I’m working on a Python project where I need to run a large grid search optimization using multiprocessing. My challenge is to dispatch a large number of jobs without running into memory issues.

Here’s my current approach:

1. Memory Check Function: Checks if the current memory usage is within a specified limit.
2. Optimization Task Function: Runs the optimization using SciPy’s minimize on given parameters.
3. Main Function:

• Creates a pool of worker processes.
• Dispatches subsets of tasks (G’) to the pool.
• Waits for the current tasks to complete.
• Checks memory usage before dispatching the next subset.
• Updates the best result and parameters if the current batch yields a better result.
• Closes the pool after all tasks are processed.
  Here’s my code:

import multiprocessing as mp
import psutil
import numpy as np
from scipy.optimize import minimize
from typing import List, Dict, Any
import time

# Function to check current memory usage
def memory_usage_within_limit(limit: float) -> bool:
    """Check if the current memory usage is within the specified limit."""
    memory_info = psutil.virtual_memory()
    return memory_info.available / memory_info.total >= limit

# Function to run the optimization task
def optimization_task(params: Dict[str, Any]) -> Dict[str, Any]:
    """Run optimization using SciPy minimize on the given parameters."""
    def objective_function(x):
        return np.sum(x**2)  # Example: simple quadratic function
    result = minimize(objective_function, np.array(list(params.values())))
    return {'params': params, 'result': result}

# Function to run the optimization in parallel while managing memory usage
def run_optimization(grid: List[Dict[str, Any]], memory_limit: float, num_workers: int):
    """Run the optimization tasks in parallel, managing memory usage."""
    best_result = None
    best_params = None
    pool = mp.Pool(num_workers)  # Create a pool of workers

    try:
        for subset in grid:
            # Wait until memory usage is within limit
            while not memory_usage_within_limit(memory_limit):
                time.sleep(1)  # Wait until memory usage is within limit

            # Dispatch the subset of tasks to the worker pool using .map
            async_results = pool.map(optimization_task, subset)

            # Update the best result and params if the current results are better
            for result in async_results:
                if best_result is None or result['result'].fun < best_result['result'].fun:
                    best_result = result
                    best_params = result['params']
    finally:
        pool.close()  # Close the pool
        pool.join()  # Wait for all worker processes to finish
        pool.terminate()  # Ensure all processes are terminated
    return best_result, best_params

# Define hyperparameter grid
hyperparameter_grid = [
    {'x1': i, 'x2': j} for i in range(-2, 3) for j in range(-2, 3)  # Reduced grid for small example
]

# Split grid into subsets
subset_size = 3
grid_subsets = [hyperparameter_grid[i:i + subset_size] for i in range(0, len(hyperparameter_grid), subset_size)]

# Run the optimization with memory management
memory_limit = 0.1  # Allow usage up to 90% of memory
num_workers = 1  # Example with 1 worker

start_time = time.time()
best_result, best_params = run_optimization(grid_subsets, memory_limit, num_workers)
end_time = time.time()

duration = end_time - start_time
print("Best Result:", best_result)
print("Best Params:", best_params)
print("Duration:", duration)

I appreciate any help or suggestions on how to improve the robustness of my multiprocessing job dispatching to avoid memory errors — especially because this can’t be a new problem. I bet it’s been solved before. Thank you!

While this approach might works, I wish I could continually dispatch multiprocessing jobs without having memory issues. Ideally, I would like to continually dispatch jobs, and after they run for a bit and the memory increase stops, dispatch more jobs while always keeping a safe margin from the total memory limit. Is this possible? If so, how can I implement it?

First attempt, but I feel these are amateur attempts to my issues:

import multiprocessing
import psutil
import time

def example_task(arg):
    time.sleep(5)  # Simulate a task that takes time
    return f"Processed {arg}"

def memory_safe_worker(func, args, memory_limit, sleep_interval=1):
    """
    Wrapper function to monitor memory usage and manage jobs accordingly.
    """
    pool = multiprocessing.Pool()
    jobs = []
    
    def check_memory():
        mem = psutil.virtual_memory()
        return mem.available / mem.total
    
    try:
        while args:
            current_memory = check_memory()
            if current_memory > memory_limit:
                arg = args.pop(0)
                job = pool.apply_async(func, (arg,))
                jobs.append(job)
                print(f"Dispatched a job for argument {arg}. Available memory: {current_memory * 100:.2f}%")
            else:
                print(f"Waiting for memory to free up. Available memory: {current_memory * 100:.2f}%")
            time.sleep(sleep_interval)
            # Clean up completed jobs
            jobs = [job for job in jobs if not job.ready()]
    except KeyboardInterrupt:
        print("Terminating all jobs...")
        pool.terminate()
        pool.join()
    finally:
        pool.close()
        pool.join()

if __name__ == "__main__":
    memory_limit = 0.2  # Adjust this to set how much memory should be available before dispatching new jobs (e.g., 0.2 means 20% of total memory should be free)
    args = [i for i in range(10)]  # Example arguments for tasks

    memory_safe_worker(example_task, args, memory_limit)