configurable degree of parallelism via SNKIT_PROCESSES

src/snkit/network.py

@@ -1,5 +1,6 @@
 """Network representation and utilities
 """
+import logging
 import os
 import warnings
 
@@ -28,6 +29,9 @@
 
 from collections import Counter
 
+# configure logging with a timestamp and process id prefix
+logging.basicConfig(format="%(asctime)s %(process)s %(message)s", level=logging.INFO)
+
 # optional progress bars
 if "SNKIT_PROGRESS" in os.environ and os.environ["SNKIT_PROGRESS"] in ("1", "TRUE"):
     try:
@@ -38,11 +42,20 @@
     from snkit.utils import tqdm_standin as tqdm
 
 # optional parallel processing
-if "SNKIT_PARALLEL" in os.environ and os.environ["SNKIT_PARALLEL"] in ("1", "TRUE"):
-    PARALLEL = True
+if "SNKIT_PROCESSES" in os.environ:
+    processes_env_var = os.environ["SNKIT_PROCESSES"]
+    try:
+        requested_processes = int(processes_env_var)
+    except TypeError:
+        raise RuntimeError(
+            "SNKIT_PROCESSES env var must be a non-negative integer. "
+            "Use 0 or unset for serial operation."
+        )
+    PARALLEL_PROCESS_COUNT = min([os.cpu_count(), requested_processes])
     import multiprocessing
+    logging.info(f"SNKIT_PROCESSES={processes_env_var}, using {PARALLEL_PROCESS_COUNT} processes")
 else:
-    PARALLEL = False
+    PARALLEL_PROCESS_COUNT = 0
 
 
 class Network:
@@ -317,17 +330,20 @@ def _split_edges_at_nodes(
 
 
 def split_edges_at_nodes(network, tolerance=1e-9):
-    """Split network edges where they intersect node geometries"""
+    """
+    Split network edges where they intersect node geometries. Will operate in
+    parallel if SNKIT_PROCESSES is in the environment and a positive integer.
+    """
     split_edges = []
 
     n = len(network.edges)
-    if PARALLEL and (n > 10_000):
-        chunk_size = int(n / os.cpu_count())
+    if PARALLEL_PROCESS_COUNT and (n > 1_000):
+        chunk_size = max([1, int(n / PARALLEL_PROCESS_COUNT)])
         args = [
             (network.edges.iloc[i : i + chunk_size, :], network.nodes, tolerance)
             for i in range(0, n, chunk_size)
         ]
-        with multiprocessing.Pool() as pool:
+        with multiprocessing.Pool(PARALLEL_PROCESS_COUNT) as pool:
             results = pool.starmap(_split_edges_at_nodes, args)
 
         # flatten return list