Decorator API¶

The @cluster decorator is the main interface for Clustrix, allowing you to easily execute functions on remote clusters or locally with parallelization.

clustrix.decorator.cluster(_func=None, *, cores=None, memory=None, time=None, partition=None, queue=None, parallel=None, environment=None, **kwargs)[source]¶

Decorator to execute functions on a cluster.

Parameters:

cores (Optional[int]) – Number of CPU cores to request
memory (Optional[str]) – Memory to request (e.g., “8GB”)
time (Optional[str]) – Time limit (e.g., “01:00:00”)
partition (Optional[str]) – Cluster partition to use
queue (Optional[str]) – Queue to submit to
parallel (Optional[bool]) – Whether to parallelize loops automatically
environment (Optional[str]) – Conda environment name
**kwargs – Additional job parameters

Returns:

Decorated function that executes on cluster

Examples¶

Basic Usage¶

from clustrix import cluster

@cluster(cores=4, memory='8GB')
def my_function(x, y):
    return x + y

result = my_function(10, 20)

Resource Specification¶

@cluster(
    cores=16,
    memory='32GB',
    time='04:00:00',
    partition='gpu'
)
def gpu_computation():
    # Your GPU code here
    pass

Parallel Loop Execution¶

@cluster(cores=8, parallel=True)
def parallel_processing(data):
    results = []
    for item in data:  # This loop will be parallelized
        results.append(expensive_operation(item))
    return results

Local Parallelization¶

# Configure for local execution
import clustrix
clustrix.configure(cluster_host=None)

@cluster(cores=8, parallel=True)
def local_parallel_function(data):
    # Executes locally using multiprocessing
    return [process_item(item) for item in data]