refactor: replace multiprocessing.Pool with ThreadPoolExecutor in process_single_position

src/iohub/ngff/utils.py

@@ -2,9 +2,10 @@
 
 import inspect
 import itertools
-import multiprocessing as mp
+import os
 from collections import defaultdict
 from collections.abc import Callable, Sequence
+from concurrent.futures import ThreadPoolExecutor
 from functools import partial
 from pathlib import Path
 from typing import Any, Literal
@@ -395,21 +396,18 @@ def process_single_position(
         output_position_path,
         **kwargs,
     )
-    num_processes = min(num_processes, len(flat_iterable), mp.cpu_count())
-    click.echo(f"\nStarting multiprocess pool with {num_processes} processes")
-    if num_processes <= 1:
-        # Run serially — Pool(1) with spawn unnecessarily forks a subprocess
+    num_workers = min(num_processes, len(flat_iterable), os.cpu_count())
+    click.echo(f"\nStarting thread pool with {num_workers} workers")
+    if num_workers <= 1:
         for args in flat_iterable:
             partial_apply_transform_to_czyx_and_save(*args)
     else:
-        # NOTE: use spawn to work around tensorstore#61
-        context = mp.get_context("spawn")
-        with context.Pool(num_processes) as p:
-            p.starmap(
-                partial_apply_transform_to_czyx_and_save,
+        with ThreadPoolExecutor(max_workers=num_workers) as executor:
+            list(executor.map(
+                lambda args: partial_apply_transform_to_czyx_and_save(*args),
                 flat_iterable,
-            )
-    click.echo("Shut down multiprocess pool")
+            ))
+    click.echo("Shut down thread pool")
 
 
 # -- Pure utility functions ------------------------------------------------