Configuration

def set_config(
    verbosity=None,
    use_rmm=None,
    rmm_pool_size=None,
    parameter_validation=None,
    gpu_coordinate_descent=None
):
    """
    Set global configuration parameters.

    Parameters:
    - verbosity: Verbosity level (0=silent, 1=warning, 2=info, 3=debug)
    - use_rmm: Whether to use RAPIDS Memory Manager for GPU memory allocation
    - rmm_pool_size: RMM memory pool size in bytes
    - parameter_validation: Whether to validate parameters (True/False)
    - gpu_coordinate_descent: Whether to use GPU for coordinate descent

    Returns:
    None

    Note:
    Configuration changes affect all subsequent XGBoost operations.
    """

def get_config():
    """
    Get current global configuration.

    Returns:
    dict: Dictionary containing current configuration parameters
    """

def config_context(**config):
    """
    Context manager for temporary configuration changes.

    Parameters:
    **config: Configuration parameters to set temporarily

    Returns:
    Context manager that restores original configuration on exit

    Example:
    with xgb.config_context(verbosity=0):
        # XGBoost operations run silently
        model = xgb.train(params, dtrain)
    # Original verbosity restored
    """

def build_info():
    """
    Get build information about XGBoost installation.

    Returns:
    dict: Dictionary containing build information including:
        - USE_OPENMP: OpenMP support status
        - USE_CUDA: CUDA support status  
        - USE_NCCL: NCCL support status
        - USE_RMM: RMM support status
        - BUILD_WITH_SHARED_LIB: Shared library build status
        - GCC_VERSION: GCC version used for compilation
        - CUDA_VERSION: CUDA version if available
        And other build-time configuration details
    """

import xgboost as xgb

# Set verbosity levels
xgb.set_config(verbosity=0)  # Silent
xgb.set_config(verbosity=1)  # Warnings only  
xgb.set_config(verbosity=2)  # Info messages
xgb.set_config(verbosity=3)  # Debug output

# Enable RMM for GPU memory management
xgb.set_config(use_rmm=True, rmm_pool_size=1024**3)  # 1GB pool

# Disable RMM
xgb.set_config(use_rmm=False)

# Enable parameter validation (default)
xgb.set_config(parameter_validation=True)

# Disable parameter validation for performance
xgb.set_config(parameter_validation=False)

import xgboost as xgb

# Check current configuration
config = xgb.get_config()
print("Current config:", config)

# Set global verbosity
xgb.set_config(verbosity=1)

# Train model with current settings
dtrain = xgb.DMatrix(X_train, label=y_train)
model = xgb.train(params, dtrain, num_boost_round=100)

import xgboost as xgb

# Normal verbosity
print("Current verbosity:", xgb.get_config()['verbosity'])

# Temporarily change configuration
with xgb.config_context(verbosity=0):
    # Silent training
    model = xgb.train(params, dtrain, num_boost_round=100)
    print("Training completed silently")

# Original verbosity restored
print("Verbosity restored:", xgb.get_config()['verbosity'])

import xgboost as xgb

# Check build info for GPU support
build_info = xgb.build_info()
print("CUDA support:", build_info.get('USE_CUDA', False))
print("RMM support:", build_info.get('USE_RMM', False))

if build_info.get('USE_CUDA'):
    # Configure GPU memory management
    xgb.set_config(
        use_rmm=True,
        rmm_pool_size=2 * 1024**3,  # 2GB memory pool
        gpu_coordinate_descent=True
    )
    
    # Train on GPU
    params = {
        'objective': 'reg:squarederror',
        'tree_method': 'gpu_hist',
        'device': 'cuda'
    }
    model = xgb.train(params, dtrain)

import xgboost as xgb

# Optimize for production (disable validation, reduce verbosity)
xgb.set_config(
    verbosity=0,
    parameter_validation=False
)

# Training will run faster but with less safety checks
model = xgb.train(params, dtrain, num_boost_round=100)

# Re-enable for development
xgb.set_config(
    verbosity=1,
    parameter_validation=True
)

import xgboost as xgb
import os

# Environment-specific configuration
if os.getenv('XGB_DEBUG'):
    xgb.set_config(verbosity=3)  # Debug mode
elif os.getenv('XGB_QUIET'):
    xgb.set_config(verbosity=0)  # Silent mode
else:
    xgb.set_config(verbosity=1)  # Default warnings

# Check environment capabilities
build_info = xgb.build_info()
if build_info.get('USE_OPENMP'):
    print("OpenMP parallelization available")
if build_info.get('USE_CUDA'):
    print("GPU acceleration available")

import xgboost as xgb

# Different configurations for different tasks
def train_model_quietly(params, dtrain):
    with xgb.config_context(verbosity=0):
        return xgb.train(params, dtrain, num_boost_round=100)

def debug_model_training(params, dtrain):
    with xgb.config_context(verbosity=3, parameter_validation=True):
        return xgb.train(params, dtrain, num_boost_round=10)

# Use appropriate configuration for each task
production_model = train_model_quietly(params, dtrain)
debug_model = debug_model_training(params, dtrain)

# Development configuration
xgb.set_config(
    verbosity=2,           # Show info messages
    parameter_validation=True  # Validate parameters
)

# Production configuration  
xgb.set_config(
    verbosity=1,           # Warnings only
    parameter_validation=False  # Skip validation for speed
)

# For GPU environments with limited memory
if xgb.build_info().get('USE_CUDA'):
    xgb.set_config(
        use_rmm=True,
        rmm_pool_size=512 * 1024**2  # 512MB pool
    )

# For distributed training
xgb.set_config(verbosity=1)  # Reduce log noise across workers