Core Distributed Computing

def init(address=None, num_cpus=None, num_gpus=None, memory=None, 
         object_store_memory=None, resources=None, runtime_env=None,
         logging_level="INFO", log_to_driver=True, configure_logging=True,
         **kwargs):
    """
    Initialize Ray runtime.
    
    Args:
        address (str, optional): Ray cluster address to connect to
        num_cpus (int, optional): Number of CPUs to use
        num_gpus (int, optional): Number of GPUs to use  
        memory (int, optional): Amount of memory to use in bytes
        object_store_memory (int, optional): Object store memory in bytes
        resources (dict, optional): Custom resources dictionary
        runtime_env (dict, optional): Runtime environment configuration
        logging_level (str): Logging level ("DEBUG", "INFO", "WARNING", "ERROR")
        log_to_driver (bool): Whether to log to driver
        configure_logging (bool): Whether to configure logging
    
    Returns:
        ray.ClientContext: Ray context information
    """

def is_initialized():
    """Check if Ray is initialized."""

def shutdown():
    """Shutdown Ray runtime and cleanup resources."""

def get_runtime_context():
    """Get current Ray runtime context information."""

def remote(num_cpus=None, num_gpus=None, memory=None, resources=None,
           max_calls=None, max_retries=3, retry_exceptions=False,
           runtime_env=None, **kwargs):
    """
    Decorator to make functions executable as Ray tasks.
    
    Args:
        num_cpus (float, optional): Number of CPUs required
        num_gpus (float, optional): Number of GPUs required
        memory (int, optional): Memory required in MB
        resources (dict, optional): Custom resources required
        max_calls (int, optional): Maximum calls before actor restart
        max_retries (int): Maximum number of retries on failure
        retry_exceptions (bool/list): Exceptions to retry on
        runtime_env (dict, optional): Runtime environment
    
    Returns:
        RemoteFunction: Ray remote function
    """

def put(value, _owner=None):
    """
    Store object in Ray object store.
    
    Args:
        value: Object to store
        _owner (ActorHandle, optional): Actor that owns this object
    
    Returns:
        ObjectRef: Reference to stored object
    """

def get(object_refs, timeout=None):
    """
    Retrieve objects from object store.
    
    Args:
        object_refs (ObjectRef/list): Object references to retrieve
        timeout (float, optional): Timeout in seconds
    
    Returns:
        Object or list of objects
    """

def wait(object_refs, num_returns=1, timeout=None, fetch_local=True):
    """
    Wait for subset of object references to become ready.
    
    Args:
        object_refs (list): List of object references
        num_returns (int): Number of objects to wait for
        timeout (float, optional): Timeout in seconds
        fetch_local (bool): Whether to fetch objects locally
    
    Returns:
        tuple: (ready_refs, remaining_refs)
    """

def get_actor(name, namespace=None):
    """
    Get handle to named actor.
    
    Args:
        name (str): Actor name
        namespace (str, optional): Actor namespace
    
    Returns:
        ActorHandle: Handle to the actor
    """

def kill(actor, no_restart=True):
    """
    Kill an actor, task, or placement group.
    
    Args:
        actor: Actor handle, task, or placement group to kill
        no_restart (bool): Whether to prevent actor restart
    """

def cancel(object_ref, force=False, recursive=True):
    """
    Cancel task execution.
    
    Args:
        object_ref (ObjectRef): Task to cancel
        force (bool): Force cancellation
        recursive (bool): Cancel dependent tasks
    
    Returns:
        bool: True if task was cancelled
    """

def cluster_resources():
    """
    Get total cluster resources.
    
    Returns:
        dict: Resource counts by type
    """

def available_resources():
    """
    Get available cluster resources.
    
    Returns:
        dict: Available resource counts by type
    """

def nodes():
    """
    Get information about cluster nodes.
    
    Returns:
        list: List of node information dictionaries
    """

def timeline():
    """
    Get Ray timeline for profiling and debugging.
    
    Returns:
        list: Timeline events
    """

def get_gpu_ids():
    """
    Get GPU IDs visible to current worker.
    
    Returns:
        list: List of GPU IDs
    """

def java_function(class_name: str, function_name: str):
    """
    Create Ray remote function from Java class.
    
    Args:
        class_name (str): Java class name
        function_name (str): Java function name
    
    Returns:
        JavaFunction: Ray Java function
    """

def cpp_function(function_name: str):
    """
    Create Ray remote function from C++ function.
    
    Args:
        function_name (str): C++ function name
    
    Returns:
        CppFunction: Ray C++ function
    """

def java_actor_class(class_name):
    """
    Create Ray actor class from Java class.
    
    Args:
        class_name (str): Java class name
    
    Returns:
        JavaActorClass: Ray Java actor class
    """

def show_in_dashboard(message: str, key: str = "", dtype: str = "text"):
    """
    Display message in Ray dashboard.
    
    Args:
        message (str): Message to be displayed
        key (str): Key name for the message
        dtype (str): Message type ("text" or "html")
    """

class LoggingConfig:
    """Ray logging configuration."""
    
    def __init__(self, encoding="TEXT", log_level="INFO", logs_dir=None,
                 enable_default_setup=True):
        """
        Initialize logging configuration.
        
        Args:
            encoding (str): Log encoding ("TEXT" or "JSON")
            log_level (str): Log level
            logs_dir (str, optional): Directory for log files
            enable_default_setup (bool): Enable default logging setup
        """

# Core Object Types
class ObjectRef:
    """Reference to object in Ray object store."""

class ObjectRefGenerator:
    """Generator for streaming object references."""

class DynamicObjectRefGenerator:
    """Dynamic generator for object references."""

# ID Types
class ActorID:
    """Unique identifier for Ray actor."""

class TaskID:
    """Unique identifier for Ray task."""

class JobID:
    """Unique identifier for Ray job."""

class NodeID:
    """Unique identifier for Ray node."""

class WorkerID:
    """Unique identifier for Ray worker."""

class FunctionID:
    """Unique identifier for Ray function."""

class UniqueID:
    """Base class for Ray unique identifiers."""

class PlacementGroupID:
    """Unique identifier for placement group."""

class ClusterID:
    """Unique identifier for Ray cluster."""

class ActorClassID:
    """Unique identifier for Ray actor class."""

# Language Support
class Language:
    """Supported programming languages in Ray."""
    PYTHON = "PYTHON"
    JAVA = "JAVA" 
    CPP = "CPP"

# Mode Constants
LOCAL_MODE = "LOCAL_MODE"
SCRIPT_MODE = "SCRIPT_MODE"
WORKER_MODE = "WORKER_MODE"

import ray

ray.init()

@ray.remote
def square(x):
    return x * x

# Execute task
future = square.remote(4)
result = ray.get(future)
print(result)  # 16

ray.shutdown()

import ray

ray.init()

@ray.remote
class Counter:
    def __init__(self):
        self.count = 0
    
    def increment(self):
        self.count += 1
        return self.count
    
    def get_count(self):
        return self.count

# Create actor
counter = Counter.remote()

# Call actor methods
ray.get(counter.increment.remote())
count = ray.get(counter.get_count.remote())
print(count)  # 1

ray.shutdown()

import ray
import time

ray.init()

@ray.remote
def slow_function(i):
    time.sleep(1)
    return i * i

# Execute in parallel
futures = [slow_function.remote(i) for i in range(4)]
results = ray.get(futures)
print(results)  # [0, 1, 4, 9]

ray.shutdown()