: str) -> bool: ...

def check_server_connection(tracking_uri: Optional[str] = None) -> Dict[str, Any]: ...
def get_env_vars_dict() -> Dict[str, str]: ...

# Context managers
def temporary_tracking_uri(uri: str) -> ContextManager[None]: ...
def temporary_experiment_id(experiment_id: str) -> ContextManager[None]: ...
def isolated_mlflow_config() -> ContextManager[None]: ...

# Configuration profile type
ConfigProfile = Dict[str, Any]

# Database profile type  
DatabaseProfile = Dict[str, str]  # host, port, database, username, etc.