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ClusterOps API Reference

ClusterOps is a Python library for managing and executing tasks across CPU and GPU resources in a distributed computing environment. It provides functions for resource discovery, task execution, and performance monitoring.

Installation

$ pip3 install clusterops

Table of Contents

  1. CPU Operations
  2. GPU Operations
  3. Utility Functions
  4. Resource Monitoring

CPU Operations

list_available_cpus()

Lists all available CPU cores.

Returns

Type Description
List[int] A list of available CPU core indices.

Raises

Exception Description
RuntimeError If no CPUs are found.

Example

from clusterops import list_available_cpus

available_cpus = list_available_cpus()
print(f"Available CPU cores: {available_cpus}")

execute_on_cpu(cpu_id: int, func: Callable, *args: Any, **kwargs: Any) -> Any

Executes a callable on a specific CPU.

Parameters

Name Type Description
cpu_id int The CPU core to run the function on.
func Callable The function to be executed.
*args Any Arguments for the callable.
**kwargs Any Keyword arguments for the callable.

Returns

Type Description
Any The result of the function execution.

Raises

Exception Description
ValueError If the CPU core specified is invalid.
RuntimeError If there is an error executing the function on the CPU.

Example

from clusterops import execute_on_cpu

def sample_task(n: int) -> int:
    return n * n

result = execute_on_cpu(0, sample_task, 10)
print(f"Result of sample task on CPU 0: {result}")

execute_with_cpu_cores(core_count: int, func: Callable, *args: Any, **kwargs: Any) -> Any

Executes a callable using a specified number of CPU cores.

Parameters

Name Type Description
core_count int The number of CPU cores to run the function on.
func Callable The function to be executed.
*args Any Arguments for the callable.
**kwargs Any Keyword arguments for the callable.

Returns

Type Description
Any The result of the function execution.

Raises

Exception Description
ValueError If the number of CPU cores specified is invalid or exceeds available cores.
RuntimeError If there is an error executing the function on the specified CPU cores.

Example

from clusterops import execute_with_cpu_cores

def parallel_task(n: int) -> int:
    return sum(range(n))

result = execute_with_cpu_cores(4, parallel_task, 1000000)
print(f"Result of parallel task using 4 CPU cores: {result}")

GPU Operations

list_available_gpus() -> List[str]

Lists all available GPUs.

Returns

Type Description
List[str] A list of available GPU names.

Raises

Exception Description
RuntimeError If no GPUs are found.

Example

from clusterops import list_available_gpus

available_gpus = list_available_gpus()
print(f"Available GPUs: {available_gpus}")

select_best_gpu() -> Optional[int]

Selects the GPU with the most free memory.

Returns

Type Description
Optional[int] The GPU ID of the best available GPU, or None if no GPUs are available.

Example

from clusterops import select_best_gpu

best_gpu = select_best_gpu()
if best_gpu is not None:
    print(f"Best GPU for execution: GPU {best_gpu}")
else:
    print("No GPUs available")

execute_on_gpu(gpu_id: int, func: Callable, *args: Any, **kwargs: Any) -> Any

Executes a callable on a specific GPU using Ray.

Parameters

Name Type Description
gpu_id int The GPU to run the function on.
func Callable The function to be executed.
*args Any Arguments for the callable.
**kwargs Any Keyword arguments for the callable.

Returns

Type Description
Any The result of the function execution.

Raises

Exception Description
ValueError If the GPU index is invalid.
RuntimeError If there is an error executing the function on the GPU.

Example

from clusterops import execute_on_gpu

def gpu_task(n: int) -> int:
    return n ** 2

result = execute_on_gpu(0, gpu_task, 10)
print(f"Result of GPU task on GPU 0: {result}")

execute_on_multiple_gpus(gpu_ids: List[int], func: Callable, all_gpus: bool = False, timeout: float = None, *args: Any, **kwargs: Any) -> List[Any]

Executes a callable across multiple GPUs using Ray.

Parameters

Name Type Description
gpu_ids List[int] The list of GPU IDs to run the function on.
func Callable The function to be executed.
all_gpus bool Whether to use all available GPUs (default: False).
timeout float Timeout for the execution in seconds (default: None).
*args Any Arguments for the callable.
**kwargs Any Keyword arguments for the callable.

Returns

Type Description
List[Any] A list of results from the execution on each GPU.

Raises

Exception Description
ValueError If any GPU index is invalid.
RuntimeError If there is an error executing the function on the GPUs.

Example

from clusterops import execute_on_multiple_gpus

def multi_gpu_task(n: int) -> int:
    return n ** 3

results = execute_on_multiple_gpus([0, 1], multi_gpu_task, 5)
print(f"Results of multi-GPU task: {results}")

distributed_execute_on_gpus(gpu_ids: List[int], func: Callable, *args: Any, **kwargs: Any) -> List[Any]

Executes a callable across multiple GPUs and nodes using Ray's distributed task scheduling.

Parameters

Name Type Description
gpu_ids List[int] The list of GPU IDs across nodes to run the function on.
func Callable The function to be executed.
*args Any Arguments for the callable.
**kwargs Any Keyword arguments for the callable.

Returns

Type Description
List[Any] A list of results from the execution on each GPU.

Example

from clusterops import distributed_execute_on_gpus

def distributed_task(n: int) -> int:
    return n ** 4

results = distributed_execute_on_gpus([0, 1, 2, 3], distributed_task, 3)
print(f"Results of distributed GPU task: {results}")

Utility Functions

retry_with_backoff(func: Callable, retries: int = RETRY_COUNT, delay: float = RETRY_DELAY, *args: Any, **kwargs: Any) -> Any

Retries a callable function with exponential backoff in case of failure.

Parameters

Name Type Description
func Callable The function to execute with retries.
retries int Number of retries (default: RETRY_COUNT from env).
delay float Delay between retries in seconds (default: RETRY_DELAY from env).
*args Any Arguments for the callable.
**kwargs Any Keyword arguments for the callable.

Returns

Type Description
Any The result of the function execution.

Raises

Exception Description
Exception After all retries fail.

Example

from clusterops import retry_with_backoff

def unstable_task():
    # Simulating an unstable task that might fail
    import random
    if random.random() < 0.5:
        raise Exception("Task failed")
    return "Task succeeded"

result = retry_with_backoff(unstable_task, retries=5, delay=1)
print(f"Result of unstable task: {result}")

Resource Monitoring

monitor_resources()

Continuously monitors CPU and GPU resources and logs alerts when thresholds are crossed.

Example

from clusterops import monitor_resources

# Start monitoring resources
monitor_resources()

profile_execution(func: Callable, *args: Any, **kwargs: Any) -> Any

Profiles the execution of a task, collecting metrics like execution time and CPU/GPU usage.

Parameters

Name Type Description
func Callable The function to profile.
*args Any Arguments for the callable.
**kwargs Any Keyword arguments for the callable.

Returns

Type Description
Any The result of the function execution along with the collected metrics.

Example

from clusterops import profile_execution

def cpu_intensive_task():
    return sum(i*i for i in range(10000000))

result = profile_execution(cpu_intensive_task)
print(f"Result of profiled task: {result}")

This API reference provides a comprehensive overview of the ClusterOps library's main functions, their parameters, return values, and usage examples. It should help users understand and utilize the library effectively for managing and executing tasks across CPU and GPU resources in a distributed computing environment.