python, subprocess: launch new process when one (in a group) has

ghz 12hours ago ⋅ 1 views

python, subprocess: launch new process when one (in a group) has terminated

I have n files to analyze separately and independently of each other with the same Python script analysis.py. In a wrapper script, wrapper.py, I loop over those files and call analysis.py as a separate process with subprocess.Popen:

for a_file in all_files:
    command = "python analysis.py %s" % a_file
    analysis_process = subprocess.Popen(
                                            shlex.split(command),
                                            stdout=subprocess.PIPE,
                                            stderr=subprocess.PIPE)
    analysis_process.wait()

Now, I would like to use all the k CPU cores of my machine in order to speed up the whole analysis. Is there a way to always have k-1 running processes as long as there are files to analyze?

Answer

Yes, you can use Python's concurrent.futures module or manage the processes manually to achieve parallelism and ensure that at any point there are at most k-1 processes running simultaneously.

Here’s how you can do it:

1. Use concurrent.futures with ProcessPoolExecutor:

The concurrent.futures module simplifies managing parallel processes and provides easy-to-use methods for managing a pool of worker processes. The ProcessPoolExecutor will handle creating processes, and it will ensure that no more than k-1 processes run concurrently.

Here's how you can modify your wrapper.py to use ProcessPoolExecutor:

import subprocess
import shlex
from concurrent.futures import ProcessPoolExecutor, as_completed

# Define a function that runs the analysis
def run_analysis(a_file):
    command = "python analysis.py %s" % a_file
    analysis_process = subprocess.Popen(
        shlex.split(command),
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE
    )
    stdout, stderr = analysis_process.communicate()  # Wait for the process to complete and capture output
    return stdout, stderr

# List of all files to process
all_files = [...]  # Your list of files here

# Set the number of workers to be k-1 (for example, 4 CPU cores means 3 workers)
k = 4  # Set to the number of cores you want to use
max_workers = k - 1

# Use ProcessPoolExecutor to parallelize the process
with ProcessPoolExecutor(max_workers=max_workers) as executor:
    futures = [executor.submit(run_analysis, a_file) for a_file in all_files]
    
    for future in as_completed(futures):
        stdout, stderr = future.result()
        # Process the output from each analysis
        print(stdout.decode())  # Print or handle stdout as needed
        if stderr:
            print(stderr.decode())  # Handle errors if any

Explanation:

  1. ProcessPoolExecutor:

    • This is the key part that allows you to easily manage parallel processes. By setting max_workers=k-1, we ensure that only k-1 processes run concurrently.

  2. executor.submit(run_analysis, a_file):

    • This submits each file to be processed by a separate worker process. The run_analysis function handles running the subprocess for each file.

  3. as_completed(futures):

    • This returns futures as they complete. It ensures that you can collect the results or handle errors as each process finishes.

  4. Subprocess Management:

    • The subprocess.Popen runs the analysis.py script with the given file and captures its output (stdout and stderr).
    • analysis_process.communicate() ensures that the main process waits for the child process to finish and retrieves its output.

2. Manual Process Management with a Queue:

If you prefer more fine-grained control over process management, you can use a Queue to manage processes manually and ensure that no more than k-1 processes are running concurrently. Here's an example:

import subprocess
import shlex
import os
from queue import Queue
from multiprocessing import Process, cpu_count

# Function to run a single analysis
def run_analysis(a_file, queue):
    command = f"python analysis.py {a_file}"
    analysis_process = subprocess.Popen(
        shlex.split(command),
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE
    )
    stdout, stderr = analysis_process.communicate()
    queue.put((stdout, stderr))  # Place results in the queue

def main():
    all_files = [...]  # List of all files to process
    
    # Set number of workers to be k-1 (for example, 4 cores -> 3 workers)
    k = 4  # Number of cores you want to use
    num_workers = k - 1

    queue = Queue()
    processes = []

    # Create worker processes
    for a_file in all_files:
        if len(processes) >= num_workers:
            # Wait for the first process to finish before continuing
            for p in processes:
                p.join()
            processes = []  # Reset the process list

        # Create a new process for each file
        process = Process(target=run_analysis, args=(a_file, queue))
        processes.append(process)
        process.start()

    # Ensure all remaining processes are joined after the loop
    for p in processes:
        p.join()

    # Collect results from the queue
    while not queue.empty():
        stdout, stderr = queue.get()
        print(stdout.decode())  # Process stdout
        if stderr:
            print(stderr.decode())  # Process stderr

if __name__ == '__main__':
    main()

Key Points:

  • Queue: The Queue is used to store the results of each process. Each worker process adds its output (stdout, stderr) to the queue after completion.

  • Process Management: We ensure that no more than k-1 processes are running at once by checking the length of the processes list and waiting (join()) for processes to finish when needed.

  • Process Control: After starting a new process, we check if the number of running processes exceeds k-1 and wait for some to finish before starting more.

Conclusion:

Using ProcessPoolExecutor is the simplest and most efficient approach if you're looking for parallel processing in Python. It manages processes and their results elegantly. However, if you need more control, you can manually manage processes with multiprocessing.Process and a Queue.

Both methods will allow you to run k-1 processes concurrently, ensuring efficient use of CPU cores.

#py